Best Practices for Running Computational Fluid Dynamics (CFD) Workloads on AWS

By Amazon Web Services

July 8, 2020

The scalable nature and variable demand of CFD workloads makes them well-suited for a cloud computing environment. Many of the AWS instance types, such as the compute family instance types, are designed to include support for this type of workload. AWS has network options that support extreme scalability and short turn-around time as necessary.

Fluid dynamics is the study of the motion of fluids, usually in the presence of an object. Typical fluid flows of interest to engineers and scientist include: flow in pipes, through engines, and around objects, such as buildings, automobiles, and airplanes. Computational fluid dynamics (CFD) is the study of these flows through computer simulation and modeling. CFD involves the solution of conservation equations (mass, momentum, energy, and others) in a finite domain.

A typical CFD simulation involves the following four steps.

  1. Define the Geometry

In some cases, this step is simple, such as modeling flow in a duct. In other cases, this step involves complex components and moving parts, such as modeling a gas turbine engine. For many cases, the geometry creation is extremely time-consuming. The geometry step is graphics intensive and requires a capable graphics workstation, preferably with a Graphics Processing Unit (GPU). Often, the geometry is provided by a designer, but the CFD engineer must “clean” the geometry for input into the flow solver.

  1. Generate the Mesh or Grid

Mesh generation is a critical step because computational accuracy is dependent on the size, cell location, and skewness of the cells. Mesh generation can be iterative with the solution, where fixes to the mesh are driven by an understanding of flow features and gradients in the solution. Meshing is frequently an interactive process and its elliptical nature generally requires a substantial amount of memory. Like geometry definition, generating a single mesh can take hours, days, weeks, and sometimes months.

  1. Solve for the solution

For some solutions, tens of thousands of cores (processors) run over weeks to achieve a solution. Conversely, some jobs may run in just minutes when scaled out appropriately. There are choices for model equations depending on the desired solution fidelity. The Navier-Stokes equations form the basis of the solved equations for most fluid calculations. The addition of chemical reactions, liquid and gas multi-phase flows, and many other physical properties create increasing complexity. For example, increasing the fidelity of turbulence modeling is achieved with various approximate equation sets such as Reynolds Average Navier-Stokes (RANS), Large Eddy Simulation (LES), Delayed Detached Eddy Simulation (DDES), and Direct Numerical Simulation (DNS). These models typically do not change the fundamental computational characteristics or scaling of the simulation.

  1. Post process through visualization

Examples of this step include the creation of images, video, and post processing of flow fields, such as creating total forces and moments. Similar to the geometry and mesh steps, this can be graphics and memory intensive CFD workloads that typically scale well on the cloud. Most codes rely on domain decomposition to distribute portions of the calculation to the compute nodes. A case can be run highly parallel to receive results in minutes, or large numbers of cases can run simultaneously as efficiently and cheaply as possible to allow the timely completion of all cases.

Why CFD on AWS?

AWS is a great place to run CFD cases. CFD workloads are typically MPI-based, tightly coupled workloads relying on a large number of cores across many nodes. Many of the AWS instance types, such as the compute family instance types, are designed to include support for this type of workload. AWS has network options that support extreme scalability and short turn-around time as necessary. Small CFD cases can be run on a single node, with a large number of cores, and do not require the use of multiple instances.

 

CFD workloads typically scale well on the cloud. Most codes rely on domain decomposition to distribute portions of the calculation to the compute nodes. A case can be run highly parallel to receive results in minutes, or large numbers of cases can run simultaneously as efficiently and cheaply as possible to allow the timely completion of all cases.

The cloud offers a quick way to deploy and turn around CFD w

orkloads at any scale without the need to own your own infrastructure. You can run jobs that once were in the realm of national labs or large industry. In just an hour or two, you can deploy CFD software, upload input files, launch compute nodes, and complete jobs on a large number of cores. When your job completes, results can be visualized and downloaded, and then all resources can be terminated – allowing you to only pay for what you use. If preferred, your results can be securely archived in cloud storage. Due to cloud scalability, you have the option to run multiple cases simultaneously with a dedicated cluster for each case.

The cloud accommodates the variable demand of CFD. Often, there is a need to run a large number of cases as quickly as possible. Situations can require a sudden burst of tens, to hundreds, to thousands of calculations immediately, and then perhaps noruns until the next cycle. The need to run a large number of cases could be for a preliminary design review, or perhaps a sweep of cases for the creation of a solution database. On the cloud, the cost is the same to run many jobs simultaneously, in parallel, as it is to run them serially, so you can get your data more quickly and at no extra cost. The cost savings in engineering time is an often forgotten part of cost analysis. Running in parallel can be an ideal solution for design optimization. On AWS, you can launch the cases you need when your case is ready, without waiting in a queue for available cluster resources.

