Summary: In this session, learn about ArmoniK, an open source job scheduler that helps you deploy elastic workloads on premises and on the cloud. To help integrate on premises and on AWS , ArmoniK considers the location of the data and manages data transfers between tasks. Kubernetes is the only requirement for ArmoniK, which makes it easy to deploy in a variety of environments. ArmoniK is currently used to run large graphs of tasks (up to 100 million tasks) with strict business service-level agreements (SLA).

Summary: Training AI (ML) models requires setting up clusters that enable many GPUs to talk to each other using low-latency networking that is capable of driving massive throughput. This session shares in real time how you can quickly and easily set up an ML training cluster using AWS ParallelCluster, NVIDIA GPUs, Elastic Fabric Adapter (EFA), and Amazon FSx for Lustre. Learn how to train a GPT model using Megatron-LM, store the results, and collapse the infrastructure when you’re finished.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: Nextira, a part of Accenture, specializes in helping companies make use of state-of-the-art AI tooling and infrastructure in the cloud. In this session, explore an example ML implementation for legal informatics, where there is a pressing need to be continuously updated on the latest changes to US laws and regulations. Get an introduction to a system that deploys a Retrieval Augmented Generation (RAG) model on Amazon Web Services (AWS), focusing on a regulatory and compliance use case.

Summary: Training AI (ML) models requires setting up clusters that enable many GPUs to talk to each other using low-latency networking that is capable of driving massive throughput. This session shares in real time how you can quickly and easily set up an ML training cluster using AWS ParallelCluster, NVIDIA GPUs, Elastic Fabric Adapter (EFA), and Amazon FSx for Lustre. Learn how to train a GPT model using Megatron-LM, store the results, and collapse the infrastructure when you’re finished.

Summary: Nextira, a part of Accenture, specializes in helping companies make use of state-of-the-art AI tooling and infrastructure in the cloud. In this session, explore an example ML implementation for legal informatics, where there is a pressing need to be continuously updated on the latest changes to US laws and regulations. Get an introduction to a system that deploys a Retrieval Augmented Generation (RAG) model on Amazon Web Services (AWS), focusing on a regulatory and compliance use case.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: Training AI (ML) models requires setting up clusters that enable many GPUs to talk to each other using low-latency networking that is capable of driving massive throughput. This session shares in real time how you can quickly and easily set up an ML training cluster using AWS ParallelCluster, NVIDIA GPUs, Elastic Fabric Adapter (EFA), and Amazon FSx for Lustre. Learn how to train a GPT model using Megatron-LM, store the results, and collapse the infrastructure when you’re finished.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: Promethium is a revolutionary chemistry simulation platform developed by QC Ware that will significantly accelerate the drug, material, and chemical discovery process. Built for virtually unlimited scalability on Amazon EC2 P3 and P4d instances, Promethium was designed to be GPU native and perform fully ab initio density functional theory (DFT) calculations with exceptional speed and accuracy. Coupled with its capability to treat system sizes not typically accessible by DFT, Promethium has the potential to transform the industry and bring immediate value to customers in areas like pharma and materials science. Whether you’re trying to discover the next blockbuster drug or developing more advanced materials, join this session to learn how Promethium can help you make breakthrough discoveries and take your research to the next level.

Summary: In this session, explore innovations in climate simulation, weather modeling, energy efficiency, and more powered by full-stack Accelerators from AWS and NVIDIA.

Summary: In this session, explore innovations in climate simulation, weather modeling, energy efficiency, and more powered by full-stack Accelerators from AWS and NVIDIA.

Summary: Promethium is a revolutionary chemistry simulation platform developed by QC Ware that will significantly accelerate the drug, material, and chemical discovery process. Built for virtually unlimited scalability on Amazon EC2 P3 and P4d instances, Promethium was designed to be GPU native and perform fully ab initio density functional theory (DFT) calculations with exceptional speed and accuracy. Coupled with its capability to treat system sizes not typically accessible by DFT, Promethium has the potential to transform the industry and bring immediate value to customers in areas like pharma and materials science. Whether you’re trying to discover the next blockbuster drug or developing more advanced materials, join this session to learn how Promethium can help you make breakthrough discoveries and take your research to the next level.

Summary: Quantum computing specialists from the Amazon Braket team Rm 406

Summary: Amazon Braket helps organizations get access to quantum computing hardware and simulators so they can speed up their scientific research and software development for quantum computing. This session shows short tutorials and shares how to run quantum circuits using real gate-based devices and simulators. Learn how to run your first quantum AI algorithm using Amazon Braket Hybrid Jobs. Along the way, explore Amazon Braket’s features, see examples from the AWS quantum algorithm library, and get your questions answered.

Summary: Amazon Braket helps organizations get access to quantum computing hardware and simulators so they can speed up their scientific research and software development for quantum computing. This session shows short tutorials and shares how to run quantum circuits using real gate-based devices and simulators. Learn how to run your first quantum AI algorithm using Amazon Braket Hybrid Jobs. Along the way, explore Amazon Braket’s features, see examples from the AWS quantum algorithm library, and get your questions answered.

Summary: Amazon Braket helps organizations get access to quantum computing hardware and simulators so they can speed up their scientific research and software development for quantum computing. This session shows short tutorials and shares how to run quantum circuits using real gate-based devices and simulators. Learn how to run your first quantum AI algorithm using Amazon Braket Hybrid Jobs. Along the way, explore Amazon Braket’s features, see examples from the AWS quantum algorithm library, and get your questions answered.

Summary: Arm, AWS, and NVIDIA want to welcome you to Denver with a happy hour downtown near the train station.

Summary: Developers from Arm, AWS, and NVIDIA Rm 407

Summary: Agenda: https://sc23.supercomputing.org/presentation/?id=bof120&sess=sess350 Rm 407

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: HPC systems deployed on AWS often rely on various resources like file systems, networking, and directory services. Despite AWS ParallelCluster’s automation capabilities, setting up and integrating these dependencies can become complex if you have a lot of custom requirements. In this session, walk through how the new HPC Recipes for AWS library simplifies this process. Then, learn how to create a multi-user environment, configure shared storage, set up a budget, and deploy a benchmarking cluster with just a few clicks in the AWS Management Console. Finally, discover how to combine, modify, and reuse these recipes to meet your particular needs—without needing to be an AWS expert.

Summary: The generative capability of AI holds significant promise across a diverse range of industries fueled by engineering design, such as automotive, motor sports, and aerospace. In this session, discover how to create a pipeline that uses generative AI designs to feed conventional physics-based simulations, and learn how to loop all of this to create a converging, rapid design process for exploring new design concepts starting from a single image. Learn how to use open source frameworks to create digital twins, deploy OpenFOAM in containers for the simulations, and use serverless tools coupled with AWS Batch to drive all this at scale.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: The generative capability of AI holds significant promise across a diverse range of industries fueled by engineering design, such as automotive, motor sports, and aerospace. In this session, discover how to create a pipeline that uses generative AI designs to feed conventional physics-based simulations, and learn how to loop all of this to create a converging, rapid design process for exploring new design concepts starting from a single image. Learn how to use open source frameworks to create digital twins, deploy OpenFOAM in containers for the simulations, and use serverless tools coupled with AWS Batch to drive all this at scale.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: The generative capability of AI holds significant promise across a diverse range of industries fueled by engineering design, such as automotive, motor sports, and aerospace. In this session, discover how to create a pipeline that uses generative AI designs to feed conventional physics-based simulations, and learn how to loop all of this to create a converging, rapid design process for exploring new design concepts starting from a single image. Learn how to use open source frameworks to create digital twins, deploy OpenFOAM in containers for the simulations, and use serverless tools coupled with AWS Batch to drive all this at scale.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: Migrating engineering workloads like CAE simulation to cloud-based HPC has been a challenge for many AWS users. In this demo, learn how Ansys Gateway powered by AWS offers an easy workflow to deploy virtual desktops and virtual clusters with your selected Ansys applications installed, configured, and tuned for optimal performance. This is deployed in your own AWS environment, giving you the improved data control and affordability. Come see how this service can help accelerate your engineering workloads with the power of Ansys and AWS HPC.

Summary: TotalCAE on AWS reduces CAE simulation time up to 80% in just a few clicks for hundreds of CAE applications. See how easy it can be to adopt the latest AWS HPC technologies without being an IT expert, backed by TotalCAE one-hour support times.

Summary: Migrating engineering workloads like CAE simulation to cloud-based HPC has been a challenge for many AWS users. In this demo, learn how Ansys Gateway powered by AWS offers an easy workflow to deploy virtual desktops and virtual clusters with your selected Ansys applications installed, configured, and tuned for optimal performance. This is deployed in your own AWS environment, giving you the improved data control and affordability. Come see how this service can help accelerate your engineering workloads with the power of Ansys and AWS HPC.

