Category: Compute

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.

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.

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.

In this blog post, we’ll walk you through the process of building a successful Cryo-EM benchmarking pilot using AWS ParallelCluster, Amazon FSx for Lustre, and cryoSPARC (from Structura Biotechnology) and explain some of our design decisions along the way.

This blog post explains how to run parallel rendering workloads and produce an animation in a cost and time effective way using AWS Batch and AWS Step Functions. AWS Batch manages the rendering jobs on Amazon Elastic Compute Cloud (Amazon EC2), and AWS Step Functions coordinates the dependencies across the individual steps of the rendering workflow. Additionally, Amazon EC2 Spot instances can be used to reduce compute costs by up to 90% compared to On-Demand prices.

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.

In this blog post, we help you understand the AWS Batch job termination process and how you may take actions to gracefully terminate a job by capturing SIGTERM signal inside the application. It provides you with an efficient way to exit your Batch jobs. You also get to know about how job timeouts occur, and how the retry operation works with both traditional AWS Batch jobs and array jobs.

Ampersand is a data-driven TV advertising technology company that provides aggregated TV audience impression insights and planning on 42 million households, in every media market, across more than 165 networks and apps and in all dayparts (broadcast day segments). The Ampersand Data Science team estimated that building their statistical models would require up to 600,000 physical CPU hours to run, which would not be feasible without using a massively parallel and large-scale architecture in the cloud. AWS Batch enabled Ampersand to compress their time of computation over 500x through massive scaling while optimizing their costs using Amazon EC2 Spot. In this blog post, we will provide an overview of how Ampersand built their TV audience impressions (“impressions”) models at scale on AWS, review the architecture they have been using, and discuss optimizations they conducted to run their workload efficiently on AWS Batch.

In this blog post, we cover how to run GROMACS – a popular open source designed for simulations of proteins, lipids, and nucleic acids – cost effectively by leveraging EC2 Spot Instances within AWS ParallelCluster. We also show how to checkpoint GROMACS to recover gracefully from possible Spot Instance interruptions.

Today we’re excited to announce the availability of a new public Spack Binary Cache. In a collaboration, between AWS, E4S, Kitware, and the Lawrence Livermore National Laboratory (LLNL), Spack users now have access to a public build cache hosted on Amazon S3. The use of this Binary Cache will result in up to 20x faster install times for common Spack packages.