AWS HPC Blog

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.

Many shared file systems are used in supporting read-intensive applications, like financial backtesting. These applications typically exploit copies of datasets whose authoritative copy resides somewhere else. For small datasets, in-memory databases and caching techniques can yield impressive results. However, low latency flash-based scalable shared file systems can provide both massive IOPs and bandwidth. They’re also easy to adopt because of their use of a file-level abstraction. In this post, I’ll share how to easily create and scale a shared, distributed POSIX compatible file system that performs at local NVMe speeds for files opened read-only.

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.

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 […]

The Financial Services industry makes significant use of high performance computing (HPC) but it tends to be in the form of loosely coupled, embarrassingly parallel workloads to support risk modelling. The infrastructure tends to scale out to meet ever increasing demand as the analyses look at more and finer grained data. At AWS we’ve helped many customers tackle scaling challenges are noticing some common themes. In this post we describe how HPC teams are thinking about how they deliver compute capacity today, and highlight how we see the solutions converging for the future.

Recently, we talked about the advances NICE DCV has made to push pixels from cloud-hosted desktops or applications over the internet even more efficiently than before. Since we published that post on this blog channel, we’ve been asked by several customers whether all this efficient pixel-pushing could lead to outbound data charges moving up on their AWS bill. We decided to try it on your behalf, and share the details with you in this post. The bottom line? The charges are unlikely to be significant unless you’re doing intensive streaming (such as gaming) and other cost optimizations (like AWS Instance Savings Plans) that will have more impact on your bill.

This three-part series of posts cover the price performance characteristics of running GROMACS on Amazon Elastic Compute Cloud (Amazon EC2) GPU instances. Part 1 covered some background no GROMACS and how it utilizes GPUs for acceleration. Part 2 covered the price performance of GROMACS on a particular GPU instance family running on a single instance. […]

This three-part series of posts cover the price performance characteristics of running GROMACS on Amazon Elastic Compute Cloud (Amazon EC2) GPU instances. Part 1 covered some background no GROMACS and how it utilizes GPUs for acceleration. This post (Part 2) covers the price performance of GROMACS on a particular GPU instance family running on a […]

Comparing the performance of real applications across different Amazon Elastic Compute Cloud (Amazon EC2) instance types is the best way we’ve found for finding optimal configurations for HPC applications here at AWS. Previously, we wrote about price-performance optimizations for GROMACS that showed how the GROMACS molecular dynamics simulation runs on single instances, and how it […]

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.