Summary: In 2019, UberCloud met with a Fortune 50 high-tech company that develops consumer-tech products—let’s call it TC-Anonymous (TCA). After moving several engineering simulations at TCA to AWS, UberCloud built its first automated self-service engineering simulation production platform on AWS and onboarded the first seven engineers of TCA’s R&D department to AWS. Today, TCA is running 34 different simulation workflows on AWS, with annual cloud consumption of $15 million, and increasing engineers’ productivity by a factor of 10 to 40, depending on the scalability of the simulation jobs and the cloud HPC hardware. In the AWS booth, see a demo of this simulation platform and get deeper insight into the challenges and benefits for TCA.

Summary: In this demo, see [RS1] how you can run CFD simulations in the cloud in just a few clicks. Simcenter Cloud HPC integrates Simcenter software with AWS Cloud infrastructure, delivering turnkey HPC as a service from a single platform. This cloud-based HPC solution frees engineers from the overhead of managing hardware and configuring infrastructure. It provides instant access to a vast pool of HPC resources without needing costly onsite clusters or queueing for HPC clusters to become available. Explore this comprehensive simulation solution that includes remote desktop–based authoring, allowing you to access Simcenter STAR-CCM+ from anywhere at any time using a web browser. With no need for local installations, you can set up, conduct, and post-process your simulations in the cloud.

Summary: TotalCAE on AWS reduces CAE simulation time up to 80% in just a few clicks for hundreds of CAE applications. See how easy it can be to adopt the latest AWS HPC technologies without being an IT expert, backed by TotalCAE one-hour support times.

Summary: In this demo, see [RS1] how you can run CFD simulations in the cloud in just a few clicks. Simcenter Cloud HPC integrates Simcenter software with AWS Cloud infrastructure, delivering turnkey HPC as a service from a single platform. This cloud-based HPC solution frees engineers from the overhead of managing hardware and configuring infrastructure. It provides instant access to a vast pool of HPC resources without needing costly onsite clusters or queueing for HPC clusters to become available. Explore this comprehensive simulation solution that includes remote desktop–based authoring, allowing you to access Simcenter STAR-CCM+ from anywhere at any time using a web browser. With no need for local installations, you can set up, conduct, and post-process your simulations in the cloud.

Summary: Migrating engineering workloads like CAE simulation to cloud-based HPC has been a challenge for many AWS users. In this demo, learn how Ansys Gateway powered by AWS offers an easy workflow to deploy virtual desktops and virtual clusters with your selected Ansys applications installed, configured, and tuned for optimal performance. This is deployed in your own AWS environment, giving you the improved data control and affordability. Come see how this service can help accelerate your engineering workloads with the power of Ansys and AWS HPC.

Summary: TotalCAE on AWS reduces CAE simulation time up to 80% in just a few clicks for hundreds of CAE applications. See how easy it can be to adopt the latest AWS HPC technologies without being an IT expert, backed by TotalCAE one-hour support times.

Summary: In 2019, UberCloud met with a Fortune 50 high-tech company that develops consumer-tech products—let’s call it TC-Anonymous (TCA). After moving several engineering simulations at TCA to AWS, UberCloud built its first automated self-service engineering simulation production platform on AWS and onboarded the first seven engineers of TCA’s R&D department to AWS. Today, TCA is running 34 different simulation workflows on AWS, with annual cloud consumption of $15 million, and increasing engineers’ productivity by a factor of 10 to 40, depending on the scalability of the simulation jobs and the cloud HPC hardware. In the AWS booth, see a demo of this simulation platform and get deeper insight into the challenges and benefits for TCA.

Summary: Migrating engineering workloads like CAE simulation to cloud-based HPC has been a challenge for many AWS users. In this demo, learn how Ansys Gateway powered by AWS offers an easy workflow to deploy virtual desktops and virtual clusters with your selected Ansys applications installed, configured, and tuned for optimal performance. This is deployed in your own AWS environment, giving you the improved data control and affordability. Come see how this service can help accelerate your engineering workloads with the power of Ansys and AWS HPC.

Summary: In this demo, see [RS1] how you can run CFD simulations in the cloud in just a few clicks. Simcenter Cloud HPC integrates Simcenter software with AWS Cloud infrastructure, delivering turnkey HPC as a service from a single platform. This cloud-based HPC solution frees engineers from the overhead of managing hardware and configuring infrastructure. It provides instant access to a vast pool of HPC resources without needing costly onsite clusters or queueing for HPC clusters to become available. Explore this comprehensive simulation solution that includes remote desktop–based authoring, allowing you to access Simcenter STAR-CCM+ from anywhere at any time using a web browser. With no need for local installations, you can set up, conduct, and post-process your simulations in the cloud.

Summary: In 2019, UberCloud met with a Fortune 50 high-tech company that develops consumer-tech products—let’s call it TC-Anonymous (TCA). After moving several engineering simulations at TCA to AWS, UberCloud built its first automated self-service engineering simulation production platform on AWS and onboarded the first seven engineers of TCA’s R&D department to AWS. Today, TCA is running 34 different simulation workflows on AWS, with annual cloud consumption of $15 million, and increasing engineers’ productivity by a factor of 10 to 40, depending on the scalability of the simulation jobs and the cloud HPC hardware. In the AWS booth, see a demo of this simulation platform and get deeper insight into the challenges and benefits for TCA.

Summary: https://sighpc-syspros.org/social/ Lucky Strike bowling, 500 16th St Mall #340, Denver, CO 80202

Summary: Arm, AWS, and NVIDIA want to welcome you to Denver with a happy hour downtown near the train station.

Summary: Developers from Arm, AWS, and NVIDIA Rm 407

Summary: https://sc23.supercomputing.org/presentation/?id=tut144&sess=sess238 Rm 205

Summary: Agenda: https://womeninhpc.org/events/sc-2023-workshop Rm 710

Summary: https://sc23.supercomputing.org/session/?sess=sess446 Rm 603

Summary: https://sc23.supercomputing.org/presentation/?id=bof126&sess=sess339 Rm 205-207

Summary: Agenda: https://sc23.supercomputing.org/presentation/?id=bof120&sess=sess350 Rm 407

Summary: Details: https://womeninhpc.org/events/sc-2023-networking-reception The Curtis Hotel

Summary: https://sc23.supercomputing.org/presentation/?id=bof222&sess=sess342 Rm 205-207

Summary: https://sc23.supercomputing.org/presentation/?id=bof113&sess=sess362 Rm 405-406-407

Summary: https://sc23.supercomputing.org/presentation/?id=bof110&sess=sess385 Rm 605

Summary: https://sc23.supercomputing.org/presentation/?id=bof212&sess=sess354 Rm 401-402

Summary: https://sc23.supercomputing.org/presentation/?id=bof196&sess=sess346 205-207

Summary: In 2019, UberCloud met with a Fortune 50 high-tech company that develops consumer-tech products—let’s call it TC-Anonymous (TCA). After moving several engineering simulations at TCA to AWS, UberCloud built its first automated self-service engineering simulation production platform on AWS and onboarded the first seven engineers of TCA’s R&D department to AWS. Today, TCA is running 34 different simulation workflows on AWS, with annual cloud consumption of $15 million, and increasing engineers’ productivity by a factor of 10 to 40, depending on the scalability of the simulation jobs and the cloud HPC hardware. In the AWS booth, see a demo of this simulation platform and get deeper insight into the challenges and benefits for TCA.

Summary: In 2019, UberCloud met with a Fortune 50 high-tech company that develops consumer-tech products—let’s call it TC-Anonymous (TCA). After moving several engineering simulations at TCA to AWS, UberCloud built its first automated self-service engineering simulation production platform on AWS and onboarded the first seven engineers of TCA’s R&D department to AWS. Today, TCA is running 34 different simulation workflows on AWS, with annual cloud consumption of $15 million, and increasing engineers’ productivity by a factor of 10 to 40, depending on the scalability of the simulation jobs and the cloud HPC hardware. In the AWS booth, see a demo of this simulation platform and get deeper insight into the challenges and benefits for TCA.

Summary: In 2019, UberCloud met with a Fortune 50 high-tech company that develops consumer-tech products—let’s call it TC-Anonymous (TCA). After moving several engineering simulations at TCA to AWS, UberCloud built its first automated self-service engineering simulation production platform on AWS and onboarded the first seven engineers of TCA’s R&D department to AWS. Today, TCA is running 34 different simulation workflows on AWS, with annual cloud consumption of $15 million, and increasing engineers’ productivity by a factor of 10 to 40, depending on the scalability of the simulation jobs and the cloud HPC hardware. In the AWS booth, see a demo of this simulation platform and get deeper insight into the challenges and benefits for TCA.

Summary: Nextira, a part of Accenture, specializes in helping companies make use of state-of-the-art AI tooling and infrastructure in the cloud. In this session, explore an example ML implementation for legal informatics, where there is a pressing need to be continuously updated on the latest changes to US laws and regulations. Get an introduction to a system that deploys a Retrieval Augmented Generation (RAG) model on Amazon Web Services (AWS), focusing on a regulatory and compliance use case.

