Democratization of HPC Part 4: Deep Learning for Fluid Flow Prediction in the Cloud

By Jannik Zuern, Wolfgang Gentzsch, Markus Stoll, Stefan Suwelack, and Joseph Pareti

November 1, 2018

This is the fourth and final article demonstrating the growing acceptance of high-performance computing (HPC) in new user communities and application areas. In this article we present UberCloud use case #211 on Deep Learning for Fluid Flow Prediction in the Advania Data Centers Cloud, for educational purposes, for our wider engineering simulation community. This project is another demonstration of the trend toward easy-to-use application software (in this case OpenFOAM) and the seamless access to HPC cloud resources.


This UberCloud project #211 has been collaboratively performed by Jannik Zuern, master student at the Karlsruhe Institute of Technology (KIT) supported by Renumics GmbH for Automated Computer Aided Engineering in Germany, and cloud resource provider Advania Data Centers in Iceland, with sponsorship from HPE and Intel. OpenFOAM and Renumics AI tools have been packaged into an UberCloud HPC software container.

Solving fluid flow problems using computational fluid dynamics (CFD) is demanding both in terms of computing power and simulation time. Artificial neural networks (ANN) can learn complex dependencies between high-dimensional variables. This ability is exploited in a data-driven approach to CFD that is presented in this case study. An ANN is applied in predicting the fluid flow given only the shape of the object that is to be simulated. The goal of the approach is to apply an ANN to solve fluid flow problems to significantly decrease time-to-solution while preserving much of the accuracy of a traditional CFD solver. Creating a large number of simulation samples is paramount to let the neural network learn the dependencies between simulated design and the flow field around it.

This project between Renumics in Karlsruhe and UberCloud in Sunnyvale was therefore established to explore the benefits of additional cloud computing resources on Advania Data Centers that can be used to create a large amount of simulation samples in parallel in a fraction of the time a desktop computer would need to create them. In this project, we wanted to explore whether the overall accuracy of the neural network can be improved the more samples are being created in the UberCloud HPC/AI container based on Docker Community Edition and OpenFOAM CFD software and then used during the training of the neural network.

Workflow Overview

In order to create the simulation samples automatically, a comprehensive four-step Deep Learning workflow was established, as shown in Figure 1.

Figure 1: Deep Learning workflow

As a first step, random two-dimensional shapes are created. These shapes have to be diverse enough to let the neural network learn the dependencies between different kinds of shapes and their respective surrounding flow fields.

In the second step, the shapes are meshed and added to an OpenFOAM simulation template (Fig. 2).

In the third step, the simulation results are post-processed using the open-source visualization tool ParaView. The flow-fields are resampled on a rectangular regular grid to simplify the information processing by the neural net.

In the fourth and final step, both the simulated design and the flow fields are fed into the input queue of the neural network. After training, the neural network is able to infer a flow field merely from seeing the to-be-simulated design.

Figure 2: Simulation setup. The flow enters the simulation domain through the inlet, flows around the obstacle and leaves the simulation domain through the outlet

The HPC hardware of the Advania Data Centers compute nodes hosting the UberCloud container consisted of 2 x 16 core compute nodes with Intel Xeon CPU E5-2683 v4 @ 2.10 GHz and 250 GB memory per node, while the user’s desktop just had a 2 x 6 core Intel i7-5820K CPU @ 3.30 GHz, and GeForce GTX 1080 (8GB GDDR5X memory) GPU card with 32 GB memory.

Training Results

As a first step, we compared the time it takes to create the samples on the desktop workstation computer with the time it takes to create the same number of samples on UberCloud/Advania. On the desktop computer it took 13h 10min to create these 10,000 samples. In the UberCloud OpenFOAM container in the Advania Data Centers Cloud, it took about 2h 4min to create 10,000 samples, which means that a speedup of 6.37 could be achieved using the UberCloud container.

