Tag: HPC

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.

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.

The Danish Meteorological Institute (DMI) is responsible for running atmospheric, climate and ocean models covering the kingdom of Denmark. We worked together with the DMI to port and run a full numerical weather prediction (NWP) cycling dataflow with the Harmonie Numerical Weather Prediction (NWP) model to AWS. You can find a report of the porting and operational experience in the ACCORD community newsletter. In this blog post, we expand on that report to present the initial timing results from running the forecast component of Harmonie model on AWS. We also present these as-is timing results together with as-is timings attained on the supercomputing systems based on Cray XC40 and Intel Xeon based Cray XC50.

A major portion of the costs incurred for running Finite Element Analyses (FEA) workloads on AWS comes from the usage of Amazon EC2 instances. Amazon EC2 Spot Instances offer a cost-effective architectural choice, allowing you to take advantage of unused EC2 capacity for up to a 90% discount compared to On-Demand Instance prices. In this post, we describe how you 0can run fault-tolerant FEA workloads on Spot Instances using Ansys LS-DYNA’s checkpointing and auto-restart utility.

Computer-aided drug discovery (CADD) has been a key player in lowering the cost and speeding up the timeline for drug development. CADD uses high performance computing (HPC) resources to virtually screen databases with billions of molecules. It can speed up the searching of potential drug molecules, and filter out molecules and compounds that are unsuitable. OpenEye Scientific developed Orion®, a cloud-based molecular design platform for CADD. Orion provides computational chemists with virtually unlimited HPC resources. These include data visualization, collaboration, and workflow management tools that help them perform calculations more efficiently. In this post, we describe the Orion architecture on AWS, and it’s capabilities to address the challenges in drug development.