SOLUTION BRIEF

AeroSUV (image courtesy of Siemens)
TABLE OF CONTENTS
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Improving Vehicle Design Through Simulation . . . . . . . . . . . . . 2
The New Era of Simulation Using GPUs . . . . . . . . . . . . . . . . . . . . 2
Doing More with Less - Improving Capital Expenditures forSimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Accelerated Computing is Sustainable Computing . . . . . . . . . . 2
Configuring the Optimal Server for your Workload . . . . . . . . . . 2
Application Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Supermicro Solution: Supermicro SYS-521GE-TNRT . . . . . . . . 3
Test Case Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Performance Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Price Performance and Sustainability . . . . . . . . . . . . . . . . . . . . . 6
Optimum Performance - Scaling . . . . . . . . . . . . . . . . . . . . . . . . . 6

Executive Summary
As automotive companies strive to reduce vehicle
development time and bring new vehicles to market faster,
there has been a growing need for computer-aided
engineering for automotive development. One recent
advancement in high performance computing (HPC) has been
leveraging GPUs for computational fluid dynamics (CFD) tosignificantly speed up calculation time while reducing carbon
footprint and cost.
Supermicro and NVIDIA have partnered to develop areference architecture for HPC applications with optimum
hardware configurations. In this Solution Brief, we will review
the key requirements to get the most out of GPU acceleration
for Computational Fluid Dynamics (CFD).

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8More Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
© 2024 Copyright Super Micro Computer, Inc. All rights reservedJanuary, 20241

Improving Vehicle Design Through Simulation
The need to improve vehicle efficiency, cost, and comfort are critical targets in the automotive industry. With the move to hybrid
and electric vehicles, improving vehicle range has become particularly important. Given that the range is directly proportional
to the drag, it’s critical to understand and reduce it early in the vehicle design. Computer Aided Engineering (CAE) enables
engineers to quickly assess changes without the need to build expensive and time-consuming prototypes. Computational Fluid
Dynamics (CFD) tools such as Siemens Simcenter STAR-CCM+ allow engineering professionals to understand aerodynamic
performance promptly. A standard automotive CFD benchmark case is used to assess GPU performance

The New Era of Simulation Using GPUs
In a 2021 survey of vehicle engineers, 52% of respondents expressed that their demands to reduce the time required to complete
design cycles have been one of the most challenging aspects of their jobs. To address this growing need for acceleration, overthe past 15 years, NVIDIA has worked closely with simulation software leaders to bring the benefits of GPUs' massive parallel
processing power and the lightning-fast interface between memory and GPU cores to vehicle simulation. By running vehicle
simulation applications on GPUs, leading simulation software providers have demonstrated up to 30X acceleration of theirapplications, thereby improving engineer productivity and helping reduce vehicle time to market.

Doing More with Less - Improving Capital Expenditures for Simulation
Increased computational efficiency means more than workload acceleration, but just as importantly, it must include a smaller
capital expenditure footprint. The incredible efficiency of GPUs for computer-aided engineering results in huge infrastructure
cost savings for automotive customers. Because modern servers can fit up to 10 dual slot GPUs, offloading computationallyheavy workloads to GPUs allows customers to achieve large amounts of server consolidation. Through the joint effort between
Supermicro and NVIDIA, we have demonstrated that a single server with 10 NVIDIA H100 GPUs replaces 30 servers, each with 2x CPUs for CFD workloads such as Siemens Simcenter STAR-CCM+. For equivalent performance, this results in 3x cost savings

Accelerated Computing is Sustainable Computing
Although much attention is focused on reducing vehicle tailpipe emissions and EV power consumption, reducing carbon
footprint along the entire vehicle design, testing, and manufacturing workflows is equally important. The third value
proposition of Supermicro servers with NVIDIA GPUs for computer-aided engineering is the incredible savings in computing
power necessary for these computationally heavy workloads. For equivalent performance, this results in a 5x power saving byrunning on NVIDIA GPUs, as shown in the performance results section.

Configuring the Optimal Server for your Workload
Supermicro’s servers are ideal for CAE because of their versatility in the number of GPUs they support. NVIDIA recommends
using 100 and 200 class products (NVIDIA A100, NVIDIA H100, NVIDIA H200, NVIDIA B100) for CAE applications because of theirneed for double precision (FP64) compute and high memory bandwidth.
To decide on the right number of GPUs for the system, GPU memory size is a critical factor. For CFD, the mesh size is often used
as a measure of the size of a simulation. The total GPU memory used is also determined by the complexity of the physics used.
As a rule of thumb, we can say that the memory required can be between 1.2-5x the size of the mesh, depending on thecomplexity of the physics. For example, for a relatively simple case in terms of physics, we’d need 150 GB of memory for a 1002
© 2024 Copyright Super Micro Computer, Inc. All rights reservedJanuary, 20242

million cell mesh (100x1.5). Given that A100 and H100 have 80GB of memory, they need at least two GPUs, but 3 GPUs would berecommended. Software vendors can give additional guidance on the memory requirements for their software.

Application Support
One of the key questions is whether your CFD tool of choice supports GPU acceleration. The last few years have seen aproliferation of GPU solver support from both mainstream CAE software and new start-ups. Check with your software vendor tosee if the physics you intend to simulate is GPU accelerated. Much of the solver runs on the GPU. By running the solver on highly
parallel GPUs is the reason for very good acceleration compared to a few years ago when CFD codes were only accelerating partof the solver.

