AWS HPC Blog

Category: Customer Solutions

Massively-scaling quantum chemistry to support a circular economy

Massively-scaling quantum chemistry to support a circular economy

As a part of AWS’s “Digital Technologies for a Circular Economy” initiative, we joined forces with Accenture, Intel and Good Chemistry to massively scale quantum chemistry simulations. This is the first and most complex step to discovering new pathways for PFAS destruction for a cleaner world.

Cost-effective and accurate genomics analysis with Sentieon on AWS

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.

Accelerating Genomics Pipelines Using Intel’s Open Omics Acceleration Framework on AWS

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.

Running large-scale CFD fire simulations on AWS for Amazon.com

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.

Bayesian ML Models at Scale with AWS Batch

Ampersand is a data-driven TV advertising technology company that provides aggregated TV audience impression insights and planning on 42 million households, in every media market, across more than 165 networks and apps and in all dayparts (broadcast day segments). The Ampersand Data Science team estimated that building their statistical models would require up to 600,000 physical CPU hours to run, which would not be feasible without using a massively parallel and large-scale architecture in the cloud. AWS Batch enabled Ampersand to compress their time of computation over 500x through massive scaling while optimizing their costs using Amazon EC2 Spot. In this blog post, we will provide an overview of how Ampersand built their TV audience impressions (“impressions”) models at scale on AWS, review the architecture they have been using, and discuss optimizations they conducted to run their workload efficiently on AWS Batch.

Benchmarking NVIDIA Clara Parabricks Somatic Variant Calling Pipeline on AWS

Somatic variants are genetic alterations which are not inherited but acquired during one’s lifespan, for example those that are present in cancer tumors. In this post, we will demonstrate how to perform somatic variant calling from matched tumor and normal genome sequence data, as well as tumor-only whole genome and whole exome datasets using an NVIDIA GPU-accelerated Parabricks pipeline, and compare the results with baseline CPU-based workflows.

AI-based drug discovery with Atomwise and WEKA Data Platform

Drug discovery is an expensive proposition, with a $2.6 billion cost over 10 years and just a 12% success rate. AI promises to significantly improve the success rate by finding small molecule hits for undruggable targets. On the forefront of using AI in drug discovery is Atomwise, with its AtomNet® platform. In this blog, we will lay out the challenges of the drug discovery process, and show how AI/ML startups are solving these challenges using solutions from Atomwise, AWS, and WEKA.

Figure 1: Comparison of simulation performance for the Le Mans test case run with Open MPI and Intel MPI. Intel MPI offers better performance compared to Open MPI.

Simcenter STAR-CCM+ price-performance on AWS

Organizations such as Amazon Prime Air and Joby Aviation use Simcenter STAR-CCM+ for running CFD simulations on AWS so they can reduce product manufacturing cycles and achieve faster times to market. In this post today, we describe the performance and price analysis of running Computational Fluid Dynamics (CFD) simulations using Siemens SimcenterTM STAR-CCM+TM software on AWS HPC clusters.

Figure 2: Identification of redun jobs and grouping them into Array Jobs to run on AWS Batch. (Top) redun represents the workflow as an Expression Graph (top-left), and identifies reductions (red boxes) that are ready to be executed. The redun Scheduler creates a redun Job (J1, J2, J3) for each reduction and dispatches those jobs to Executors based on the task-specific configuration. The Batch Executor allows jobs to accumulate for up to three seconds (default) in order to identify compatible jobs for grouping into an Array Job, which are then submitted to AWS Batch (top-right). (Bottom) As jobs complete in AWS Batch, the success (green) and failure (red) is propagated back to Executors, the Scheduler, and eventually substituted back into the Expression Graph (bottom-left).

Data Science workflows at insitro: how redun uses the advanced service features from AWS Batch and AWS Glue

Matt Rasmussen, VP of Software Engineering at insitro, expands on his first post on redun, insitro’s data science tool for bioinformatics, to describe how redun makes use of advanced AWS features. Specifically, Matt describes how AWS Batch’s Array Jobs is used to support workflows with large fan-out, and how AWS Glue’s DynamicFrame is used to run computationally heterogenous workflows with different back-end needs such as Spark, all in the same workflow definition.