Summary: Nextira, a part of Accenture, specializes in helping companies make use of state-of-the-art AI tooling and infrastructure in the cloud. In this session, explore an example ML implementation for legal informatics, where there is a pressing need to be continuously updated on the latest changes to US laws and regulations. Get an introduction to a system that deploys a Retrieval Augmented Generation (RAG) model on Amazon Web Services (AWS), focusing on a regulatory and compliance use case.

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: Promethium is a revolutionary chemistry simulation platform developed by QC Ware that will significantly accelerate the drug, material, and chemical discovery process. Built for virtually unlimited scalability on Amazon EC2 P3 and P4d instances, Promethium was designed to be GPU native and perform fully ab initio density functional theory (DFT) calculations with exceptional speed and accuracy. Coupled with its capability to treat system sizes not typically accessible by DFT, Promethium has the potential to transform the industry and bring immediate value to customers in areas like pharma and materials science. Whether you’re trying to discover the next blockbuster drug or developing more advanced materials, join this session to learn how Promethium can help you make breakthrough discoveries and take your research to the next level.

Summary: Promethium is a revolutionary chemistry simulation platform developed by QC Ware that will significantly accelerate the drug, material, and chemical discovery process. Built for virtually unlimited scalability on Amazon EC2 P3 and P4d instances, Promethium was designed to be GPU native and perform fully ab initio density functional theory (DFT) calculations with exceptional speed and accuracy. Coupled with its capability to treat system sizes not typically accessible by DFT, Promethium has the potential to transform the industry and bring immediate value to customers in areas like pharma and materials science. Whether you’re trying to discover the next blockbuster drug or developing more advanced materials, join this session to learn how Promethium can help you make breakthrough discoveries and take your research to the next level.

Summary: The generative capability of AI holds significant promise across a diverse range of industries fueled by engineering design, such as automotive, motor sports, and aerospace. In this session, discover how to create a pipeline that uses generative AI designs to feed conventional physics-based simulations, and learn how to loop all of this to create a converging, rapid design process for exploring new design concepts starting from a single image. Learn how to use open source frameworks to create digital twins, deploy OpenFOAM in containers for the simulations, and use serverless tools coupled with AWS Batch to drive all this at scale.

Summary: AWS Trainium purpose-built accelerators can be used to train large language models (LLMs) such as Llama 2. AWS Neuron is the SDK used to run deep learning workloads on AWS Trainium–based instances. It integrates the AI framework, such as PyTorch or TensorFlow, with the hardware to enable end-to-end ML development, including building new models and training and scaling these models. The AWS Neuron SDK supports multiple distributed libraries using tensor parallelism, pipeline parallelism, and sequence parallelism for large-scale training. In this session, find out how to use AWS ParallelCluster to launch Amazon EC2 Trn1 instances and train a Llama 2 model (up to 70B) on more than 1,000 chips.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: In this session, explore innovations in climate simulation, weather modeling, energy efficiency, and more powered by full-stack Accelerators from AWS and NVIDIA.

Summary: The generative capability of AI holds significant promise across a diverse range of industries fueled by engineering design, such as automotive, motor sports, and aerospace. In this session, discover how to create a pipeline that uses generative AI designs to feed conventional physics-based simulations, and learn how to loop all of this to create a converging, rapid design process for exploring new design concepts starting from a single image. Learn how to use open source frameworks to create digital twins, deploy OpenFOAM in containers for the simulations, and use serverless tools coupled with AWS Batch to drive all this at scale.

Summary: AWS Trainium purpose-built accelerators can be used to train large language models (LLMs) such as Llama 2. AWS Neuron is the SDK used to run deep learning workloads on AWS Trainium–based instances. It integrates the AI framework, such as PyTorch or TensorFlow, with the hardware to enable end-to-end ML development, including building new models and training and scaling these models. The AWS Neuron SDK supports multiple distributed libraries using tensor parallelism, pipeline parallelism, and sequence parallelism for large-scale training. In this session, find out how to use AWS ParallelCluster to launch Amazon EC2 Trn1 instances and train a Llama 2 model (up to 70B) on more than 1,000 chips.

Summary: In this session, explore innovations in climate simulation, weather modeling, energy efficiency, and more powered by full-stack Accelerators from AWS and NVIDIA.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: The generative capability of AI holds significant promise across a diverse range of industries fueled by engineering design, such as automotive, motor sports, and aerospace. In this session, discover how to create a pipeline that uses generative AI designs to feed conventional physics-based simulations, and learn how to loop all of this to create a converging, rapid design process for exploring new design concepts starting from a single image. Learn how to use open source frameworks to create digital twins, deploy OpenFOAM in containers for the simulations, and use serverless tools coupled with AWS Batch to drive all this at scale.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: Developers from Arm, AWS, and NVIDIA Rm 407

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: The Fugaku supercomputer has made substantial strides in computational capability and user accessibility by integrating a robust system software stack involving HPC, Big Data, AI, and more than 50 HPC productivity applications and tools preinstalled and maintained ready-to-use and accessible through a robust GUI environment. As developer and owner of Fugaku, RIKEN Center for Computational Science (R-CCS) continues development of Virtual Fugaku, an initiative aimed at seamlessly extending the software environment of Fugaku to a broader user base and packaged into a containerized portable software distribution. In this session, learn how R-CCS and AWS are working together to deliver the full capability of Fugaku onto a virtual cluster on the AWS Cloud using AWS Graviton3 and AWS Graviton3E Arm-based cloud infrastructure. This integration highlights Fugaku's versatility and AWS's commitment to embrace cutting-edge technologies in the realm of HPC and high-end AI.

Summary: AWS Arm-based Graviton3 instances provide high performance for HPC and AI while delivering cost and energy savings. Running on Arm instances is straightforward and supported by open source and major commercial applications. In this demonstration, explore the performance, scalability, and simplicity of HPC and AI on Arm.

Summary: Agenda: https://sc23.supercomputing.org/presentation/?id=bof120&sess=sess350 Rm 407

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: AWS Arm-based Graviton3 instances provide high performance for HPC and AI while delivering cost and energy savings. Running on Arm instances is straightforward and supported by open source and major commercial applications. In this demonstration, explore the performance, scalability, and simplicity of HPC and AI on Arm.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: AWS Arm-based Graviton3 instances provide high performance for HPC and AI while delivering cost and energy savings. Running on Arm instances is straightforward and supported by open source and major commercial applications. In this demonstration, explore the performance, scalability, and simplicity of HPC and AI on Arm.

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: Promethium is a revolutionary chemistry simulation platform developed by QC Ware that will significantly accelerate the drug, material, and chemical discovery process. Built for virtually unlimited scalability on Amazon EC2 P3 and P4d instances, Promethium was designed to be GPU native and perform fully ab initio density functional theory (DFT) calculations with exceptional speed and accuracy. Coupled with its capability to treat system sizes not typically accessible by DFT, Promethium has the potential to transform the industry and bring immediate value to customers in areas like pharma and materials science. Whether you’re trying to discover the next blockbuster drug or developing more advanced materials, join this session to learn how Promethium can help you make breakthrough discoveries and take your research to the next level.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: Promethium is a revolutionary chemistry simulation platform developed by QC Ware that will significantly accelerate the drug, material, and chemical discovery process. Built for virtually unlimited scalability on Amazon EC2 P3 and P4d instances, Promethium was designed to be GPU native and perform fully ab initio density functional theory (DFT) calculations with exceptional speed and accuracy. Coupled with its capability to treat system sizes not typically accessible by DFT, Promethium has the potential to transform the industry and bring immediate value to customers in areas like pharma and materials science. Whether you’re trying to discover the next blockbuster drug or developing more advanced materials, join this session to learn how Promethium can help you make breakthrough discoveries and take your research to the next level.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: As focus shifts from data-centric to compute-intensive tasks, many traditional cloud tools struggle to maintain efficiency and scalability. Built on AWS, Covalent provides developer-friendly abstractions for high-compute workloads. Users can integrate their own AWS Batch jobs for computing while Covalent takes care of orchestration to maintain data privacy. The framework harnesses AWS services such as Amazon SQS, Amazon EventBridge, and AWS Lambda to enhance scalability and cost-efficiency. Covalent optimizes costs and simplifies development, making compute-intensive tasks feel like an extension of one’s local laptop. This demo delves into Covalent’s unique positioning in meeting the demands of high-compute developments, including large language models (AIs) and generative AI.