Supermicro Solution - The Supermicro SYS-521GE-TNRT
Basics of the SYS-521GE-TNRT
GPU

10 x NVIDIA H100 GPUs 80 GB (PCIe)CPU

2x 4th Gen Intel® Xeon® CPU Max
Processors (9468)

Memory Installed

1 TB

Operating System

Rocky Linux 8.6NVIDIA Driver

535.104.12

CUDA Version

12.2

Supermicro SYS-521GE-TNRT Server (2 CPUs, 10 x NVIDIA H100 PCIe GPUs)
The Supermicro SYS-521GE-TNRT server is designed to accommodate up to 10 PCIe NVIDIA H100 GPUs. The CPUs can be either
4th or 5th Gen Intel Xeon processors and can hold up to 8 TB of memory.

Test Case Details•Standard AeroSUV external aerodynamics test case (AeroSUV
https://www.sae.org/publications/technical-papers/content/2019-01-0646/)•Mesh size - Server: 57, 106, 320 million cells

byFKFS,Stuttgart,3© 2024 Copyright Super Micro Computer, Inc. All rights reserved

January, 2024

3Germany•

Mixed precision•Siemens Simcenter STAR-CCM+ : Version 23.06•

Coupled solver

Performance Results
The chart below shows results for running the same AeroSUV test case of various mesh sizes on different numbers of H100 GPU
on the server configuration. Note the 57 million cell case can be run on less than 4 GPUs if required.
Key Results:•For the smallest mesh size (57 million cells) with 4 NVIDIA H100 PCIe GPUs are 6.6x faster than 94 CPU cores•For the medium mesh size (106 million cells) with 6 NVDIDIA H100 PCIe GPUs are 9.5x faster than 94 CPU cores•For the largest mesh size (322 million cells) with 10 NVIDIA H100 PCIe GPUs are 29.3x faster than 94 CPU cores

The results above demonstrate the significant improvements in turnaround time that can be achieved by running CFD
simulations on NVIDIA GPUs leveraging Supermicro hardware. Using GPUs allows more designs to be considered in less time,
resulting in a more optimized product.

4© 2024 Copyright Super Micro Computer, Inc. All rights reservedJanuary, 20244

5© 2024 Copyright Super Micro Computer, Inc. All rights reservedJanuary, 20245

Price Performance and Sustainability
As well as comparing pure performance across one server, it is essential to consider the overall price performance. Assuming aGPU server (8-10 H100 GPUs) and a speed-up of ~30x compared to a CPU server, customers would have to invest in 30 CPU
servers to match the performance of one GPU server. These results assume linear scaling. Therefore, running on GPU, we could
estimate it would be 3x cheaper than CPU for equivalent performance. Alternatively, spending the same budget as the CPU onthe GPU provides a 3x improvement in throughput.
Similar cost savings can also be seen in the power required for the same performance. Estimating from the thermal design
power, a GPU node would require approximately 3000W compared to a CPU node ~500W per CPU node, given the need for 30
servers to match GPU performance. This represents a 5x power saving.

Optimum Performance - Scaling
In the previous sections, we discussed the minimum number of GPUs required for running the simulation, but what is theoptimum number? Below are charts that show the speed-up and the memory usage for the 57 million cell aerodynamics case.
We can see that performance scales up to around 8 GPUs. Suppose we look at the memory usage per GPU. Below 6 GPUs thatmemory usage is below 20GB per GPU, so less than 25% of the GPUs' available memory. The conclusion is that scaling will suffer
if running a case only occupies a small percentage of GPU memory. Therefore, having at least 25% of the GPU memory occupied
is good for maintaining reasonable scaling.
Speedup6© 2024 Copyright Super Micro Computer, Inc. All rights reservedJanuary, 202467© 2024 Copyright Super Micro Computer, Inc. All rights reservedJanuary, 20247Summary
The results presented above show that GPU acceleration offers a significant opportunity to reduce CFD simulations' turnaroundtime and save on hardware costs. Power savings are also considerable and becoming more critical as companies look toimplement more sustainable solutions. Supermicro provides a range of efficient solutions ranging from deskside workstations
to 10 GPU systems for maximum performance. To find out more about the benefits of CAE for your automotive workflows,
please contact Supermicro. NVIDIA and Supermicro jointly work together to help our customers develop the most optimal
solution for their CAE workloads.

For More Information
Supermicro NVIDIA Accelerated Systems – https://www.supermicro.com/accelerators/nvidia
Supermicro 10x GPU PCIe Server - https://www.supermicro.com/en/products/system/gpu/5u/sys-521ge-tnrt

SUPERMICRONVIDIAAs a global leader in high performance, high efficiency server technology
and innovation, we develop and provide end-to-end green computing
solutions to the data center, cloud computing, enterprise IT, big data,HPC, and embedded markets. Our Building Block Solutions® approachallows us to provide a broad range of SKUs, and enables us to build anddeliver application-optimized solutions based upon your requirements.
See www.supermicro.com

Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer inaccelerated computing. The company’s invention of the GPU in 1999
sparked the growth of the PC gaming market, redefined computer
graphics, ignited the era of modern AI and is fueling industrial
digitalization across markets. NVIDIA is now a full-stack computing
company with data-center-scale offerings that are reshaping industry.
More information at https://nvidianews.nvidia.com.

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