AWS HPC Blog

Category: Best Practices

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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Figure 4: Relative price-to-performance ratio ($USD/ns) while scaling the simulation across single and multi-GPU instances and comparing to CPU (EFA enabled) performance-to-price (baseline CPU perf).

Running GROMACS on GPU instances: multi-node price-performance

This three-part series of posts cover the price performance characteristics of running GROMACS on Amazon Elastic Compute Cloud (Amazon EC2) GPU instances. Part 1 covered some background no GROMACS and how it utilizes GPUs for acceleration. Part 2 covered the price performance of GROMACS on a particular GPU instance family running on a single instance. […]

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Figure 4: Performance scaling as a function of CPU core count increase while number of GPU's remain constant.

Running GROMACS on GPU instances: single-node price-performance

This three-part series of posts cover the price performance characteristics of running GROMACS on Amazon Elastic Compute Cloud (Amazon EC2) GPU instances. Part 1 covered some background no GROMACS and how it utilizes GPUs for acceleration. This post (Part 2) covers the price performance of GROMACS on a particular GPU instance family running on a […]

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Figure 2: Work distribution across CPU and GPU for a single simulation timestep

Running GROMACS on GPU instances

Comparing the performance of real applications across different Amazon Elastic Compute Cloud (Amazon EC2) instance types is the best way we’ve found for finding optimal configurations for HPC applications here at AWS. Previously, we wrote about price-performance optimizations for GROMACS that showed how the GROMACS molecular dynamics simulation runs on single instances, and how it […]

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AWS Batch Dos and Don’ts: Best Practices in a Nutshell

AWS Batch is a service that enables scientists and engineers to run computational workloads at virtually any scale without requiring them to manage a complex architecture. In this blog post, we share a set of best practices and practical guidance devised from our experience working with customers in running and optimizing their computational workloads. The readers will learn how to optimize their costs with Amazon EC2 Spot on AWS Batch, how to troubleshoot their architecture should an issue arise and how to tune their architecture and containers layout to run at scale.

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Bare metal performance with the AWS Nitro System

High Performance Computing (HPC) is known as a domain where applications are well-optimized to get the highest performance possible on a platform. Unsurprisingly, a common question when moving a workload to AWS is what performance difference there may be from an existing on-premises “bare metal” platform. This blog will show the performance differential between “bare metal” instances and instances that use the AWS Nitro hypervisor is negligible for the evaluated HPC workloads.

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Numerical weather prediction on AWS Graviton2

The Weather Research and Forecasting (WRF) model is a numerical weather prediction (NWP) system designed to serve both atmospheric research and operational forecasting needs. With the release of Arm-based AWS Graviton2 Amazon Elastic Compute Cloud (EC2) instances, a common question has been how these instances perform on large-scale NWP workloads. In this blog, we will present results from a standard WRF benchmark simulation and compare across three different instance types.

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GROMACS price-performance optimizations header image

GROMACS price-performance optimizations on AWS

Molecular dynamics (MD) is a simulation method for analyzing the movement and tracing trajectories of atoms and molecules where the dynamics of a system evolve over time. MD simulations are used across various domains such as material sciences, biochemistry, biophysics and are typically used in two broad ways to study a system. The importance of […]

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