Summary: Amazon EC2 Hpc7a instances, powered by fourth-generation AMD EPYC processors, deliver up to 2.5 times better performance compared to Amazon EC2 Hpc6a instances. Hpc7a instances feature 2 times higher core density (up to 192 cores), 2.1 times higher memory bandwidth throughput, 2 times memory (768 GB), and 3 times higher network bandwidth compared to Hpc6a instances. In this demonstration, explore how Hpc7a instances are designed to run your tightly coupled HPC workloads, such as computational fluid dynamics, weather forecasting, and multiphysics simulations, with better performance.

Summary: In this demonstration, learn about the new Amazon EC2 C7i instance, powered by custom fourth-generation Intel Xeon processors that bring Intel Accelerator Engines, and the Hpc6id instance, powered by third-generation Intel Xeon processors, that offer cost-effective price performance for memory-bound and data-intensive high performance computing (HPC) workloads. Discover how the Intel oneAPI HPC Toolkit delivers what developers need to build, analyze, optimize, and scale HPC applications. With these instances, businesses in the HPC community can address their high performance and transformation demands. Together, AWS and Intel offer customers a broad and deep set of leading compute, networking, storage, cloud orchestration, and visualization technologies.

Summary: In this demonstration, learn about the new Amazon EC2 C7i instance, powered by custom fourth-generation Intel Xeon processors that bring Intel Accelerator Engines, and the Hpc6id instance, powered by third-generation Intel Xeon processors, that offer cost-effective price performance for memory-bound and data-intensive high performance computing (HPC) workloads. Discover how the Intel oneAPI HPC Toolkit delivers what developers need to build, analyze, optimize, and scale HPC applications. With these instances, businesses in the HPC community can address their high performance and transformation demands. Together, AWS and Intel offer customers a broad and deep set of leading compute, networking, storage, cloud orchestration, and visualization technologies.

Summary: Amazon EC2 Hpc7a instances, powered by fourth-generation AMD EPYC processors, deliver up to 2.5 times better performance compared to Amazon EC2 Hpc6a instances. Hpc7a instances feature 2 times higher core density (up to 192 cores), 2.1 times higher memory bandwidth throughput, 2 times memory (768 GB), and 3 times higher network bandwidth compared to Hpc6a instances. In this demonstration, explore how Hpc7a instances are designed to run your tightly coupled HPC workloads, such as computational fluid dynamics, weather forecasting, and multiphysics simulations, with better performance.

Summary: Amazon EC2 Hpc7a instances, powered by fourth-generation AMD EPYC processors, deliver up to 2.5 times better performance compared to Amazon EC2 Hpc6a instances. Hpc7a instances feature 2 times higher core density (up to 192 cores), 2.1 times higher memory bandwidth throughput, 2 times memory (768 GB), and 3 times higher network bandwidth compared to Hpc6a instances. In this demonstration, explore how Hpc7a instances are designed to run your tightly coupled HPC workloads, such as computational fluid dynamics, weather forecasting, and multiphysics simulations, with better performance.

Summary: In this demonstration, learn about the new Amazon EC2 C7i instance, powered by custom fourth-generation Intel Xeon processors that bring Intel Accelerator Engines, and the Hpc6id instance, powered by third-generation Intel Xeon processors, that offer cost-effective price performance for memory-bound and data-intensive high performance computing (HPC) workloads. Discover how the Intel oneAPI HPC Toolkit delivers what developers need to build, analyze, optimize, and scale HPC applications. With these instances, businesses in the HPC community can address their high performance and transformation demands. Together, AWS and Intel offer customers a broad and deep set of leading compute, networking, storage, cloud orchestration, and visualization technologies.

Summary: In this session, learn about ArmoniK, an open source job scheduler that helps you deploy elastic workloads on premises and on the cloud. To help integrate on premises and on AWS , ArmoniK considers the location of the data and manages data transfers between tasks. Kubernetes is the only requirement for ArmoniK, which makes it easy to deploy in a variety of environments. ArmoniK is currently used to run large graphs of tasks (up to 100 million tasks) with strict business service-level agreements (SLA).

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: Nextira, a part of Accenture, specializes in helping companies make use of state-of-the-art AI tooling and infrastructure in the cloud. In this session, explore an example ML implementation for legal informatics, where there is a pressing need to be continuously updated on the latest changes to US laws and regulations. Get an introduction to a system that deploys a Retrieval Augmented Generation (RAG) model on Amazon Web Services (AWS), focusing on a regulatory and compliance use case.

Summary: Nextira, a part of Accenture, specializes in helping companies make use of state-of-the-art AI tooling and infrastructure in the cloud. In this session, explore an example ML implementation for legal informatics, where there is a pressing need to be continuously updated on the latest changes to US laws and regulations. Get an introduction to a system that deploys a Retrieval Augmented Generation (RAG) model on Amazon Web Services (AWS), focusing on a regulatory and compliance use case.

Summary: Cognizant is proud to be an AWS Competency Partner, having demonstrated technical proficiency and proven customer success with HPC solutions encompassing high-performance solvers, HPC platforms, and workload management. Cognizant has engaged with scientists in different fields (such as life sciences, oil and gas, and financial services) and with the AWS HPC team to implement the latest technologies, architectures, processors, and compilers to solve problems efficiently in these industries. Learn how Cognizant’s workshops and advisory services on HPC and quantum computing for both business executives and technical experts have enhanced customers’ efforts and success during HPC deployment or modernization.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: Compute availability is the biggest challenge with most workloads (including AI and ML) now chasing constrained global capacity. The imperative is to think and act quickly to get what you need but be smart, be efficient, and reduce risk. On-premises resources are still very much part of the equation, and, combined with the huge global cloud footprint of AWS, YellowDog uniquely enables workload portability at scale efficiently and cost effectively, reducing operational risk and accelerating insight.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: Compute availability is the biggest challenge with most workloads (including AI and ML) now chasing constrained global capacity. The imperative is to think and act quickly to get what you need but be smart, be efficient, and reduce risk. On-premises resources are still very much part of the equation, and, combined with the huge global cloud footprint of AWS, YellowDog uniquely enables workload portability at scale efficiently and cost effectively, reducing operational risk and accelerating insight.

Summary: High-throughput computing (HTC) is emerging as a powerful capability for engineering and research across many industries. From drug discovery and computational chemistry to electronic design automation (EDA) and environmental research, HTC is helping scientific and engineering teams perform massively parallel processing to quickly analyze vast quantities of information for solving some of the world’s most complex scientific and engineering challenges. In this demo, see how companies like Arm use the Rescale HTC platform powered by services like AWS Batch and AWS Graviton3E-based Amazon EC2 instances to run large-scale analyses for semiconductor design.

Summary: In this demonstration, learn about the Parallel Works single pane of glass, multicloud environment operated by General Dynamics Information Technology for NOAA’s research and development HPC organization. Learn about key aspects of the implementation and optimization of the platform for large-scale HPC-driven research and development. The Parallel Works platform uses more than 20 AWS services, including Amazon FSx for Lustre, Amazon S3, Amazon EC2, and Amazon EFS. Dive deep on the end user experience, which incorporates the familiar on-premises computing environment and the flexibility and innovation of the cloud. Explore the portability of this environment to the multicloud environment and how it bridges the cloud to NOAA’s on-premises HPC resources. Discover how this environment supports AI operations that are critical to the evolution of environmental prediction at NOAA.

Summary: In this demonstration, learn about the Parallel Works single pane of glass, multicloud environment operated by General Dynamics Information Technology for NOAA’s research and development HPC organization. Learn about key aspects of the implementation and optimization of the platform for large-scale HPC-driven research and development. The Parallel Works platform uses more than 20 AWS services, including Amazon FSx for Lustre, Amazon S3, Amazon EC2, and Amazon EFS. Dive deep on the end user experience, which incorporates the familiar on-premises computing environment and the flexibility and innovation of the cloud. Explore the portability of this environment to the multicloud environment and how it bridges the cloud to NOAA’s on-premises HPC resources. Discover how this environment supports AI operations that are critical to the evolution of environmental prediction at NOAA.

Summary: In this demonstration, learn about the Parallel Works single pane of glass, multicloud environment operated by General Dynamics Information Technology for NOAA’s research and development HPC organization. Learn about key aspects of the implementation and optimization of the platform for large-scale HPC-driven research and development. The Parallel Works platform uses more than 20 AWS services, including Amazon FSx for Lustre, Amazon S3, Amazon EC2, and Amazon EFS. Dive deep on the end user experience, which incorporates the familiar on-premises computing environment and the flexibility and innovation of the cloud. Explore the portability of this environment to the multicloud environment and how it bridges the cloud to NOAA’s on-premises HPC resources. Discover how this environment supports AI operations that are critical to the evolution of environmental prediction at NOAA.

Summary: Using real life examples and use cases, DXC explains how traditional on-premises models of HPC can be transformed by the introduction of AWS Cloud. In this session, learn the merits of hybrid compute and how to manage the transition to a successful managed solution.

