Organizations such as Amazon Prime Air and Joby Aviation use Simcenter STAR-CCM+ for running CFD simulations on AWS so they can reduce product manufacturing cycles and achieve faster times to market. In this post today, we describe the performance and price analysis of running Computational Fluid Dynamics (CFD) simulations using Siemens SimcenterTM STAR-CCM+TM software on AWS HPC clusters.
The AWS ParallelCluster version 3 CLI differs significantly from ParallelCluster version 2. This post provides some guidance on mapping between versions to help you with migrating to ParallelCluster 3. We also summarize new CLI features in ParallelCluster 3 to expose the things you just couldn’t do previously.
OpenFOAM is one the most widely used Computational Fluid Dynamics (CFD) packages and helps companies in a broad range of sectors (automotive, aerospace, energy, and life-sciences) to conduct research and design new products. In this post, we’ll discuss six practical things you can do as an OpenFOAM user to run your simulations faster and more cost effectively.
We worked with our financial services customers to develop an open-source, scalable, cloud-native, high throughput computing solution on AWS — AWS HTC-Grid. HTC-Grid allows you to submit large volumes of short and long running tasks and scale environments dynamically. In this first blog of a two-part series, we describe the structure of HTC-Grid and its objective to provide a configurable blueprint for HPC grid scheduling on the cloud.
This post by Roberto Meda and Salvo Maccarone covers how you can configure NICE EnginFrame to leverage OKTA as an identity service provider to support SAML 2.0 single sign on authentication and several other features like multi-factor verification, API access management and multi-device support.
Today we’re introducing AWS HPC Connector, a new feature in NICE EnginFrame that allows customers to leverage managed HPC resources on AWS. With this release, EnginFrame provides a unified interface for administrators to make hybrid HPC resources available to their users both on-premises and within AWS. In this post, we’ll provide some context around EnginFrame’s typical use cases, and show how you can use AWS HPC Connector to stand up HPC compute resources on AWS.
OMass Therapeutics, a biotechnology company identifying medicines against highly validated target ecosystems, used Yellowdog on AWS to analyze and screen 337 million compounds in 7 hours, a task which would have taken two months using an on-premises HPC cluster. YellowDog, based in Bristol in the UK, ran the drug discovery application on an extremely large, multi-region cluster in AWS with the AWS ‘pay-as-you-go’ pricing model. It provided a central, unified interface to monitor and manage AWS Region selection, compute provisioning, job allocation and execution. The entire workload completed in 65 minutes, enabling scientists to start work on analysis the same day, significantly accelerating the drug discovery process. In this post, we’ll discuss the AWS and YellowDog services we deployed, and the mechanisms used to scale to 3.2m vCPUs using multiple EC2 instance types across multiple regions in 33 minutes, running at a 95% utilization rate.
This year, we’ve launched a lot of new capabilities for HPC customers, making AWS the best place for the length and breadth of their workflows. EFA went mainstream and is now available in sixteen instance families for fast fabric capabilities for scaling MPI and NCCL codes. We’ve written deep-dive studies to explore and explain the optimizations that will drive your workloads faster in the cloud than elsewhere. We released a major new version of AWS ParallelCluster with its own API for controlling the cluster lifecycle. AWS Batch became deeply integrated into AWS Step Functions and now supports fair-share scheduling, with multiple levers to control the experience. Today we’re signaling the arrival of a new HPC-dedicated instance family – the Hpc6a – and an enhanced EnginFrame that will bring the best of the cloud and on-premises together in a single interface.
HPC systems are traditionally access through a Command Line Interface (CLI) where the users submit and manage their computational jobs. Depending on their experience and sophistication, the CLI can be a daunting experience for users not accustomed in using it. Fortunately, the cloud offers many other options for users to submit and manage their computational jobs. In this blog post we will cover how to create a serverless API to interact with an HPC system in the the cloud built with AWS ParallelCluster.
The Slurm Workload Manager by SchedMD is a popular HPC scheduler and is supported by AWS ParallelCluster, an elastic HPC cluster management service offered by AWS. Traditional HPC workflows involve logging into a head node and running shell commands to submit jobs to a scheduler and check job status. Modern distributed systems often use representational […]