Cloud computing is a strong choice for other CFD steps. You can easily launch a GPU instance, a high-memory instance, or a cluster of high-memory instances, to handle the geometry, meshing, and post-processing. With remote visualization software available to handle the display, you can manage the GPU instance running your post-processing visualization from any screen (laptop, desktop, web browser) as though you were working on a large workstation.

Read the full white paper to learn about best practices for running computational fluid dynamics (CFD) workloads on AWS and quick start tools.

Get started with running your CAE/CFD workloads now – fill the form and get a $100 AWS credit!

 

 

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 industy updates delivered to you every week!

Why HPC Storage Matters More Now Than Ever: Analyst Q&A

September 17, 2021

With soaring data volumes and insatiable computing driving nearly every facet of economic, social and scientific progress, data storage is seizing the spotlight. Hyperion Research analyst and noted storage expert Mark No Read more…

GigaIO Gets $14.7M in Series B Funding to Expand Its Composable Fabric Technology to Customers

September 16, 2021

Just before the COVID-19 pandemic began in March 2020, GigaIO introduced its Universal Composable Fabric technology, which allows enterprises to bring together any HPC and AI resources and integrate them with networking, Read more…

What’s New in HPC Research: Solar Power, ExaWorks, Optane & More

September 16, 2021

In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

AI Hardware Summit: Panel on Memory Looks Forward

September 15, 2021

What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…

AWS Solution Channel

Supporting Climate Model Simulations to Accelerate Climate Science

The Amazon Sustainability Data Initiative (ASDI), AWS is donating cloud resources, technical support, and access to scalable infrastructure and fast networking providing high performance computing (HPC) solutions to support simulations of near-term climate using the National Center for Atmospheric Research (NCAR) Community Earth System Model Version 2 (CESM2) and its Whole Atmosphere Community Climate Model (WACCM). Read more…

ECMWF Opens Bologna Datacenter in Preparation for Atos Supercomputer

September 14, 2021

In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) – a juggernaut in the weather forecasting scene – signed a four-year, $89-million contract with European tech firm Atos to quintuple its supercomputing capacity. With the deal approaching the two-year mark, ECMWF... Read more…

Why HPC Storage Matters More Now Than Ever: Analyst Q&A

September 17, 2021

With soaring data volumes and insatiable computing driving nearly every facet of economic, social and scientific progress, data storage is seizing the spotlight Read more…

Cerebras Brings Its Wafer-Scale Engine AI System to the Cloud

September 16, 2021

Five months ago, when Cerebras Systems debuted its second-generation wafer-scale silicon system (CS-2), co-founder and CEO Andrew Feldman hinted of the company’s coming cloud plans, and now those plans have come to fruition. Today, Cerebras and Cirrascale Cloud Services are launching... Read more…

AI Hardware Summit: Panel on Memory Looks Forward

September 15, 2021

What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…

ECMWF Opens Bologna Datacenter in Preparation for Atos Supercomputer

September 14, 2021

In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) – a juggernaut in the weather forecasting scene – signed a four-year, $89-million contract with European tech firm Atos to quintuple its supercomputing capacity. With the deal approaching the two-year mark, ECMWF... Read more…

Quantum Computer Market Headed to $830M in 2024

September 13, 2021

What is one to make of the quantum computing market? Energized (lots of funding) but still chaotic and advancing in unpredictable ways (e.g. competing qubit tec Read more…

Amazon, NCAR, SilverLining Team for Unprecedented Cloud Climate Simulations

September 10, 2021

Earth’s climate is, to put it mildly, not in a good place. In the wake of a damning report from the Intergovernmental Panel on Climate Change (IPCC), scientis Read more…

After Roadblocks and Renewals, EuroHPC Targets a Bigger, Quantum Future

September 9, 2021

The EuroHPC Joint Undertaking (JU) was formalized in 2018, beginning a new era of European supercomputing that began to bear fruit this year with the launch of several of the first EuroHPC systems. The undertaking, however, has not been without its speed bumps, and the Union faces an uphill... Read more…

How Argonne Is Preparing for Exascale in 2022

September 8, 2021

Additional details came to light on Argonne National Laboratory’s preparation for the 2022 Aurora exascale-class supercomputer, during the HPC User Forum, held virtually this week on account of pandemic. Exascale Computing Project director Doug Kothe reviewed some of the 'early exascale hardware' at Argonne, Oak Ridge and NERSC (Perlmutter), while Ti Leggett, Deputy Project Director & Deputy Director... Read more…

