Running GROMACS on GPU instances

By Amazon Web Services

October 21, 2021

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 scales across many instances. In that post, we covered a variety of CPU-only Amazon EC2 instances. In this three-part series of posts, we will be extending that analysis to include instances with GPUs. Similar to last time, we’ll also consider instances with and without Elastic Fabric Adapter (EFA), our high performance network capabilities.

Specifically, the first part will cover some background on GROMACS and how it utilizes GPUs for acceleration; the second part will cover the price performance of GROMACS on GPU instance families running on a single instance; and the third part will cover the price performance of GROMACS running across multiple GPU instances, with and without EFA.

Part 1: How GROMACS utilizes GPUs for acceleration

GROMACS is a molecular dynamics (MD) package designed for simulations of solvated proteins, lipids, and nucleic acids. It is open-source and released under the GNU Lesser General Public License (LGPL). GROMACS runs on CPU and GPU nodes in single-node and multi-node (cluster) configurations. See https://www.gromacs.org/ for more information.

Before we talk about the runs and results, it’s worth briefly diving into what happens in an MD simulation and how GROMACS approaches the challenges associated with it. This will help us to choose optimal hardware for fast and efficient MD simulations.

In Molecular Dynamics, Newton’s equations of motion are integrated for a system of N particles, which could be the atoms of a protein in solution, for instance. The interactions between the particles are described by a potential that is called the force field. It includes bonded interactions that model the chemical bonds, as well as non-bonded, electrostatic (Coulomb) and van der Waals interactions that act between all atoms. From the positions of the atoms and the potential, the interatomic forces are calculated, which are then used to update the velocities and the positions of the atoms. This is done in a loop over many millions of time steps until a desired time span of the dynamics of the system is captured in the form of a trajectory that contains the positions as a function of time.

GROMACS tries to make use of all the hardware (CPUs, GPUs) available on a compute node to maximize the simulation performance. In contrast to scientific codes that run exclusively on CPUs or natively on GPUs, GROMACS uses an offloading approach. That means, the main loop over the time steps that update the position of the particles is run on the CPU, while portions of the interactions are offloaded to the GPU to be efficiently computed there. Since GROMACS version 4.6, the non-bonded interactions, which account for the main part of the calculations needed in an MD step, can be offloaded to GPUs.

A big advantage of this approach is that only the offloaded interactions need a GPU implementation (i.e. require CUDA or OpenCL code), while the main code base with its numerous algorithms that already exist in C++ does not need to be ported to the GPU. Later on, when new functionality is introduced, we still get increased simulation performance from the GPUs even though the new functionality is implemented solely on the CPU.

Another advantage of offloading is that both CPU and GPU FLOPS — floating point operations per second — are put to good use. This leads to very good performance on nodes that have both a powerful CPU and GPU installed. Ideally, CPU and GPU finish their computations at about the same time in the time step, so that no cycles are lost waiting. Luckily, as a result of the Particle Mesh Ewald algorithm used to calculate electrostatic interactions, the computational intensity between the short-range part (calculated on the GPU, if present) and the long-range part (calculated on the CPU) of these interactions can be adjusted. GROMACS does this automatically at the start of each run to balance computations between GPU and CPU to minimize idle times during the time step.

However, the offloading approach requires us to have at least some balance of CPU to GPU capacity. Having only CPUs will work, because all algorithms can run on the CPU, but it doesn’t work the other way around (yet). If we have too little CPU FLOPS for too much GPU FLOPS, GROMACS will try to compensate by shifting as many interactions as possible to the GPU. But with too little CPU compute capacity, the GPU will starve, waiting for the CPU to catch up, and effectively leaving GPU FLOPS unused and leaving performance gains on the table.

Read the full blog to learn more about running GROMACS on NVIDIA GPUs.

Reminder: You can learn a lot from AWS HPC engineers by subscribing to the HPC Tech Short YouTube channel, and following the AWS HPC Blog channel.

 

Return to Solution Channel Homepage
Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire