Stan Posey
NVIDIA, Santa Clara, CA, USA; sposey@nvidia.com
NVIDIA HPC Technology and CAE Strategy
Technology
Development of professional GPUs as co-processing accelerators for x86 CPUs
Strategy
Strategic Alliances
Business and technical collaboration with ISVs; Industry customers; Research organizations
Applications Engineering
Technical collaboration with ISVs (ANSYS, etc.) for development of GPU-accelerated solvers
Software Development
NVIDIA linear solver toolkit (implicit iterative solvers) , CUDA libraries, GPU compilers
GPU System Integration
HP, Dell, IBM, Cray, SGI, Fujitsu, others; Kepler K20 based-systems available since 2012
2
GPU Product Summary for CAE Applications
NVIDIA Kepler family GPUs for CAE simulations
K20 (5 GB)
K20X (6 GB)
K40 (12 GB)
K6000 (12 GB)
3
CAE Workstations Now Configure with 2 GPUs
NVIDIA® MAXIMUS
Visual Computing
Parallel Computing
Intelligent GPU job Allocation
Unified Driver for Quadro + Tesla
ANSYS Certifications
CAD Operations
Pre-processing
Post-processing
FEA
CFD
CEM
HP, Dell, Xenon, others
Now Kepler-based GPUs
Available Since November 2011
4
NVIDIA GPUs Accelerate CAE at Any Scale
Same GPU Technology from
MAXIMUS Workstations to
TITAN at ORNL 20+ PetaFlops
18,688 NVIDIA Tesla K20x
TITAN — #2 at Top500.org
MAXIMUS Workstation
Key Application S3D for
Turbulent Combustion
How to efficiently burn next
gen diesel and bio fuels?
5
NVIDIA Use of CAE in Product Engineering
ANSYS Icepak – active and passive cooling of IC packages
ANSYS Mechanical – large deflection bending of PCBs
ANSYS Mechanical – comfort and fit of 3D emitter glasses
ANSYS Mechanical – shock & vib of solder ball assemblies
6
CAE Trends and GPU Acceleration Benefits
Higher fidelity (better models)
GPUs permit higher fidelity for existing (CPU-only) job times
Parameter sensitivities (more models)
GPUs increase throughput for existing (CPU-only) job capacity,
and at lower cost
Advanced techniques
GPUs make practical: high order methods, time dependent vs.
static, use of 3D solid finite elements vs. 2D shells, etc.
Larger ISV software budgets
GPUs provide more use of existing ISV software investment
7
Progress Summary for GPU-Parallel CAE
Strong GPU investments by commercial CAE vendors (ISVs)
GPU adoption led by implicit FEA and CEM, followed by CFD
Recent CFD breakthroughs in linear solvers (AMG) and preconditioners
GPUs now production-HPC for leading CAE end-user sites
Led by automotive, electronics, and aerospace industries
GPUs contributing to fast growth in emerging CAE applications
New developments in particle-based CFD (LBM, SPH, DEM, etc.)
