Understanding deal and portfolio risk and capital requirements is a computationally expensive process that requires the execution of multiple financial forecasting models every day and in often in real time. This post describes how it works …
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A key part of the development of quantum hardware and quantum algorithms is simulation using existing classical architectures and HPC techniques. In this blog post, we describe how to perform large-scale quantum circuits simulations using …
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In the AWS Deep Learning Challenge held from January 5, 2022, to March 1, 2022, participants from academia, startups, and enterprise organizations joined to test their skills and train a deep learning model of their choice using Amazon …
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In this blog, we showcase the first version of Open Omics and benchmark three applications that are used in processing NGS data – sequence alignment tools BWA-MEM, minimap2, and single cell ATAC-Seq on Xeon-based Amazon Elastic Compute …
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In this final part of this three-part blog series on building predictive models at scale in AWS, we will use the synthetic dataset and the models generated in the previous post to showcase the model updating and sensitivity analysis …
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In the first part of this three-part blog series, we introduced the aws-do-pm framework for building predictive models at scale in AWS. In this blog, we showcase a sample application for predicting the life of batteries in a fleet of …
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Predictive models have powered the design and analysis of real-world systems such as jet engines, automobiles, and powerplants for decades. These models are used to provide insights on system performance and to run simulations, at a …
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Spack has already been removing the ugly work from building HPC codes, but with the announcement of the Spack Binary Cache at ISC'22, build and deploy times for these complicated applications will drop by 95% or more in most cases.
Greg …
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