Summary: Using real life examples and use cases, DXC explains how traditional on-premises models of HPC can be transformed by the introduction of AWS Cloud. In this session, learn the merits of hybrid compute and how to manage the transition to a successful managed solution.

Summary: Using real life examples and use cases, DXC explains how traditional on-premises models of HPC can be transformed by the introduction of AWS Cloud. In this session, learn the merits of hybrid compute and how to manage the transition to a successful managed solution.

Summary: In this demonstration, learn about the new Amazon EC2 C7i instance, powered by custom fourth-generation Intel Xeon processors that bring Intel Accelerator Engines, and the Hpc6id instance, powered by third-generation Intel Xeon processors, that offer cost-effective price performance for memory-bound and data-intensive high performance computing (HPC) workloads. Discover how the Intel oneAPI HPC Toolkit delivers what developers need to build, analyze, optimize, and scale HPC applications. With these instances, businesses in the HPC community can address their high performance and transformation demands. Together, AWS and Intel offer customers a broad and deep set of leading compute, networking, storage, cloud orchestration, and visualization technologies.

Summary: In this demonstration, learn about the new Amazon EC2 C7i instance, powered by custom fourth-generation Intel Xeon processors that bring Intel Accelerator Engines, and the Hpc6id instance, powered by third-generation Intel Xeon processors, that offer cost-effective price performance for memory-bound and data-intensive high performance computing (HPC) workloads. Discover how the Intel oneAPI HPC Toolkit delivers what developers need to build, analyze, optimize, and scale HPC applications. With these instances, businesses in the HPC community can address their high performance and transformation demands. Together, AWS and Intel offer customers a broad and deep set of leading compute, networking, storage, cloud orchestration, and visualization technologies.

Summary: In this demonstration, learn about the new Amazon EC2 C7i instance, powered by custom fourth-generation Intel Xeon processors that bring Intel Accelerator Engines, and the Hpc6id instance, powered by third-generation Intel Xeon processors, that offer cost-effective price performance for memory-bound and data-intensive high performance computing (HPC) workloads. Discover how the Intel oneAPI HPC Toolkit delivers what developers need to build, analyze, optimize, and scale HPC applications. With these instances, businesses in the HPC community can address their high performance and transformation demands. Together, AWS and Intel offer customers a broad and deep set of leading compute, networking, storage, cloud orchestration, and visualization technologies.

Summary: Amazon FSx for Lustre helps you deploy high-performance Lustre systems in just a few clicks and has capabilities that most traditional storage systems lack—think of processing hundreds of gigabytes per second of throughput without months of planning, logistics, and testing. In this session, learn how to spin up a large-scale and fast Lustre file system in less time than it takes to make a coffee. Then, discover how it can synchronize with massive datasets in object storage and how you can choose from different classes of storage performance and price to match your organization’s needs. Finally, learn how you can use Amazon File Cache to deploy Lustre as a massively parallel cloud cache for storage systems in your own data centers and how this can be a useful tool for creating hybrid facilities that speed up your users’ time to results.

Summary: Amazon FSx for Lustre helps you deploy high-performance Lustre systems in just a few clicks and has capabilities that most traditional storage systems lack—think of processing hundreds of gigabytes per second of throughput without months of planning, logistics, and testing. In this session, learn how to spin up a large-scale and fast Lustre file system in less time than it takes to make a coffee. Then, discover how it can synchronize with massive datasets in object storage and how you can choose from different classes of storage performance and price to match your organization’s needs. Finally, learn how you can use Amazon File Cache to deploy Lustre as a massively parallel cloud cache for storage systems in your own data centers and how this can be a useful tool for creating hybrid facilities that speed up your users’ time to results.

Summary: Amazon FSx for Lustre helps you deploy high-performance Lustre systems in just a few clicks and has capabilities that most traditional storage systems lack—think of processing hundreds of gigabytes per second of throughput without months of planning, logistics, and testing. In this session, learn how to spin up a large-scale and fast Lustre file system in less time than it takes to make a coffee. Then, discover how it can synchronize with massive datasets in object storage and how you can choose from different classes of storage performance and price to match your organization’s needs. Finally, learn how you can use Amazon File Cache to deploy Lustre as a massively parallel cloud cache for storage systems in your own data centers and how this can be a useful tool for creating hybrid facilities that speed up your users’ time to results.

Summary: UCit's solutions on AWS ParallelCluster can help you create custom clusters on AWS for Hybrid operations. In this session, learn how you can benefit from workstation fleets integration and seamless pre- and post-processing applications for both Linux and Windows fleets. OKA Cloud is included to increase efficiency, limit waste, and provide powerful insights on cluster usage, performance, and costs.

Summary: Sanofi opted to shift from on-premises infrastructure to Amazon Web Services (AWS) for its business-critical WISE platform (200+ servers), to mitigate risk and enhance performance. Join this session to learn how this cloud migration allowed for increased agility, better user-centric services, and substantial computational power for emerging applications like genomics and AI algorithms. The move aimed to minimize downtime, ensure stability, and meet the escalating demands of thousands of users, averting potential business repercussions.

Summary: UCit's solutions on AWS ParallelCluster can help you create custom clusters on AWS for Hybrid operations. In this session, learn how you can benefit from workstation fleets integration and seamless pre- and post-processing applications for both Linux and Windows fleets. OKA Cloud is included to increase efficiency, limit waste, and provide powerful insights on cluster usage, performance, and costs.

Summary: UCit's solutions on AWS ParallelCluster can help you create custom clusters on AWS for Hybrid operations. In this session, learn how you can benefit from workstation fleets integration and seamless pre- and post-processing applications for both Linux and Windows fleets. OKA Cloud is included to increase efficiency, limit waste, and provide powerful insights on cluster usage, performance, and costs.

Summary: Sanofi opted to shift from on-premises infrastructure to Amazon Web Services (AWS) for its business-critical WISE platform (200+ servers), to mitigate risk and enhance performance. Join this session to learn how this cloud migration allowed for increased agility, better user-centric services, and substantial computational power for emerging applications like genomics and AI algorithms. The move aimed to minimize downtime, ensure stability, and meet the escalating demands of thousands of users, averting potential business repercussions.

Summary: Sanofi opted to shift from on-premises infrastructure to Amazon Web Services (AWS) for its business-critical WISE platform (200+ servers), to mitigate risk and enhance performance. Join this session to learn how this cloud migration allowed for increased agility, better user-centric services, and substantial computational power for emerging applications like genomics and AI algorithms. The move aimed to minimize downtime, ensure stability, and meet the escalating demands of thousands of users, averting potential business repercussions.

Summary: https://sighpc-syspros.org/social/ Lucky Strike bowling, 500 16th St Mall #340, Denver, CO 80202

Summary: Arm, AWS, and NVIDIA want to welcome you to Denver with a happy hour downtown near the train station.

Summary: Details: https://womeninhpc.org/events/sc-2023-networking-reception The Curtis Hotel

Summary: https://awsatsc23.splashthat.com/ Henry's Tavern

Summary: https://awsxnvidiareception.splashthat.com/ Hilton Denver City Center

Summary: https://aws-research-breakfast-sc23.splashthat.com/ The Curtis Hotel

Summary: https://rescale.com/lp/sc23-hpc-mixer/ Hilton @ Denver City Center

Summary: HPC systems deployed on AWS often rely on various resources like file systems, networking, and directory services. Despite AWS ParallelCluster’s automation capabilities, setting up and integrating these dependencies can become complex if you have a lot of custom requirements. In this session, walk through how the new HPC Recipes for AWS library simplifies this process. Then, learn how to create a multi-user environment, configure shared storage, set up a budget, and deploy a benchmarking cluster with just a few clicks in the AWS Management Console. Finally, discover how to combine, modify, and reuse these recipes to meet your particular needs—without needing to be an AWS expert.

Summary: Training AI (ML) models requires setting up clusters that enable many GPUs to talk to each other using low-latency networking that is capable of driving massive throughput. This session shares in real time how you can quickly and easily set up an ML training cluster using AWS ParallelCluster, NVIDIA GPUs, Elastic Fabric Adapter (EFA), and Amazon FSx for Lustre. Learn how to train a GPT model using Megatron-LM, store the results, and collapse the infrastructure when you’re finished.

Summary: HPC systems deployed on AWS often rely on various resources like file systems, networking, and directory services. Despite AWS ParallelCluster’s automation capabilities, setting up and integrating these dependencies can become complex if you have a lot of custom requirements. In this session, walk through how the new HPC Recipes for AWS library simplifies this process. Then, learn how to create a multi-user environment, configure shared storage, set up a budget, and deploy a benchmarking cluster with just a few clicks in the AWS Management Console. Finally, discover how to combine, modify, and reuse these recipes to meet your particular needs—without needing to be an AWS expert.