Figure 3: Performance and speedup of flow simulations with neural network prediction

A total of 70,000 samples were created. We compared the losses and accuracies of the neural network for different training set sizes. In order to determine the loss and the accuracy of the neural network, we first defined “loss of the neural network prediction.” This measure describes the difference between the prediction of the neural network and the fully simulated results. A loss of 0.0 for all samples would mean that every flow velocity field in the dataset is predicted perfectly. Similarly, the level of accuracy that the neural network achieves, had to be described. For details about the ‘loss’ and the ‘level of accuracy’ see the complete case study.

The generated samples are divided into the training and validation datasets. The training- and validation loss for different numbers of training samples was evaluated. The neural net was trained three times from scratch with 1,000, 10,000, and 70,000 training samples respectively. Figure 4 shows the loss after 50,000 training steps:

Figure 4: Loss after 50,000 training steps

The more different samples the neural network processes during the training process the better and faster it is able to infer a flow velocity field from the shape of the simulated object suspended in the fluid. Figure 5 illustrates the difference between the ground truth flow field (left image) and the predicted flow field (right image) for one exemplary simulation sample after 300,000 training steps. The arrow direction indicates the flow direction and the arrow color indicates the flow velocity. Visually, no difference between the two flow fields can be made out.

Figure 5: Exemplary simulated flow field (left image) and predicted flow field (right image) 

Conclusion

We were able to prove a mantra amongst machine learning engineers: The more data the better. We showed that the training of the neural network is substantially faster using a large dataset of samples compared to smaller datasets of samples. Additionally, the proposed metrics for measuring the accuracies of the neural network predictions exhibited higher values for the larger numbers of samples. The overhead of creating high volumes of additional samples can be effectively compensated by the high-performance containerized (based on Docker) computing node provided by UberCloud on the Advania Data Centers Cloud. A speed-up of more than 6 compared to a state-of-the-art desktop workstation allows creating the tens of thousands of samples needed for the neural network training process in a matter of hours instead of days.

In order to train more complex models (e.g., for transient 3D flow models) much more training data will be required. Thus, software platforms for training data generation and management as well as flexible compute infrastructure will become increasingly important.

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!

What’s New in HPC Research: Dark Matter, Arrhythmia, Sustainability & More

February 28, 2020

In this bimonthly 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…

By Oliver Peckham

Microsoft Announces General Availability of AMD-backed Azure HBv2 Instances for HPC

February 27, 2020

Nearly seven months after they were first announced, Microsoft Azure’s HPC-targeted HBv2 virtual machines (VMs) based on AMD second-generation Epyc processors are ready for primetime. The new VMs, which Azure claims of Read more…

By Staff report

Sequoia Decommissioned, Making Room for El Capitan

February 27, 2020

After eight years of service, Sequoia has been felled. Once the most powerful publicly ranked supercomputer in the world, Sequoia – hosted by Lawrence Livermore National Laboratory (LLNL) – has been decommissioned to Read more…

By Oliver Peckham

Quantum Bits: Q-Ctrl, D-Wave Start News Flow on Eve of APS March Meeting

February 27, 2020

The annual trickle of quantum computing news during the lead-up to next week’s APS March Meeting 2020 has begun. Yesterday D-Wave introduced a significant upgrade to its quantum portal and tool suite, Leap2. Today quantum computing start-up Q-Ctrl announced the beta release of its ‘professional-grade’ tool Boulder Opal software... Read more…

By John Russell

Blue Waters Supercomputer Helps Tackle Pandemic Flu Simulations

February 26, 2020

While not the novel coronavirus that is now sweeping across the world, the 2009 H1N1 flu pandemic (pH1N1) infected up to 21 percent of the global population and killed over 200,000 people. Now, a team of researchers from Read more…

By Staff report

AWS Solution Channel

Amazon FSx for Lustre Update: Persistent Storage for Long-Term, High-Performance Workloads

Last year I wrote about Amazon FSx for Lustre and told you how our customers can use it to create pebibyte-scale, highly parallel POSIX-compliant file systems that serve thousands of simultaneous clients driving millions of IOPS (Input/Output Operations per Second) with sub-millisecond latency. Read more…

IBM Accelerated Insights

Intelligent HPC – Keeping Hard Work at Bay(es)

