AWS HPC Blog

Category: Best Practices

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.

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Building a Scalable Predictive Modeling Framework in AWS – Part 3

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.

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Building a Scalable Predictive Modeling Framework in AWS – Part 2

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.

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Building a Scalable Predictive Modeling Framework in AWS – Part 1

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.

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Understanding the AWS Batch termination process

In this blog post, we help you understand the AWS Batch job termination process and how you may take actions to gracefully terminate a job by capturing SIGTERM signal inside the application. It provides you with an efficient way to exit your Batch jobs. You also get to know about how job timeouts occur, and how the retry operation works with both traditional AWS Batch jobs and array jobs.

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Getting the best OpenFOAM Performance on AWS

OpenFOAM is one the most widely used Computational Fluid Dynamics (CFD) packages and helps companies in a broad range of sectors (automotive, aerospace, energy, and life-sciences) to conduct research and design new products. In this post, we’ll discuss six practical things you can do as an OpenFOAM user to run your simulations faster and more cost effectively.

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Figure 1: High level architecture of the file system.

Scaling a read-intensive, low-latency file system to 10M+ IOPs

Many shared file systems are used in supporting read-intensive applications, like financial backtesting. These applications typically exploit copies of datasets whose authoritative copy resides somewhere else. For small datasets, in-memory databases and caching techniques can yield impressive results. However, low latency flash-based scalable shared file systems can provide both massive IOPs and bandwidth. They’re also easy to adopt because of their use of a file-level abstraction. In this post, I’ll share how to easily create and scale a shared, distributed POSIX compatible file system that performs at local NVMe speeds for files opened read-only.

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Putting bitrates into perspective

Recently, we talked about the advances NICE DCV has made to push pixels from cloud-hosted desktops or applications over the internet even more efficiently than before. Since we published that post on this blog channel, we’ve been asked by several customers whether all this efficient pixel-pushing could lead to outbound data charges moving up on their AWS bill. We decided to try it on your behalf, and share the details with you in this post. The bottom line? The charges are unlikely to be significant unless you’re doing intensive streaming (such as gaming) and other cost optimizations (like AWS Instance Savings Plans) that will have more impact on your bill.

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