Slurm accounting adds flexibility, transparency, and control to operating an #HPC cluster. #AWS #ParallelCluster 3.3.0 can now automatically configure #Slurm accounting whether you are using your own database or Amazon #Aurora.
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.
Building deep learning models for geoscience using MATLAB and NVIDIA GPUs on Amazon EC2 (Part 2 of 2)
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.
Building deep learning models for geoscience using MATLAB and NVIDIA GPUs on Amazon EC2 (Part 1 of 2)
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.
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.
Today we announced the AWS Impact Computing Project at the Harvard Data Science Initiative (HDSI) to identify potential solutions that can improve the lives of humans, other species, and natural ecosystems. Deb Goldfarb describes its goals and our joint vision.