Category: High Performance Computing

In this blog post, we benchmark the performance of Sentieon’s DNAseq and DNAscope pipelines using publicly available genomics datasets on AWS. You will gain an understanding of the runtime, cost, and accuracy performance of these germline variant calling pipelines across a wide range of Amazon EC2 instances.

AWS Batch customers often ask for guidance to optimize their architectures and make their workload to scale rapidly. Here we describe an observability solution that provides insights into your AWS Batch architectures and allows you to optimize them for scale and quickly identify potential throughput bottlenecks for jobs and instances.

In this post we recap all the really significant feature released in DCV from 2022 that delighted our customers. Of course, we’re still not done, so expect more in 2023.

With #AWS #ParallelCluster 3.3, you can now easily take advantage of #EC2 On-Demand Capacity Reservations to help ensure your jobs have the capacity they need when they need it. This post describes the new feature and how you can benefit from it.

This is the second of a two-part post.Part 1 discussed the workflow for developing AI models using MATLAB for seismic interpretation. Today, we will discuss the various compute resources leveraged from AWS and NVIDIA for developing the models.

In this blog post, we discuss how geoscientists can use shallow RNN-based algorithms with MATLAB to automatically recognize distinct geologic features in seismic images. We discuss the workflow for developing the AI models using MATLAB for seismic interpretation.  In a second post will introduce the various compute resources leveraged from AWS and NVIDIA for developing the models.

Calling budding bioinformaticians! If you learn through hands-on practicals and walkthroughs, AWS and GIS have developed training and resources to help you increase the scale and productivity of your research using the AWS cloud.

Since launch, EFA has seen continuous improvements in performance. In this post, we talk about our 2nd generation of EFA, which takes another step in improving Machine Learning and High Performance Computing in the Cloud.

In this post we describe the process to launch large, self-supervised training jobs using AWS ParallelCluster and Facebook’s Vision Self-Supervised Learning (VISSL) library.

Pay-as-you-go resources are a compelling but budget-limited researchers performing HPC workloads need help working within the bounds of their grants. In this post, we show how to build a real-time cost guardian for AWS Batch to help enforce those limits.