Rapid growth for range of CEM applications and GPU adoption
8
GPU Progress – Commercial CAE Software
GPU Status
Available
Today
Structural Mechanics
ANSYS Mechanical
Abaqus/Standard
MSC Nastran
Marc
AFEA
NX Nastran
HyperWorks OptiStruct
PAM-CRASH implicit
LS-DYNA implicit
Product
Evaluation
Research
Evaluation
Fluid Dynamics
LS-DYNA
Abaqus/Explicit
RADIOSS
PAM-CRASH
Electromagnetics
ANSYS CFD (FLUENT)
Moldflow
Culises (OpenFOAM)
Particleworks
SpeedIT (OpenFOAM)
AcuSolve
EMPro
CST MWS
XFdtd
SEMCAD X
FEKO
Nexxim
CFD-ACE+
JMAG
HFSS
Abaqus/CFD
LS-DYNA CFD
Xpatch
CFD++
FloEFD
STAR-CCM+
XFlow
9
Additional Commercial GPU Developments
ISV
Domain
Location
Primary Applications
FluiDyna
CFD
Germany
Culises for OpenFOAM; LBultra
Vratis
CFD
Poland
Speed-IT for OpenFOAM; ARAEL
Prometech
CFD
Japan
Particleworks
Turbostream
CFD
England, UK
Turbostream
IMPETUS
Explicit FEA
Sweden
AFEA
AVL
CFD
Austria
FIRE
CoreTech
CFD (molding)
Taiwan
Moldex3D
Intes
Implicit FEA
Germany
PERMAS
Next Limit
CFD
Spain
XFlow
CPFD
CFD
USA
BARRACUDA
Convergent/IDAJ
CFD
USA
Converge CFD
SCSK
Implicit FEA
Japan
ADVENTURECluster
CDH
Implicit FEA
Germany
AMLS; FastFRS
FunctionBay
MB Dynamics
S. Korea
RecurDyn
Cradle Software
CFD
Japan
SC/Tetra; scSTREAM
10
Status Summary of ISVs and GPU Acceleration
Every primary ISV has products available on GPUs or ongoing evaluation
The 4 largest ISVs all have products based on GPUs, some at 3rd generation
ANSYS
SIMULIA
MSC Software
Altair
The top 4 out of 5 ISV applications are available on GPUs today
ANSYS Fluent, ANSYS Mechanical, Abaqus/Standard, MSC Nastran, . . . LS-DYNA implicit only
Several new ISVs were founded with GPUs as a primary competitive strategy
Prometech, FluiDyna, Vratis, IMPETUS, Turbostream
Availability of commercial CEM software expanding with ECAE growth
CST, Remcom, Agilent, EMSS on 3rd-gen; JSOL to release JMAG, ANSYS to release HFSS
11
CAE Software Focus on Sparse Solvers
CAE Application Software
Read input, matrix Set-up
Implicit Sparse
Matrix Operations
GPU
40% - 75% of
Profile time,
Small % LoC
- Hand-CUDA Parallel
- GPU Libraries, CUBLAS
- OpenACC Directives
Implicit Sparse
Matrix Operations
CPU
Global solution, write output
(Investigating OpenACC
for more tasks on GPU)
+
12
GPU Approach of Direct Solvers for Implicit CSM
Most time consumed in dense matrix operations such as Cholesky
factorization, Schur complement, and others
Method decomposes global stiffness matrix into tree of dense matrix fronts
Most CSM implementations send dense operations to GPU while keeping the
assembly tree transversal on the CPU
13
GPU Approach of Direct Solvers for Implicit CSM
Typical implicit CSM deployment of multi-frontal sparse direct solvers
Large dense matrix
fronts factored on GPU
Schematic Representation
of the Stiffness Matrix that is
Factorized by the Direct Solver
Lower threshold:
Fronts too small to
overcome PCIe
data transfer costs
stay on CPU cores
Small dense matrix fronts factored in parallel on CPU – more cores means higher performance
14
CAE Priority for ISV Software on GPUs
#4
#2
#3
ANSYS / ANSYS Fluent
OpenFOAM (Various ISVs)
CD-adapco / STAR-CCM+
Autodesk Simulation CFD
ESI / CFD-ACE+
SIMULIA / Abaqus/CFD
#1
LSTC / LS-DYNA
SIMULIA / Abaqus/Explicit
Altair / RADIOSS
ESI / PAM-CRASH
ANSYS / ANSYS Mechanical
Altair / RADIOSS
Altair / AcuSolve (CFD)
Autodesk / Moldflow
ANSYS / ANSYS Mechanical
SIMULIA / Abaqus/Standard
MSC Software / MSC Nastran
MSC Software / Marc
LSTC / LS-DYNA implicit
Altair / RADIOSS Bulk
Siemens / NX Nastran
Autodesk / Mechanical
15
Basics of GPU Computing for ISV Software
ISV software use of GPU acceleration is user-transparent
Jobs launch and complete without additional user steps
User informs ISV application (GUI, command) that a GPU exists
Schematic of a CPU with an attached GPU accelerator
CPU begins/ends job, GPU manages heavy computations
1
DDR
GDDR
GDDR
Cache
CPU
DDR
2
I/O
Hub
PCI-Express
4
GPU
Schematic of an x86 CPU
with a GPU accelerator
1.
