AWS HPC Blog

Tag: HPC

Running large-scale CFD fire simulations on AWS for Amazon.com

In this blog post, we discuss the AWS solution that Amazon’s construction division used to conduct large-scale CFD fire simulations as part of their Fire Strategy solutions to demonstrate safety and fire mitigation strategies. We outline the five key steps taken that resulted in simulation times that were 15-20x faster than previous on-premises architectures, reducing the time to complete from up to twenty-one days to less than one day.

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Expanded filesystems support in AWS ParallelCluster 3.2

Expanded filesystems support in AWS ParallelCluster 3.2

AWS ParallelCluster version 3.2 introduces support for two new Amazon FSx filesystem types (NetApp ONTAP and OpenZFS). It also lifts the limit on the number of filesystem mounts you can have on your cluster. We’ll show you how, and help you with the details for getting this going right away.

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Memory aware scheduling with Slurm is now available in AWS ParallelCluster

Slurm-based memory-aware scheduling in AWS ParallelCluster 3.2

AWS ParallelCluster version 3.2 now supports memory-aware scheduling in Slurm to give you control over the placement of jobs with specific memory requirements. In this blog post, we’ll show you how it works, and explain why this will be really useful to people with memory-hungry workloads.

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Call for participation: RADIUSS Tutorial Series

Lawrence Livermore National Laboratory (LLNL) and AWS are joining forces to provide a training opportunity for emerging HPC tools and application. RADIUSS (Rapid Application Development via an Institutional Universal Software Stack) is a broad suite of open-source software projects originating from LLNL. Together we are hosting a tutorial series to give attendees hands-on experience with these cutting-edge technologies. Find out how to participate in these events in this blog post.

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Analyzing Genomic Data using Amazon Genomics CLI and Amazon SageMaker

In this blog post, we demonstrate how to leverage the AWS Genomics Command line and Amazon SageMaker to analyze large-scale exome sequences and derive meaningful insights. We use the bioinformatics workflow manager Nextflow, it’s open source library of pipelines, NF-Core, and AWS Batch.

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Getting Started with NVIDIA Clara Parabricks on AWS Batch using AWS CloudFormation

In this blog post, we’ll show how you can run NVIDIA Parabricks on AWS Batch leveraging AWS CloudFormation templates. Parabricks is a GPU-accelerated tool for secondary genomic analysis. It reduces the runtime of variant calling on a 30x human genome from 30 hours to just 30 minutes, and leverages AWS Batch to provide an interface that scales compute jobs across multiple instances in the cloud.

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Benchmarking NVIDIA Clara Parabricks Somatic Variant Calling Pipeline on AWS

Somatic variants are genetic alterations which are not inherited but acquired during one’s lifespan, for example those that are present in cancer tumors. In this post, we will demonstrate how to perform somatic variant calling from matched tumor and normal genome sequence data, as well as tumor-only whole genome and whole exome datasets using an NVIDIA GPU-accelerated Parabricks pipeline, and compare the results with baseline CPU-based workflows.

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