Category: AWS Batch

Figure 6: With three shareIdentifier values and 75% capacity reserved, each identifier has exclusive access to 25% of the capacity.

Introducing fair-share scheduling for AWS Batch

Today we are announcing fair-share scheduling (FSS) for AWS Batch, which provides fine-grain control of the scheduling behavior by using a scheduling policy. With FSS, customers can prevent “unfair” situations caused by strict first-in, first-out scheduling where high priority jobs can’t “jump the queue” without draining other jobs first. You can now balance resource consumption between groups of workloads and have confidence that the shared compute environment is not dominated by a single workload. In this post, we’ll explain how fair-share scheduling works in more detail. You’ll also find a link to a step-by-step workshop at the end of this post, so you can try it out yourself.

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Running 20k simulations in 3 days to accelerate early stage drug discovery with AWS Batch

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 simulations, and pmx, a free-energy calculation package from the Computational Biomolecular Dynamics Group at Max Planck Institute in Germany.

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Using AWS Batch Console Support for Step Functions Workflows

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 customers have used the secondary analysis solution to automate their bioinformatics pipelines in AWS. A common pain point expressed […]

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AWS Batch Dos and Don’ts: Best Practices in a Nutshell

AWS Batch is a service that enables scientists and engineers to run computational workloads at virtually any scale without requiring them to manage a complex architecture. In this blog post, we share a set of best practices and practical guidance devised from our experience working with customers in running and optimizing their computational workloads. The readers will learn how to optimize their costs with Amazon EC2 Spot on AWS Batch, how to troubleshoot their architecture should an issue arise and how to tune their architecture and containers layout to run at scale.

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Scalable and Cost-Effective Batch Processing for ML workloads with AWS Batch and Amazon FSx

Batch processing is a common need across varied machine learning use cases such as video production, financial modeling, drug discovery, or genomic research. The elasticity of the cloud provides efficient ways to scale and simplify batch processing workloads while cutting costs. In this post, you’ll learn a scalable and cost-effective approach to configure AWS Batch Array jobs to process datasets that are stored on Amazon S3 and presented to compute instances with Amazon FSx for Lustre.

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AWS batch loves Amazon EFS - header image

Introducing support for per-job Amazon EFS volumes in AWS Batch

Large-scale data analysis usually involves some multi-step process where the output of one job acts as the input of subsequent jobs. Customers using AWS Batch for data analysis want a simple and performant storage solution to share with and between jobs. We are excited to announce that customers can now use Amazon Elastic File System (Amazon […]

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Welcome to the AWS HPC Blog

This post is written by Deepak Singh, Vice President of Compute Services. At AWS, we love working with customers to solve their toughest challenges. High performance computing (HPC) is one of those challenges that pushes against the boundaries of AWS performance at scale. HPC is also a personal interest of mine, as I came to […]

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