2.
3.
4.
ISV job launched on CPU
Solver operations sent to GPU
GPU sends results back to CPU
ISV job completes on CPU
3
16
Computational Fluid Dynamics
ANSYS Fluent
17
ANSYS and NVIDIA Collaboration Roadmap
Release
ANSYS Mechanical
13.0
SMP, Single GPU, Sparse
and PCG/JCG Solvers
Dec 2010
14.0
ANSYS Fluent
ANSYS EM
ANSYS Nexxim
+ Distributed ANSYS;
+ Multi-node Support
Radiation Heat Transfer
(beta)
ANSYS Nexxim
+ Radiation HT;
+ GPU AMG Solver (beta),
Single GPU
ANSYS Nexxim
Nov 2012
+ Multi-GPU Support;
+ Hybrid PCG;
+ Kepler GPU Support
15.0
+ CUDA 5 Kepler Tuning
+ Multi-GPU AMG Solver;
+ CUDA 5 Kepler Tuning
ANSYS Nexxim
ANSYS HFSS (Transient)
Dec 2011
14.5
Dec 2013
18
ANSYS 15.0 HPC License Scheme for GPUs
Treats each GPU socket as a CPU core, which significantly increases
simulation productivity of your HPC licenses
Needs 1 HPC task
to enable a GPU
All ANSYS HPC products unlock GPUs in 15.0, including HPC, HPC Pack, HPC Workgroup,
and HPC Enterprise products.
19
ANSYS Fluent Profile for Coupled PBNS Solver
Non-linear iterations
Assemble Linear System of Equations
Runtime:
~ 35%
~ 65%
Solve Linear System of Equations: Ax = b
Accelerate
this first
Converged ?
No
Stop
Yes
20
Overview of AmgX Linear Solver Library
Two forms of AMG
Classical AMG, as in HYPRE, strong convergence, scalar
Un-smoothed Aggregation AMG, lower setup times, handles block
systems
Krylov methods
GMRES, CG, BiCGStab, preconditioned and ‘flexible’ variants
Classic iterative methods
Block-Jacobi, Gauss-Seidel, Chebyshev, ILU0, ILU1
Multi-colored versions for fine-grained parallelism
Flexible configuration
All methods as solvers, preconditioners, or smoothers; nesting
Designed for non-linear problems
Allows for frequently changing matrix, parallel and efficient setup
21
AmgX Developed for Ease-of-Use
No CUDA experience necessary to use the library
C API: links with C, C++ or Fortran
Small API, focused
Reads common matrix formats (CSR, COO, MM)
Single GPU, Multi-GPU
Interoperates easily with MPI, OpenMP, and Hybrid
parallel applications
Tuned for K20 & K40; supports Fermi and newer
Single, Double precision
Supported on Linux, Win64
22
How to Enable NVIDIA GPUs in ANSYS Fluent
Windows:
Linux:
fluent 3ddp -g -ssh –t2 -gpgpu=1 -i journal.jou
Cluster specification:
nprocs = Total number of fluent processes
M = Number of machines
ngpgpus = Number of GPUs per machine
Requirement 1
nprocs mod M = 0
Same number of solver processes on each machine
Requirement 2
𝑛𝑝𝑟𝑜𝑐
𝑀
mod ngpgpus = 0
No. of processes should be an integer multiple of GPUs
23
Considerations for ANSYS Fluent on GPUs
• GPUs accelerate the AMG solver of the CFD analysis
– Fine meshes and low-dissipation problems have high %AMG
– Coupled solution scheme spends 65% on average in AMG
• In many cases, pressure-based coupled solvers offer faster convergence
compared to segregated solvers (problem-dependent)
• The system matrix must fit in the GPU memory
– For coupled PBNS, each 1 MM cells need about 4 GB of GPU memory
– High-memory GPUs such as Tesla K40 or Quadro K6000 are ideal
• Better performance with use of lower CPU core counts