Since the dawn of time, humans have looked for ways to make their lives easier. Over the centuries human ingenuity has given us inventions such as the wheel and simple machines – which help greatly with tasks that would otherwise be extremely laborious. Read more…

Micron Accelerator Bumps Up Memory Bandwidth

February 26, 2020

Deep learning accelerators based on chip architectures coupled with high-bandwidth memory are emerging to enable near real-time processing of machine learning algorithms. Memory chip specialist Micron Technology argues t Read more…

By George Leopold

Quantum Bits: Q-Ctrl, D-Wave Start News Flow on Eve of APS March Meeting

February 27, 2020

The annual trickle of quantum computing news during the lead-up to next week’s APS March Meeting 2020 has begun. Yesterday D-Wave introduced a significant upgrade to its quantum portal and tool suite, Leap2. Today quantum computing start-up Q-Ctrl announced the beta release of its ‘professional-grade’ tool Boulder Opal software... Read more…

By John Russell

Cray to Provide NOAA with Two AMD-Powered Supercomputers

February 24, 2020

The United States’ National Oceanic and Atmospheric Administration (NOAA) last week announced plans for a major refresh of its operational weather forecasting supercomputers, part of a 10-year, $505.2 million program, which will secure two HPE-Cray systems for NOAA’s National Weather Service to be fielded later this year and put into production in early 2022. Read more…

By Tiffany Trader

NOAA Lays Out Aggressive New AI Strategy

February 24, 2020

Roughly coincident with last week’s announcement of a planned tripling of its compute capacity, the National Oceanic and Atmospheric Administration issued an Read more…

By John Russell

New Supercomputer Cooling Method Saves Half-Million Gallons of Water at Sandia National Laboratories

February 24, 2020

A new cooling method for supercomputer systems is picking up steam – literally. After saving millions of gallons of water at a National Renewable Energy Laboratory (NREL) datacenter, this innovative approach, called... Read more…

By Oliver Peckham

University of Stuttgart Inaugurates ‘Hawk’ Supercomputer

February 20, 2020

This week, the new “Hawk” supercomputer was inaugurated in a ceremony at the High-Performance Computing Center of the University of Stuttgart (HLRS). Offici Read more…

By Staff report

US to Triple Its Supercomputing Capacity for Weather and Climate with Two New Crays

February 20, 2020

The blizzard of news around the race for weather and climate supercomputing leadership continues. Just three days after the UK announced a £1.2 billion plan to build the world’s largest weather and climate supercomputer, the U.S. National Oceanic and Atmospheric Administration... Read more…

By Oliver Peckham

Japan’s AIST Benchmarks Intel Optane; Cites Benefit for HPC and AI

February 19, 2020

Last April Intel released its Optane Data Center Persistent Memory Module (DCPMM) – byte addressable nonvolatile memory – to increase main memory capacity a Read more…

By John Russell

UK Announces £1.2 Billion Weather and Climate Supercomputer

February 19, 2020

While the planet is heating up, so is the race for global leadership in weather and climate computing. In a bombshell announcement, the UK government revealed p Read more…

By Oliver Peckham

Julia Programming’s Dramatic Rise in HPC and Elsewhere

January 14, 2020

Back in 2012 a paper by four computer scientists including Alan Edelman of MIT introduced Julia, A Fast Dynamic Language for Technical Computing. At the time, t Read more…

By John Russell

Cray, Fujitsu Both Bringing Fujitsu A64FX-based Supercomputers to Market in 2020

November 12, 2019

The number of top-tier HPC systems makers has shrunk due to a steady march of M&A activity, but there is increased diversity and choice of processing compon Read more…

By Tiffany Trader

SC19: IBM Changes Its HPC-AI Game Plan

November 25, 2019

It’s probably fair to say IBM is known for big bets. Summit supercomputer – a big win. Red Hat acquisition – looking like a big win. OpenPOWER and Power processors – jury’s out? At SC19, long-time IBMer Dave Turek sketched out a different kind of bet for Big Blue – a small ball strategy, if you’ll forgive the baseball analogy... Read more…

