AWS HPC Blog

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.

In this blog post, we discuss the AWS solution that Amazon’s construction division used to conduct large-scale CFD fire simulations as part of their Fire Strategy solutions to demonstrate safety and fire mitigation strategies. We outline the five key steps taken that resulted in simulation times that were 15-20x faster than previous on-premises architectures, reducing the time to complete from up to twenty-one days to less than one day.

AWS ParallelCluster version 3.2 introduces support for two new Amazon FSx filesystem types (NetApp ONTAP and OpenZFS). It also lifts the limit on the number of filesystem mounts you can have on your cluster. We’ll show you how, and help you with the details for getting this going right away.

AWS ParallelCluster version 3.2 now supports memory-aware scheduling in Slurm to give you control over the placement of jobs with specific memory requirements. In this blog post, we’ll show you how it works, and explain why this will be really useful to people with memory-hungry workloads.