Summary: Join this session to learn how organizations across different fields—from EDA to drug design—have been using AWS to scale and improve their R&D. Learn how to create a real, large, and very complete cluster in a few minutes using AWS HPC services, including AWS ParallelCluster, Amazon FSx for Lustre, and visualization with NICE DCV. Find out how to customize compute images to include applications and development tools, and discover how to integrate with Spack for reliable deployment of open source packages.

Summary: HPC systems and workloads have their own unique set of security challenges. Running these workloads on AWS can help you address these challenges in new ways and with this freedom comes the ability to choose different solutions based on your organization’s unique needs. In this session, walk through a traditional HPC security scenario and discover different approaches you can take to address these challenges. Find out how the AWS secure-by-design architecture helps take care of common security challenges, and learn best practices for building secure HPC environments on AWS. When you’re done, you’ll understand how your cloud infrastructure can be even more secure than your data centers.

Summary: AWS Trainium purpose-built accelerators can be used to train large language models (LLMs) such as Llama 2. AWS Neuron is the SDK used to run deep learning workloads on AWS Trainium–based instances. It integrates the AI framework, such as PyTorch or TensorFlow, with the hardware to enable end-to-end ML development, including building new models and training and scaling these models. The AWS Neuron SDK supports multiple distributed libraries using tensor parallelism, pipeline parallelism, and sequence parallelism for large-scale training. In this session, find out how to use AWS ParallelCluster to launch Amazon EC2 Trn1 instances and train a Llama 2 model (up to 70B) on more than 1,000 chips.

Summary: Training AI (ML) models requires setting up clusters that enable many GPUs to talk to each other using low-latency networking that is capable of driving massive throughput. This session shares in real time how you can quickly and easily set up an ML training cluster using AWS ParallelCluster, NVIDIA GPUs, Elastic Fabric Adapter (EFA), and Amazon FSx for Lustre. Learn how to train a GPT model using Megatron-LM, store the results, and collapse the infrastructure when you’re finished.

Summary: HPC systems deployed on AWS often rely on various resources like file systems, networking, and directory services. Despite AWS ParallelCluster’s automation capabilities, setting up and integrating these dependencies can become complex if you have a lot of custom requirements. In this session, walk through how the new HPC Recipes for AWS library simplifies this process. Then, learn how to create a multi-user environment, configure shared storage, set up a budget, and deploy a benchmarking cluster with just a few clicks in the AWS Management Console. Finally, discover how to combine, modify, and reuse these recipes to meet your particular needs—without needing to be an AWS expert.

Summary: Join this session to learn how organizations across different fields—from EDA to drug design—have been using AWS to scale and improve their R&D. Learn how to create a real, large, and very complete cluster in a few minutes using AWS HPC services, including AWS ParallelCluster, Amazon FSx for Lustre, and visualization with NICE DCV. Find out how to customize compute images to include applications and development tools, and discover how to integrate with Spack for reliable deployment of open source packages.

Summary: HPC systems and workloads have their own unique set of security challenges. Running these workloads on AWS can help you address these challenges in new ways and with this freedom comes the ability to choose different solutions based on your organization’s unique needs. In this session, walk through a traditional HPC security scenario and discover different approaches you can take to address these challenges. Find out how the AWS secure-by-design architecture helps take care of common security challenges, and learn best practices for building secure HPC environments on AWS. When you’re done, you’ll understand how your cloud infrastructure can be even more secure than your data centers.

Summary: AWS Trainium purpose-built accelerators can be used to train large language models (LLMs) such as Llama 2. AWS Neuron is the SDK used to run deep learning workloads on AWS Trainium–based instances. It integrates the AI framework, such as PyTorch or TensorFlow, with the hardware to enable end-to-end ML development, including building new models and training and scaling these models. The AWS Neuron SDK supports multiple distributed libraries using tensor parallelism, pipeline parallelism, and sequence parallelism for large-scale training. In this session, find out how to use AWS ParallelCluster to launch Amazon EC2 Trn1 instances and train a Llama 2 model (up to 70B) on more than 1,000 chips.

Summary: Training AI (ML) models requires setting up clusters that enable many GPUs to talk to each other using low-latency networking that is capable of driving massive throughput. This session shares in real time how you can quickly and easily set up an ML training cluster using AWS ParallelCluster, NVIDIA GPUs, Elastic Fabric Adapter (EFA), and Amazon FSx for Lustre. Learn how to train a GPT model using Megatron-LM, store the results, and collapse the infrastructure when you’re finished.

Summary: HPC systems deployed on AWS often rely on various resources like file systems, networking, and directory services. Despite AWS ParallelCluster’s automation capabilities, setting up and integrating these dependencies can become complex if you have a lot of custom requirements. In this session, walk through how the new HPC Recipes for AWS library simplifies this process. Then, learn how to create a multi-user environment, configure shared storage, set up a budget, and deploy a benchmarking cluster with just a few clicks in the AWS Management Console. Finally, discover how to combine, modify, and reuse these recipes to meet your particular needs—without needing to be an AWS expert.

Summary: Quantum computing specialists from the Amazon Braket team Rm 406

Summary: Join us for a demo to help demystify quantum programming. Explore the Quantum Approximate Optimization Algorithm (QAOA) service, which simplifies quantum computing for all skill levels. See the practical application of the QAOA service on AWS via Rigetti hardware, showcasing Strangeworks’s latest cargo loading problem. Whether you’re a quantum novice or an experienced coder, gain insights into quantum computing’s potential. Discover how Strangeworks’s intuitive platform is reshaping quantum accessibility, making it easier and more efficient to harness quantum power. Start your quantum journey with Strangeworks and witness the transformative possibilities of quantum computing.

Summary: BlueQubit is a software infrastructure company for running hybrid compute jobs on QPU/GPU/CPU. Learn about their fast, zero-setup GPU simulators.

Summary: Companies and talent alike are catching on to quantum computing. Companies are racing to build quantum computers with the most qubits, and people, from business leaders to students and job seekers, are looking to join the industry. However, a challenge is that today’s quantum computers are noisy and prone to error, which affects the accuracy of everything they attempt to do. This is what Q-CTRL is solving. Q-CTRL’s mission is to make quantum technology useful. This demo showcases how Q-CTRL infrastructure software is delivering business advantage from quantum computing. Q-CTRL builds commercial-ready, quantum control and error reduction products available on the AWS Marketplace and integrated with Amazon Braket.

Summary: SoftServe combines cutting-edge advisory and software solutions. Their comprehensive R&DaaS includes fundamental research, hardware assessment, cloud proficiency, and talent development, allowing them to empower enterprises and software companies to rediscover differentiation, expedite solution development, and remain competitive in today’s marketplace, regardless of where you are on your journey. In this demo, explore SoftServe’s quantum industrialization journey, from pinpointing use cases to crafting solutions and seamless integration while benefiting from the flexibility and availability of AWS Cloud infrastructure. Then, explore widely applied combinatorial optimization problems. Additionally, learn about addressing demanding optimization problems through traditional high performance computing techniques, such as utilizing Monte Carlo methods for multi-GPU optimization and agent-based modeling for GPU-accelerated scheduling.

Summary: Deloitte used QuEra’s neutral atom system to classify manufacturing images at their Smart Factory in Wichita, Kansas. In this demo, see how Deloitte took a real-world business problem, solved it with cutting-edge quantum AI techniques (quantum reservoir computing), and productionized it using a production-ready dashboard. Deloitte compared QuEra’s implementation of quantum reservoir computing against classical methodologies and found the results exceeded the method currently being used. Learn how Deloitte is working to productionize quantum AI algorithms as part of The Smart Factory quality assurance efforts and are working with the team to find the best methods for performing these tasks in a manufacturing facility.

Summary: Amazon Braket helps organizations get access to quantum computing hardware and simulators so they can speed up their scientific research and software development for quantum computing. This session shows short tutorials and shares how to run quantum circuits using real gate-based devices and simulators. Learn how to run your first quantum AI algorithm using Amazon Braket Hybrid Jobs. Along the way, explore Amazon Braket’s features, see examples from the AWS quantum algorithm library, and get your questions answered.

Summary: Deloitte used QuEra’s neutral atom system to classify manufacturing images at their Smart Factory in Wichita, Kansas. In this demo, see how Deloitte took a real-world business problem, solved it with cutting-edge quantum AI techniques (quantum reservoir computing), and productionized it using a production-ready dashboard. Deloitte compared QuEra’s implementation of quantum reservoir computing against classical methodologies and found the results exceeded the method currently being used. Learn how Deloitte is working to productionize quantum AI algorithms as part of The Smart Factory quality assurance efforts and are working with the team to find the best methods for performing these tasks in a manufacturing facility.

Summary: Join us for a demo to help demystify quantum programming. Explore the Quantum Approximate Optimization Algorithm (QAOA) service, which simplifies quantum computing for all skill levels. See the practical application of the QAOA service on AWS via Rigetti hardware, showcasing Strangeworks’s latest cargo loading problem. Whether you’re a quantum novice or an experienced coder, gain insights into quantum computing’s potential. Discover how Strangeworks’s intuitive platform is reshaping quantum accessibility, making it easier and more efficient to harness quantum power. Start your quantum journey with Strangeworks and witness the transformative possibilities of quantum computing.