By John Russell

Intel Debuts New GPU – Ponte Vecchio – and Outlines Aspirations for oneAPI

November 17, 2019

Intel today revealed a few more details about its forthcoming Xe line of GPUs – the top SKU is named Ponte Vecchio and will be used in Aurora, the first plann Read more…

By John Russell

IBM Unveils Latest Achievements in AI Hardware

December 13, 2019

“The increased capabilities of contemporary AI models provide unprecedented recognition accuracy, but often at the expense of larger computational and energet Read more…

By Oliver Peckham

SC19: Welcome to Denver

November 17, 2019

A significant swath of the HPC community has come to Denver for SC19, which began today (Sunday) with a rich technical program. As is customary, the ribbon cutt Read more…

By Tiffany Trader

Fujitsu A64FX Supercomputer to Be Deployed at Nagoya University This Summer

February 3, 2020

Japanese tech giant Fujitsu announced today that it will supply Nagoya University Information Technology Center with the first commercial supercomputer powered Read more…

By Tiffany Trader

51,000 Cloud GPUs Converge to Power Neutrino Discovery at the South Pole

November 22, 2019

At the dead center of the South Pole, thousands of sensors spanning a cubic kilometer are buried thousands of meters beneath the ice. The sensors are part of Ic Read more…

By Oliver Peckham

Leading Solution Providers

SC 2019 Virtual Booth Video Tour

AMD
AMD
ASROCK RACK
ASROCK RACK
AWS
AWS
CEJN
CJEN
CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
IBM
IBM
MELLANOX
MELLANOX
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
SIX NINES IT
SIX NINES IT
VERNE GLOBAL
VERNE GLOBAL
WEKAIO
WEKAIO

Jensen Huang’s SC19 – Fast Cars, a Strong Arm, and Aiming for the Cloud(s)

November 20, 2019

We’ve come to expect Nvidia CEO Jensen Huang’s annual SC keynote to contain stunning graphics and lively bravado (with plenty of examples) in support of GPU Read more…

By John Russell

Cray to Provide NOAA with Two AMD-Powered Supercomputers

February 24, 2020

The United States’ National Oceanic and Atmospheric Administration (NOAA) last week announced plans for a major refresh of its operational weather forecasting supercomputers, part of a 10-year, $505.2 million program, which will secure two HPE-Cray systems for NOAA’s National Weather Service to be fielded later this year and put into production in early 2022. Read more…

By Tiffany Trader

Top500: US Maintains Performance Lead; Arm Tops Green500

November 18, 2019

The 54th Top500, revealed today at SC19, is a familiar list: the U.S. Summit (ORNL) and Sierra (LLNL) machines, offering 148.6 and 94.6 petaflops respectively, Read more…

By Tiffany Trader

Azure Cloud First with AMD Epyc Rome Processors

November 6, 2019

At Ignite 2019 this week, Microsoft's Azure cloud team and AMD announced an expansion of their partnership that began in 2017 when Azure debuted Epyc-backed instances for storage workloads. The fourth-generation Azure D-series and E-series virtual machines previewed at the Rome launch in August are now generally available. Read more…

By Tiffany Trader

IBM Debuts IC922 Power Server for AI Inferencing and Data Management

January 28, 2020

IBM today launched a Power9-based inference server – the IC922 – that features up to six Nvidia T4 GPUs, PCIe Gen 4 and OpenCAPI connectivity, and can accom Read more…

By John Russell

Intel’s New Hyderabad Design Center Targets Exascale Era Technologies

December 3, 2019

Intel's Raja Koduri was in India this week to help launch a new 300,000 square foot design and engineering center in Hyderabad, which will focus on advanced com Read more…

By Tiffany Trader

In Memoriam: Steve Tuecke, Globus Co-founder

November 4, 2019

HPCwire is deeply saddened to report that Steve Tuecke, longtime scientist at Argonne National Lab and University of Chicago, has passed away at age 52. Tuecke Read more…

By Tiffany Trader

Microsoft Azure Adds Graphcore’s IPU

November 15, 2019

Graphcore, the U.K. AI chip developer, is expanding collaboration with Microsoft to offer its intelligent processing units on the Azure cloud, making Microsoft Read more…

By George Leopold

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This