AWS Compute Blog

Category: AWS Batch

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How to run 3D interactive applications with NICE DCV in AWS Batch

This post is contributed by Alberto Falzone, Consultant, HPC and Roberto Meda, Senior Consultant, HPC. High Performance Computing (HPC) workflows across industry verticals such as Design and Engineering, Oil and Gas, and Life Sciences often require GPU-based 3D/OpenGL rendering. Setting up drivers and applications for these types of workflows can require significant effort. Similar GPU […]

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application using AWS Batch

Orchestrating an application process with AWS Batch using AWS CloudFormation

This post is written by Sivasubramanian Ramani In many real work applications, you can use custom Docker images with AWS Batch and AWS CloudFormation to execute complex jobs efficiently.   This post provides a file processing implementation using Docker images and Amazon S3, AWS Lambda, Amazon DynamoDB, and AWS Batch. In this scenario, the user […]

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Optimizing for cost, availability and throughput by selecting your AWS Batch allocation strategy

This post is contributed by Steve Kendrex, Senior Technical Product Manager, AWS Batch   Introduction   AWS offers a broad range of instances that are advantageous for batch workloads. The scale and provisioning speed of AWS’ compute instances allow you to get up and running at peak capacity in minutes without paying for downtime. Today, I’m […]

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Leveraging Elastic Fabric Adapter to run HPC and ML Workloads on AWS Batch

Leveraging Elastic Fabric Adapter to run HPC and ML Workloads on AWS Batch  This post is contributed by  Sean Smith, Software Development Engineer II, AWS ParallelCluster & Arya Hezarkhani, Software Development Engineer II, AWS Batch and HPC   On August 2, 2019, AWS Batch announced support for Elastic Fabric Adapter (EFA). This enables you to run highly […]

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Creating an AWS Batch environment for mixed CPU and GPU genomics workflows

This post is courtesy of Lee Pang – AWS Technical Business Development  I recently worked with a customer who needed to process a bunch of raw sequence files (FastQs) into Hi-C format (*.hic), which is used for the structural analysis of DNA/chromatin loops and sequence accessibility. The tooling they were interested in using was the Juicer […]

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GPU workloads on AWS Batch

Contributed by Manuel Manzano Hoss, Cloud Support Engineer I remember playing around with graphics processing units (GPUs) workload examples in 2017 when the Deep Learning on AWS Batch post was published by my colleague Kiuk Chung. He provided an example of how to train a convolutional neural network (CNN), the LeNet architecture, to recognize handwritten digits […]

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Step Functions

Building Simpler Genomics Workflows on AWS Step Functions

This post is courtesy of Ryan Ulaszek, AWS Genomics Partner Solutions Architect and Aaron Friedman, AWS Healthcare and Life Sciences Partner Solutions Architect In 2017, we published a four part blog series on how to build a genomics workflow on AWS. In part 1, we introduced a general architecture highlighting three common layers: job, batch and […]

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Building a tightly coupled molecular dynamics workflow with multi-node parallel jobs in AWS Batch

Contributed by Amr Ragab, HPC Application Consultant, AWS Professional Services and Aswin Damodar, Senior Software Development Engineer, AWS Batch At Supercomputing 2018 in Dallas, TX, AWS announced AWS Batch support for running tightly coupled workloads in a multi-node parallel jobs environment. This AWS Batch feature enables applications that require strong scaling for efficient computational workloads. Some of […]

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