AWS HPC Blog
In this post, we describe how to orchestrate protein folding jobs on AWS Batch. We also compare the performance of OpenFold and AlphaFold on a set of public targets. Finally, we will discuss how to optimize your protein folding costs.
In this post, we will provide an overview of Numerical Weather Prediction (NWP) workloads, and the AWS HPC-optimized services for it. We’ll test three popular NWP codes: WRF, MPAS, and FV3GFS.
AWS service teams continuously improve the underlying infrastructure and operations of managed services, and AWS Batch is no exception. The AWS Batch team recently moved most of their job scheduler fleet to a serverless infrastructure model leveraging AWS Fargate. I had a chance to sit with Devendra Chavan, Senior Software Development Engineer on the AWS Batch team, to discuss the move to AWS Fargate and its impact on the Batch managed scheduler service component.
This post will help you understand the tools available to ease the stress of migrating your cluster (and your users) from SGE to Slurm, which is necessary since the HPC community is no longer supporting SGE’s open-source codebase.
Understanding deal and portfolio risk and capital requirements is a computationally expensive process that requires the execution of multiple financial forecasting models every day and in often in real time. This post describes how it works at RenaissanceRe, one of the world’s leading reinsurance companies.
A key part of the development of quantum hardware and quantum algorithms is simulation using existing classical architectures and HPC techniques. In this blog post, we describe how to perform large-scale quantum circuits simulations using AWS ParallelCluster with QuEST, the Quantum Exact Simulation Toolkit. We demonstrate a simple and rapid deployment of computational resources up to 4,096 compute instances to simulate random quantum circuits with up to 44 qubits. We were able to allocate as many as 4096 EC2 instances of c5.18xlarge to simulate a non-trivial 44 qubit quantum circuit in fewer than 3.5 hours.
In this blog, we showcase the first version of Open Omics and benchmark three applications that are used in processing NGS data – sequence alignment tools BWA-MEM, minimap2, and single cell ATAC-Seq on Xeon-based Amazon Elastic Compute Cloud (Amazon EC2) Instances.
In this final part of this three-part blog series on building predictive models at scale in AWS, we will use the synthetic dataset and the models generated in the previous post to showcase the model updating and sensitivity analysis capabilities of the aws-do-pm framework.
In the first part of this three-part blog series, we introduced the aws-do-pm framework for building predictive models at scale in AWS. In this blog, we showcase a sample application for predicting the life of batteries in a fleet of electric vehicles, using the aws-do-pm framework.
Predictive models have powered the design and analysis of real-world systems such as jet engines, automobiles, and powerplants for decades. These models are used to provide insights on system performance and to run simulations, at a fraction of the cost compared to experiments with physical hardware. In this first post of three, we described the motivation and general architecture of the open-source aws-do-pm framework project for building predictive models at scale in AWS.