Summary: This demo introduces QuEra and Bloqade, an SDK and emulator for neutral-atom quantum computing. See its success in adoption, some of its features relevant for high performance computing (such as multi-threading and GPU), and its general functionality and pipeline for operating real QPUs.

Summary: During this demo, discover IonQ’s offerings tailored for Amazon Braket customers. Delve into IonQ’s roadmap and upcoming architectures, Forte and Tempo. Explore new features like debiasing and sharpening, and see a brief hands-on demo of initiating Amazon Braket with IonQ’s QPU backends.

Summary: The MATLAB Support Package for Quantum Computing lets you build, simulate, and run quantum algorithms on real quantum hardware through Amazon Braket. In this demo, explore how you can use the support package within the MATLAB environment to 1/ build circuits and implement quantum algorithms using a variety of built-in and customizable composite gates; 2/ verify the implementations with local simulators in MATLAB or simulators hosted on Amazon Braket; 3/ run the algorithms on quantum computers using Amazon Braket; and 4/ visualize, analyze, and plot results. See how the support package allows you to prototype algorithms to accelerate optimization, scenario simulation, and ML, in addition to helping you solve currently unsolvable problems in chemistry and material simulations.

Summary: Companies and talent alike are catching on to quantum computing. Companies are racing to build quantum computers with the most qubits, and people, from business leaders to students and job seekers, are looking to join the industry. However, a challenge is that today’s quantum computers are noisy and prone to error, which affects the accuracy of everything they attempt to do. This is what Q-CTRL is solving. Q-CTRL’s mission is to make quantum technology useful. This demo showcases how Q-CTRL infrastructure software is delivering business advantage from quantum computing. Q-CTRL builds commercial-ready, quantum control and error reduction products available on the AWS Marketplace and integrated with Amazon Braket.

Summary: In the constantly evolving quantum computing landscape, the Quantuloop Quantum Simulator Suite is emerging as a transformative force within the realm of high performance computing (HPC). Explore how Quantaloop’s suite empowers researchers, engineers, and innovators to unlock the capabilities of quantum computing, all without the inherent complexities typically linked with HPC software setup and configuration. Experience the flexibility of conducting quantum simulations either in the cloud or within your infrastructure, thereby augmenting security, control, and the pace of your research and development endeavors.

Summary: SoftServe combines cutting-edge advisory and software solutions. Their comprehensive R&DaaS includes fundamental research, hardware assessment, cloud proficiency, and talent development, allowing them to empower enterprises and software companies to rediscover differentiation, expedite solution development, and remain competitive in today’s marketplace, regardless of where you are on your journey. In this demo, explore SoftServe’s quantum industrialization journey, from pinpointing use cases to crafting solutions and seamless integration while benefiting from the flexibility and availability of AWS Cloud infrastructure. Then, explore widely applied combinatorial optimization problems. Additionally, learn about addressing demanding optimization problems through traditional high performance computing techniques, such as utilizing Monte Carlo methods for multi-GPU optimization and agent-based modeling for GPU-accelerated scheduling.

Summary: With quantum computing increasingly moving toward practical applications, the need for user-friendly and highly integrated platforms has never been greater. This session presents how the powerful integration between Classiq and Amazon Braket streamlines the user experience, from quantum circuit design to implementation. Learn how this integration eliminates the complexities of using and integrating quantum computers, allowing you to focus on solving real-world problems.

Summary: Companies and talent alike are catching on to quantum computing. Companies are racing to build quantum computers with the most qubits, and people, from business leaders to students and job seekers, are looking to join the industry. However, a challenge is that today’s quantum computers are noisy and prone to error, which affects the accuracy of everything they attempt to do. This is what Q-CTRL is solving. Q-CTRL’s mission is to make quantum technology useful. This demo showcases how Q-CTRL infrastructure software is delivering business advantage from quantum computing. Q-CTRL builds commercial-ready, quantum control and error reduction products available on the AWS Marketplace and integrated with Amazon Braket.

Summary: SoftServe combines cutting-edge advisory and software solutions. Their comprehensive R&DaaS includes fundamental research, hardware assessment, cloud proficiency, and talent development, allowing them to empower enterprises and software companies to rediscover differentiation, expedite solution development, and remain competitive in today’s marketplace, regardless of where you are on your journey. In this demo, explore SoftServe’s quantum industrialization journey, from pinpointing use cases to crafting solutions and seamless integration while benefiting from the flexibility and availability of AWS Cloud infrastructure. Then, explore widely applied combinatorial optimization problems. Additionally, learn about addressing demanding optimization problems through traditional high performance computing techniques, such as utilizing Monte Carlo methods for multi-GPU optimization and agent-based modeling for GPU-accelerated scheduling.

Summary: During this demo, discover IonQ’s offerings tailored for Amazon Braket customers. Delve into IonQ’s roadmap and upcoming architectures, Forte and Tempo. Explore new features like debiasing and sharpening, and see a brief hands-on demo of initiating Amazon Braket with IonQ’s QPU backends.

Summary: BlueQubit is a software infrastructure company for running hybrid compute jobs on QPU/GPU/CPU. Learn about their fast, zero-setup GPU simulators.

Summary: Deloitte used QuEra’s neutral atom system to classify manufacturing images at their Smart Factory in Wichita, Kansas. In this demo, see how Deloitte took a real-world business problem, solved it with cutting-edge quantum AI techniques (quantum reservoir computing), and productionized it using a production-ready dashboard. Deloitte compared QuEra’s implementation of quantum reservoir computing against classical methodologies and found the results exceeded the method currently being used. Learn how Deloitte is working to productionize quantum AI algorithms as part of The Smart Factory quality assurance efforts and are working with the team to find the best methods for performing these tasks in a manufacturing facility.

Summary: With quantum computing increasingly moving toward practical applications, the need for user-friendly and highly integrated platforms has never been greater. This session presents how the powerful integration between Classiq and Amazon Braket streamlines the user experience, from quantum circuit design to implementation. Learn how this integration eliminates the complexities of using and integrating quantum computers, allowing you to focus on solving real-world problems.

Summary: Join us for a demo to help demystify quantum programming. Explore the Quantum Approximate Optimization Algorithm (QAOA) service, which simplifies quantum computing for all skill levels. See the practical application of the QAOA service on AWS via Rigetti hardware, showcasing Strangeworks’s latest cargo loading problem. Whether you’re a quantum novice or an experienced coder, gain insights into quantum computing’s potential. Discover how Strangeworks’s intuitive platform is reshaping quantum accessibility, making it easier and more efficient to harness quantum power. Start your quantum journey with Strangeworks and witness the transformative possibilities of quantum computing.

Summary: This demo introduces QuEra and Bloqade, an SDK and emulator for neutral-atom quantum computing. See its success in adoption, some of its features relevant for high performance computing (such as multi-threading and GPU), and its general functionality and pipeline for operating real QPUs.

Summary: The MATLAB Support Package for Quantum Computing lets you build, simulate, and run quantum algorithms on real quantum hardware through Amazon Braket. In this demo, explore how you can use the support package within the MATLAB environment to 1/ build circuits and implement quantum algorithms using a variety of built-in and customizable composite gates; 2/ verify the implementations with local simulators in MATLAB or simulators hosted on Amazon Braket; 3/ run the algorithms on quantum computers using Amazon Braket; and 4/ visualize, analyze, and plot results. See how the support package allows you to prototype algorithms to accelerate optimization, scenario simulation, and ML, in addition to helping you solve currently unsolvable problems in chemistry and material simulations.

Summary: In the constantly evolving quantum computing landscape, the Quantuloop Quantum Simulator Suite is emerging as a transformative force within the realm of high performance computing (HPC). Explore how Quantaloop’s suite empowers researchers, engineers, and innovators to unlock the capabilities of quantum computing, all without the inherent complexities typically linked with HPC software setup and configuration. Experience the flexibility of conducting quantum simulations either in the cloud or within your infrastructure, thereby augmenting security, control, and the pace of your research and development endeavors.

Summary: Amazon Braket helps organizations get access to quantum computing hardware and simulators so they can speed up their scientific research and software development for quantum computing. This session shows short tutorials and shares how to run quantum circuits using real gate-based devices and simulators. Learn how to run your first quantum AI algorithm using Amazon Braket Hybrid Jobs. Along the way, explore Amazon Braket’s features, see examples from the AWS quantum algorithm library, and get your questions answered.

