Tag: Storage

Check out this post to learn how Lightmatter leveraged AWS tools like Research and Engineering Studio (RES) and AWS ParallelCluster to meet demanding computational requirements for electronic design.

AWS Parallel Computing Service reinvented HPC in the cloud. Learn how we combined familiar HPC tools with the flexibility of the cloud. And walk through what it is, and why it matters.

You can boost the performance of your compute grids by strategically distributing your binaries. Our experts looked at lots of strategies for fast & efficient compute grid operations – to save you some work.

Interested in getting hands-on experience with cutting-edge HPC tools? Check out this blog post on an upcoming virtual training series from @LLNL and @AWSCloud. Learn emerging technologies from the experts this August.

Want to set up a high-speed shared file system for your #HPC or #AI workloads in under an hour? Learn how with this new blog post.

Don’t sacrifice performance OR ease of use with your HPC storage. Learn how Mountpoint for Amazon S3 combines high throughput and low latency with the simplicity of S3.

Today we’re showing you our community library of HPC Recipes for AWS. It’s a public repo @github that will help you achieve feature-rich, reliable HPC deployments ready to run your workloads no matter where you’re starting from.

AWS ParallelCluster version 3.2 introduces support for two new Amazon FSx filesystem types (NetApp ONTAP and OpenZFS). It also lifts the limit on the number of filesystem mounts you can have on your cluster. We’ll show you how, and help you with the details for getting this going right away.

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.

Large-scale data analysis usually involves some multi-step process where the output of one job acts as the input of subsequent jobs. Customers using AWS Batch for data analysis want a simple and performant storage solution to share with and between jobs. We are excited to announce that customers can now use Amazon Elastic File System (Amazon […]