Category: Compute

Pay-as-you-go resources are a compelling but budget-limited researchers performing HPC workloads need help working within the bounds of their grants. In this post, we show how to build a real-time cost guardian for AWS Batch to help enforce those limits.

AWS ParallelCluster 3.3.0 now lets you define a list of Amazon EC2 instance types for resourcing a compute queue. This gives you more flexibility to optimize the cost and total time to solution of your HPC jobs, especially when capacity is limited or you’re using Spot Instances.

Today, we discuss AWS batch on Amazon EKS, and the initial motivation and design choices the team made when we developed the service, and some of the challenges to overcome.

In this post, we highlight a little-known configuration option for Slurm on @awscloud ParallelCluster that can reduce costs and increase your iteration speed by preventing idle batch instances from launching when EC2 capacity is limited.

Today we are excited to announce that all 9000+ applications provided by the BioContainers community are available within ECR Public Gallery! You don’t need an AWS account to access these images, but having one allows many more pulls to the internet, and unmetered usage within AWS. If you perform any sort of bioinformatics analysis on AWS, you should check it out!

In this post, we describe how to orchestrate protein folding jobs on AWS Batch. We also compare the performance of OpenFold and AlphaFold on a set of public targets. Finally, we will discuss how to optimize your protein folding costs.

AWS service teams continuously improve the underlying infrastructure and operations of managed services, and AWS Batch is no exception. The AWS Batch team recently moved most of their job scheduler fleet to a serverless infrastructure model leveraging AWS Fargate. I had a chance to sit with Devendra Chavan, Senior Software Development Engineer on the AWS Batch team, to discuss the move to AWS Fargate and its impact on the Batch managed scheduler service component.

This post will help you understand the tools available to ease the stress of migrating your cluster (and your users) from SGE to Slurm, which is necessary since the HPC community is no longer supporting SGE’s open-source codebase.

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 AWS ParallelCluster with QuEST, the Quantum Exact Simulation Toolkit. We demonstrate a simple and rapid deployment of computational resources up to 4,096 compute instances to simulate random quantum circuits with up to 44 qubits. We were able to allocate as many as 4096 EC2 instances of c5.18xlarge to simulate a non-trivial 44 qubit quantum circuit in fewer than 3.5 hours.

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.