AWS HPC Blog

Category: Amazon EC2

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.

GROMACS performance on Amazon EC2 with Intel Ice Lake processors

We recently launched two new Amazon EC2 instance families based on Intel’s Ice Lake – the C6i and M6i. These instances provide higher core counts and take advantage of generational performance improvements on Intel’s Xeon scalable processor family architectures. In this post we show how GROMACS performs on these new instance families. We use similar methodologies as for previous posts where we characterized price-performance for CPU-only and GPU instances (Part 1, Part 2, Part 3), providing instance recommendations for different workload sizes.

Coming soon: dedicated HPC instances and hybrid functionality

This year, we’ve launched a lot of new capabilities for HPC customers, making AWS the best place for the length and breadth of their workflows. EFA went mainstream and is now available in sixteen instance families for fast fabric capabilities for scaling MPI and NCCL codes. We’ve written deep-dive studies to explore and explain the optimizations that will drive your workloads faster in the cloud than elsewhere. We released a major new version of AWS ParallelCluster with its own API for controlling the cluster lifecycle. AWS Batch became deeply integrated into AWS Step Functions and now supports fair-share scheduling, with multiple levers to control the experience. Today we’re signaling the arrival of a new HPC-dedicated instance family – the Hpc6a – and an enhanced EnginFrame that will bring the best of the cloud and on-premises together in a single interface.

EFA is now mainstream, and that’s a Good Thing

We have recently launched three new Amazon EC2 instances types enabled with Elastic Fabric Adapter (EFA), our network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS. These bring our EFA-enabled count to sixteen different instance families covering a wide range of use cases. EFA is going mainstream and we are just getting started.

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.

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. […]

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 […]

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 […]

Cost-optimization on Spot Instances using checkpoint for Ansys LS-DYNA

A major portion of the costs incurred for running Finite Element Analyses (FEA) workloads on AWS comes from the usage of Amazon EC2 instances. Amazon EC2 Spot Instances offer a cost-effective architectural choice, allowing you to take advantage of unused EC2 capacity for up to a 90% discount compared to On-Demand Instance prices. In this post, we describe how you 0can run fault-tolerant FEA workloads on Spot Instances using Ansys LS-DYNA’s checkpointing and auto-restart utility.

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.