In this blog post, we’ll describe an ensemble run of 20K simulations to accelerate the drug discovery process, while also optimizing for run time and cost. We used two popular open-source packages — GROMACS, which does a molecular dynamics …
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Last year, we published the Genomics Secondary Analysis Using AWS Step Functions and AWS Batch solution as a companion solution to the Genomics Data Transfer, Analytics, and Machine Learning Using AWS Services whitepaper. Since then, many …
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This three-part series of posts cover the price performance characteristics of running GROMACS on Amazon Elastic Compute Cloud (Amazon EC2) GPU instances. Part 1 covered some background no GROMACS and how it utilizes GPUs for acceleration. …
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This three-part series of posts cover the price performance characteristics of running GROMACS on Amazon Elastic Compute Cloud (Amazon EC2) GPU instances. Part 1 covered some background no GROMACS and how it utilizes GPUs for acceleration. …
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Comparing the performance of real applications across different Amazon Elastic Compute Cloud (Amazon EC2) instance types is the best way we’ve found for finding optimal configurations for HPC applications here at AWS. Previously, we wrote …
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Computer-aided drug discovery (CADD) has been a key player in lowering the cost and speeding up the timeline for drug development. CADD uses high performance computing (HPC) resources to virtually screen databases with billions of …
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Running HPC workloads, like computational fluid dynamics (CFD), molecular dynamics, or weather forecasting typically involves a lot of moving parts. You need a hundreds or thousands of compute cores, a job scheduler for keeping them fed, a …
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This post was written by Swapnil Bhatkar, Cloud Engineer, NREL in collaboration with Edward Eng Ph.D. and Micah Rapp Ph.D, both SEMC/NYSBC, and Evan Bollig Ph.D. and Aniket Deshpande, both AWS. Introduction Cryo-electron microscopy …
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