Ahead of ‘Dojo,’ Tesla Reveals Its Massive Precursor Supercomputer

June 22, 2021

In spring 2019, Tesla made cryptic reference to a project called Dojo, a “super-powerful training computer” for video data processing. Then, in summer 2020, Tesla CEO Elon Musk tweeted: “Tesla is developing a [neural network] training computer called Dojo to process truly vast amounts of video data. It’s a beast! … A truly useful exaflop at de facto FP32.” Read more…

Berkeley Lab Debuts Perlmutter, World’s Fastest AI Supercomputer

May 27, 2021

A ribbon-cutting ceremony held virtually at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) today marked the official launch of Perlmutter – aka NERSC-9 – the GPU-accelerated supercomputer built by HPE in partnership with Nvidia and AMD. Read more…

Esperanto, Silicon in Hand, Champions the Efficiency of Its 1,092-Core RISC-V Chip

August 27, 2021

Esperanto Technologies made waves last December when it announced ET-SoC-1, a new RISC-V-based chip aimed at machine learning that packed nearly 1,100 cores onto a package small enough to fit six times over on a single PCIe card. Now, Esperanto is back, silicon in-hand and taking aim... Read more…

Enter Dojo: Tesla Reveals Design for Modular Supercomputer & D1 Chip

August 20, 2021

Two months ago, Tesla revealed a massive GPU cluster that it said was “roughly the number five supercomputer in the world,” and which was just a precursor to Tesla’s real supercomputing moonshot: the long-rumored, little-detailed Dojo system. “We’ve been scaling our neural network training compute dramatically over the last few years,” said Milan Kovac, Tesla’s director of autopilot engineering. Read more…

CentOS Replacement Rocky Linux Is Now in GA and Under Independent Control

June 21, 2021

The Rocky Enterprise Software Foundation (RESF) is announcing the general availability of Rocky Linux, release 8.4, designed as a drop-in replacement for the soon-to-be discontinued CentOS. The GA release is launching six-and-a-half months after Red Hat deprecated its support for the widely popular, free CentOS server operating system. The Rocky Linux development effort... Read more…

Google Launches TPU v4 AI Chips

May 20, 2021

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I Read more…

Intel Completes LLVM Adoption; Will End Updates to Classic C/C++ Compilers in Future

August 10, 2021

Intel reported in a blog this week that its adoption of the open source LLVM architecture for Intel’s C/C++ compiler is complete. The transition is part of In Read more…

AMD-Xilinx Deal Gains UK, EU Approvals — China’s Decision Still Pending

July 1, 2021

AMD’s planned acquisition of FPGA maker Xilinx is now in the hands of Chinese regulators after needed antitrust approvals for the $35 billion deal were receiv Read more…

Leading Solution Providers

Contributors

Hot Chips: Here Come the DPUs and IPUs from Arm, Nvidia and Intel

August 25, 2021

The emergence of data processing units (DPU) and infrastructure processing units (IPU) as potentially important pieces in cloud and datacenter architectures was Read more…

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

HPE Wins $2B GreenLake HPC-as-a-Service Deal with NSA

September 1, 2021

In the heated, oft-contentious, government IT space, HPE has won a massive $2 billion contract to provide HPC and AI services to the United States’ National Security Agency (NSA). Following on the heels of the now-canceled $10 billion JEDI contract (reissued as JWCC) and a $10 billion... Read more…

Quantum Roundup: IBM, Rigetti, Phasecraft, Oxford QC, China, and More

July 13, 2021

IBM yesterday announced a proof for a quantum ML algorithm. A week ago, it unveiled a new topology for its quantum processors. Last Friday, the Technical Univer Read more…

Intel Launches 10nm ‘Ice Lake’ Datacenter CPU with Up to 40 Cores

April 6, 2021

The wait is over. Today Intel officially launched its 10nm datacenter CPU, the third-generation Intel Xeon Scalable processor, codenamed Ice Lake. With up to 40 Read more…

Frontier to Meet 20MW Exascale Power Target Set by DARPA in 2008

July 14, 2021

After more than a decade of planning, the United States’ first exascale computer, Frontier, is set to arrive at Oak Ridge National Laboratory (ORNL) later this year. Crossing this “1,000x” horizon required overcoming four major challenges: power demand, reliability, extreme parallelism and data movement. Read more…

Intel Unveils New Node Names; Sapphire Rapids Is Now an ‘Intel 7’ CPU

July 27, 2021

What's a preeminent chip company to do when its process node technology lags the competition by (roughly) one generation, but outmoded naming conventions make it seem like it's two nodes behind? For Intel, the response was to change how it refers to its nodes with the aim of better reflecting its positioning within the leadership semiconductor manufacturing space. Intel revealed its new node nomenclature, and... Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire