improving analysis efficiency at delphi by using a...

Fereydoon Dadkhah

Improving Analysis Efficiency at Delphi by using a GPU

2

Overview

Background – HPC POC team

– Delphi involvement

– Nvidia Maximus program

– Simulation at Delphi

– The GPU as a co-processor

Evaluation – Workstation hardware

– Models, sizes, run times, etc.

Benefits

Pros and Cons

3

Background Delphi Involvement

The Nvidia Maximus program – Description of the program

– Delphi E&S participation

– Delphi program (POC steering team)

Simulation at Delphi – Delphi engineers routinely use simulation, esp. FEA to design and improve

products

– The simulations range from linear, steady-state analysis to those involving material and geometric non-linearity as well as time dependence

– Full time analysts completed more than 600 FEA projects

4

Background Simulation at Delphi

Beginner (1-5 years), 44

Intermediate, 41

Expert (10+

years), 18

What is your expertise level?

Percentage of total

responses

5


Early in the process, 51

After failure, 1

Both, 46

How do you use simulation?

P & D, 81

DV, 11

Production, 2

No answer, 6

When is simulation is used?

Percentage of total

responses

6


Simulation was significant, 39

Simulation identified the problem, 39

Simulation solved the problem, 32

No Answer, 18

Effectiveness of simulation

7

Background The GPU as a Co-processor

The Graphical Processing Unit is used as a co-processor

The CPU offloads some of the compute-intensive mathematical operations to the GPU

Modern GPUs have hundreds of smaller or light weight cores

By using the GPU the application appears to be running in a massively parallel environment

Modern GPUs such as the NVIDIA Tesla are fully programmable as opposed to the older devices which were dedicated to graphics (drawing polygons)

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Background The GPU as a Co-processor

CPU

4 Sockets, 16 cores

Tesla C2075 GPU

448 CUDA Cores

CPU GPU

Xeon X5650

Tesla C2075

Cores 6 448

Clock Speed 2.66 GHz 1.15 GHz

Memory Bandwidth 32 GB/s 144 GB/s

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Evaluation Workstation Hardware

HP Z800 workstation configuration as tested Windows 7 Professional 64-bit 2 x Xeon® X5650 2.67 GHz CPUs 12MB/1333 (12 cores) NVIDIA Quadro 6000 1 GB Graphics HP 24 GB (6x4GB) DDR3-1333 ECC memory Add HP 24 GB (6x4GB) for total 48 GB HP 500 GB SATA 7200 HDD + 2x 500 GB HDD configured as RAID 0 NVIDIA Tesla C2075 GPU

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Evaluation

Structural mechanics simulations suited for GPU use – Problem size (DOF)

– Analysis types

– Element types

– Solvers

Applicability and potential benefits to Delphi – POC steering team survey results

– SharePoint survey results

11

Evaluation Models, Sizes, Run times, etc.

Optimum problem size – The optimum problem size for structural mechanics problems is approximately

between 1m to 5m DOF (330,000 < nodes < 1,700,000)

– Below 1m DOF and above 5m DOF the problems don’t gain much by using the GPU

Models built with solid elements benefit more than those built with shell/plate elements

ANSYS Solvers – Direct solver generally does better

– In-core execution increases performance

– Turn off MSAVE for the PCG solver, otherwise GPU is disabled

Single v. multiple GPUs – ANSYS Mechanical R14 and below use only one GPU

– R14.5 can use multiple GPUs

– Distributed ANSYS performs better than SMP

» Interconnection speed between multiple boxes is a factor*

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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Qualifying Simulations Potential Qualifiying Future Potential Qualifying Non-Qualifying

Chart courtesy Eric Kana,

Delphi Analysis Heat Map

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Model ID Max. DOF Analysis

Battery Pack BP-nn 3,128,781 Modal

MCLC MCLCnn 4,724,478 Thermal Stress

BLPF BLPF-nn 3,259,845 Thermal Stress

TCM 80W Conn-nn 1,662,009 Thermal Stress

TCM 64W Conn-11 4,655,463 Thermal Stress

GPU Acceleration of Selected Problems

Model Descriptions

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Battery Pack

15

MCLC

16

BLPF

17

Engine Control Module

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Collected data includes: – Case ID