Summary: In the constantly evolving quantum computing landscape, the Quantuloop Quantum Simulator Suite is emerging as a transformative force within the realm of high performance computing (HPC). Explore how Quantaloop’s suite empowers researchers, engineers, and innovators to unlock the capabilities of quantum computing, all without the inherent complexities typically linked with HPC software setup and configuration. Experience the flexibility of conducting quantum simulations either in the cloud or within your infrastructure, thereby augmenting security, control, and the pace of your research and development endeavors.

Summary: This demo introduces QuEra and Bloqade, an SDK and emulator for neutral-atom quantum computing. See its success in adoption, some of its features relevant for high performance computing (such as multi-threading and GPU), and its general functionality and pipeline for operating real QPUs.

Summary: With quantum computing increasingly moving toward practical applications, the need for user-friendly and highly integrated platforms has never been greater. This session presents how the powerful integration between Classiq and Amazon Braket streamlines the user experience, from quantum circuit design to implementation. Learn how this integration eliminates the complexities of using and integrating quantum computers, allowing you to focus on solving real-world problems.

Summary: Join us for a demo to help demystify quantum programming. Explore the Quantum Approximate Optimization Algorithm (QAOA) service, which simplifies quantum computing for all skill levels. See the practical application of the QAOA service on AWS via Rigetti hardware, showcasing Strangeworks’s latest cargo loading problem. Whether you’re a quantum novice or an experienced coder, gain insights into quantum computing’s potential. Discover how Strangeworks’s intuitive platform is reshaping quantum accessibility, making it easier and more efficient to harness quantum power. Start your quantum journey with Strangeworks and witness the transformative possibilities of quantum computing.

Summary: BlueQubit is a software infrastructure company for running hybrid compute jobs on QPU/GPU/CPU. Learn about their fast, zero-setup GPU simulators.

Summary: Companies and talent alike are catching on to quantum computing. Companies are racing to build quantum computers with the most qubits, and people, from business leaders to students and job seekers, are looking to join the industry. However, a challenge is that today’s quantum computers are noisy and prone to error, which affects the accuracy of everything they attempt to do. This is what Q-CTRL is solving. Q-CTRL’s mission is to make quantum technology useful. This demo showcases how Q-CTRL infrastructure software is delivering business advantage from quantum computing. Q-CTRL builds commercial-ready, quantum control and error reduction products available on the AWS Marketplace and integrated with Amazon Braket.

Summary: Amazon Braket helps organizations get access to quantum computing hardware and simulators so they can speed up their scientific research and software development for quantum computing. This session shows short tutorials and shares how to run quantum circuits using real gate-based devices and simulators. Learn how to run your first quantum AI algorithm using Amazon Braket Hybrid Jobs. Along the way, explore Amazon Braket’s features, see examples from the AWS quantum algorithm library, and get your questions answered.

Summary: RONIN is a one-stop shop for self-service cloud management. It helps businesses migrate critical and expensive business functions, such as storage management, desktop virtualization, and HPC footprint, to a more flexible, secure, and efficient AWS architecture. RONIN can assist you in scaling up to a fleet of HPC supercomputers to meet the demands of your business, and most importantly, scale them back down when you no longer require the compute power that AWS and RONIN provide. In this demonstration, learn how RONIN can help you stay compliant with regulations, automate tasks, and provide a single pane of glass experience for a better view of your operations.

Summary: At the intersection of science and technology, Clovertex is a systems integrator specializing in architecting, building, automating, and managing scientific applications, databases, data lakes, and workflows using cloud computing technologies. As a scientific applications service provider for the pharmaceutical domain, Clovertex offers a range of capabilities tailored to meet the specific needs of the industry. In this demonstration, explore customer examples where businesses have delivered new capabilities for scientific data analysis (including cryoEM) and learn how the synergy between cutting-edge science and cloud technology can drive innovation and accelerate progress in the field of structural biology.

Summary: At the intersection of science and technology, Clovertex is a systems integrator specializing in architecting, building, automating, and managing scientific applications, databases, data lakes, and workflows using cloud computing technologies. As a scientific applications service provider for the pharmaceutical domain, Clovertex offers a range of capabilities tailored to meet the specific needs of the industry. In this demonstration, explore customer examples where businesses have delivered new capabilities for scientific data analysis (including cryoEM) and learn how the synergy between cutting-edge science and cloud technology can drive innovation and accelerate progress in the field of structural biology.

Summary: RONIN is a one-stop shop for self-service cloud management. It helps businesses migrate critical and expensive business functions, such as storage management, desktop virtualization, and HPC footprint, to a more flexible, secure, and efficient AWS architecture. RONIN can assist you in scaling up to a fleet of HPC supercomputers to meet the demands of your business, and most importantly, scale them back down when you no longer require the compute power that AWS and RONIN provide. In this demonstration, learn how RONIN can help you stay compliant with regulations, automate tasks, and provide a single pane of glass experience for a better view of your operations.

Summary: At the intersection of science and technology, Clovertex is a systems integrator specializing in architecting, building, automating, and managing scientific applications, databases, data lakes, and workflows using cloud computing technologies. As a scientific applications service provider for the pharmaceutical domain, Clovertex offers a range of capabilities tailored to meet the specific needs of the industry. In this demonstration, explore customer examples where businesses have delivered new capabilities for scientific data analysis (including cryoEM) and learn how the synergy between cutting-edge science and cloud technology can drive innovation and accelerate progress in the field of structural biology.

Summary: RONIN is a one-stop shop for self-service cloud management. It helps businesses migrate critical and expensive business functions, such as storage management, desktop virtualization, and HPC footprint, to a more flexible, secure, and efficient AWS architecture. RONIN can assist you in scaling up to a fleet of HPC supercomputers to meet the demands of your business, and most importantly, scale them back down when you no longer require the compute power that AWS and RONIN provide. In this demonstration, learn how RONIN can help you stay compliant with regulations, automate tasks, and provide a single pane of glass experience for a better view of your operations.

Summary: https://sc23.supercomputing.org/presentation/?id=bof126&sess=sess339 Rm 205-207

Summary: Agenda: https://sc23.supercomputing.org/presentation/?id=bof120&sess=sess350 Rm 407

Summary: https://sc23.supercomputing.org/presentation/?id=bof222&sess=sess342 Rm 205-207

Summary: https://sc23.supercomputing.org/presentation/?id=bof113&sess=sess362 Rm 405-406-407

Summary: https://sc23.supercomputing.org/presentation/?id=bof110&sess=sess385 Rm 605

Summary: https://sc23.supercomputing.org/presentation/?id=bof212&sess=sess354 Rm 401-402

Summary: https://sc23.supercomputing.org/presentation/?id=bof196&sess=sess346 205-207

Summary: Developers from Arm, AWS, and NVIDIA Rm 407

Summary: Quantum computing specialists from the Amazon Braket team Rm 406

Summary: https://sc23.supercomputing.org/presentation/?id=tut144&sess=sess238 Rm 205

Summary: https://sc23.supercomputing.org/session/?sess=sess446 Rm 603

Summary: HPC systems and workloads have their own unique set of security challenges. Running these workloads on AWS can help you address these challenges in new ways and with this freedom comes the ability to choose different solutions based on your organization’s unique needs. In this session, walk through a traditional HPC security scenario and discover different approaches you can take to address these challenges. Find out how the AWS secure-by-design architecture helps take care of common security challenges, and learn best practices for building secure HPC environments on AWS. When you’re done, you’ll understand how your cloud infrastructure can be even more secure than your data centers.

Summary: HPC systems and workloads have their own unique set of security challenges. Running these workloads on AWS can help you address these challenges in new ways and with this freedom comes the ability to choose different solutions based on your organization’s unique needs. In this session, walk through a traditional HPC security scenario and discover different approaches you can take to address these challenges. Find out how the AWS secure-by-design architecture helps take care of common security challenges, and learn best practices for building secure HPC environments on AWS. When you’re done, you’ll understand how your cloud infrastructure can be even more secure than your data centers.

Summary: HPC systems deployed on AWS often rely on various resources like file systems, networking, and directory services. Despite AWS ParallelCluster’s automation capabilities, setting up and integrating these dependencies can become complex if you have a lot of custom requirements. In this session, walk through how the new HPC Recipes for AWS library simplifies this process. Then, learn how to create a multi-user environment, configure shared storage, set up a budget, and deploy a benchmarking cluster with just a few clicks in the AWS Management Console. Finally, discover how to combine, modify, and reuse these recipes to meet your particular needs—without needing to be an AWS expert.

Summary: HPC systems deployed on AWS often rely on various resources like file systems, networking, and directory services. Despite AWS ParallelCluster’s automation capabilities, setting up and integrating these dependencies can become complex if you have a lot of custom requirements. In this session, walk through how the new HPC Recipes for AWS library simplifies this process. Then, learn how to create a multi-user environment, configure shared storage, set up a budget, and deploy a benchmarking cluster with just a few clicks in the AWS Management Console. Finally, discover how to combine, modify, and reuse these recipes to meet your particular needs—without needing to be an AWS expert.