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, …
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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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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 …
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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 …
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Containers are rapidly maturing within the high performance computing (HPC) community and we’re excited to be part of the movement: listening to what customers have to say and feeding this back to both the community and our own product and …
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A customer asked us what is the difference between the CancelJob and TerminateJob API calls in AWS Batch. This post provides an overview of AWS Batch job states, and how these two API calls effect the job requests that you have submitted. …
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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 …
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