– DOF

– Run time (Elapsed time)

– ANSYS release

– Solver

C:/Documents and Settings/jz07tc/My Documents/HPC/NVIDIA/GPU Computing-07-Feb-2012.xlsx



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Evaluation Models, Sizes, Run times

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

BP-1 BP-3 BP-7 BP-13

Elaspsed time without GPU 840 739 1934 8487

Elaspsed time with GPU 701 656 1907 6033

Tim

e [

S]

Elaspsed time without GPU Elaspsed time with GPU

Case ID ANSYS

Release Dist. Solver? RAID 0

Number of Cores Used

Number of Nodes

No. of modes expanded

BP-1 13SP2 NO No 12 1,042,927 30

BP-3 13SP2 NO No 8 1,042,927 30

BP-7 R14 NO Yes 8 1,042,927 30

BP-13 R14 NO No 8 1,042,927 100

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0

500

1000

1500

2000

2500

3000

MCLC-3 MCLC-6 MCLC-11 MCLC-13



Tim

e [

S]


Case ID ANSYS

Release Dist. Solver? RAID 0

Nnumber of Cores Used

Number of Nodes

MCLC-3 R14 NO No 6 1,574,826

MCLC-6 R14 NO YES 8 1,574,826

MCLC-11 13SP2 NO No 8 601,468

MCLC-13 R14 NO NO 12 1,574,826

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Case ID ANSYS

Release Dist. Solver? GPU RAID 0

Nnumber of Cores Used

Number of Nodes

Conn-1 R14 NO YES YES 8 554,003

Conn-11 NO YES YES 8 1,551,821

Conn-15 NO NO YES 4 1,551,821

Conn-14 NO NO YES 6 1,551,821

0

200

400

600

800

1000

1200

1400

1600

Conn-1 Conn-11 Conn-15 Conn-14



Tim

e [

S]


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0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

16.5

11.2

1.4

28.9

19.7

13.4

1.6

42.4

19.2

5.0

22.3

13.4

29.0

Perc

en

t

Improvement in run time due to GPU

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0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1.2 1.1

1.0

1.4

1.2

1.2

1.0

1.7

1.2

1.1

1.3

1.2

1.4

Run time improvement Higher is better

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Evaluation GPU Effectiveness v. Number of Cores

0.0

5.0

10.0

15.0

20.0

25.0

4 6 8 10

22.3

13.4

5.0 5.8

Connector Models Percent Improvement vs. number of cores

Percent Improvement

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Benefits

The Tesla C2075 GPU is capable of significantly improving FEA solution times

– Most of the test problems saw a 10% or more improvement in run time

– 6 out of 13 cases saw an improvement of 19% or better

– The maximum improvement was 42% (1.7 times faster)

Depending on the problem size, a GPU can be a relatively low-cost solution

The use of GPUs for computation is still evolving but major software vendors (ANSYS and Dassault among others) have embraced this technology

The trend is toward widespread use of GPUs

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Benefits

On workstations with 8 or more CPU cores and dual display adapters, e.g., Quadro+Tesla, the GPU offloads enough of the work to allow the user to continue using the workstation

Alternatively, it may be possible to for load balancing software to submit jobs the workstation without affecting the main user of the workstation (Should be investigated before adopting)

Workstations in the same office area could be used as a cluster after hours maximizing the investment in the hardware

GPUs can also be added to compute servers to speed up processing

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Pros and Cons

Advantages – Relatively inexpensive

– Easy to install and use

– No additional software or maintenance required

– Can be configured to be used as graphics processor or “compute server”

Disadvantages – Ineffective with very dense matrices

» PCG solver is more efficient when dealing with dense matrices

» GPU seems to help in some cases but not all

Distributed ANSYS+GPU resulted in 30% improvement