– A ratio of 4 CPU cores to 1 GPU is recommended
24
ANSYS Fluent GPU Performance for Large Cases
ANSYS Fluent 15.0 Performance – Results by NVIDIA, Dec 2013
36 CPU cores
144 CPU cores
36 CPU cores + 12 GPUs
144 CPU cores + 48 GPUs
Truck Body Model
ANSYS Fluent Time (Sec)
36
13
2X
1.4 X
9.5
Lower
is
Better
18
•
•
•
•
•
External aerodynamics
Steady, k-e turbulence
Double-precision solver
CPU: Intel Xeon E5-2667;
12 cores per node
GPU: Tesla K40, 4 per node
NOTE: Reported times
are sec per iteration
14 million cells
111 million cells
25
ANSYS Fluent GPU Performance for Large Cases
ANSYS Fluent 15.0 Performance – Results by NVIDIA, Dec 2013
144 CPU cores – Amg
144 CPU cores
48 GPUs – AmgX
144 CPU cores + 48 GPUs
36
80% AMG solver time
29
2X
2.7 X
Lower
is
Better
18
11
AMG solver time
per iteration (secs)
Truck Body Model
•
•
•
•
•
•
Fluent solution time
per iteration (secs)
111M mixed cells
External aerodynamics
Steady, k-e turbulence
Double-precision solver
CPU: Intel Xeon E5-2667;
12 cores per node
GPU: Tesla K40, 4 per node
NOTE: AmgX is a linear
solver toolkit from
NVIDIA, used by ANSYS
26
ANSYS Fluent GPU Study on Productivity Gains
• Same solution times:
64 cores vs.
32 cores + 8 GPUs
• Frees up 32 CPUs
and HPC licenses for
additional job(s)
• Approximate 56%
increase in overall
productivity for 25%
increase in cost
ANSYS Fluent Number of Jobs Per Day
ANSYS Fluent 15.0 Preview 3 Performance – Results by NVIDIA, Sep 2013
25
Truck Body Model
Higher
is
Better
20
15
16
16
64 Cores
32 Cores
+ 8 GPUs
4 x Nodes x 2 CPUs
(64 Cores Total)
2 x Nodes x 2 CPUs
(32 Cores Total)
8 GPUs (4 each Node)
10
5
14 M Mixed cells
Steady, k-e turbulence
Coupled PBNS, DP
Total solution times
CPU: AMG F-cycle
GPU: FGMRES with
AMG Preconditioner
0
NOTE: All results
fully converged
27
Computational Fluid Dynamics
OpenFOAM
28
NVIDIA Development Strategy for OpenFOAM
Provide technical support for commercial GPU solver developments
FluiDyna Culises library through NVIDIA collaboration on AMG
Vratis Speed-IT library, development of CUSP-based AMG
Invest in alliances (but not development) with key OpenFOAM organizations
ESI and OpenCFD Foundation (H. Weller, M. Salari)
Wikki and OpenFOAM-extend community (H. Jasak)
IDAJ Japan and ICON UK – support both OF and OF-ext
Conduct performance studies and customer benchmark evaluations
Collaborations: developers, customers, OEMs (Dell, SGI, HP, etc.)
29
Culises: CFD Solver Library for OpenFOAM
Culises Easy-to-Use AMG-PCG Solver:
#1. Download and license from http://www.FluiDyna.de
#2. Automatic installation with FluiDyna-provided script
#3. Activate Culises and GPUs with 2 edits to config-file
config-file CPU-only
config-file CPU+GPU
www.fluidyna.de
FluiDyna: TU Munich
Spin-Off from 2006
Culises provides a
linear solver library
Culises requires only
two edits to control
file of OpenFOAM
Multi-GPU ready
Contact FluiDyna
for license details
www.fluidyna.de
30
OpenFOAM Speedups Based on CFD Application
GPU Speedups for Different Industry Cases:
www.fluidyna.de
Range of model sizes and different solver schemes (Krylov, AMG-PCG, etc.)
Automotive
1.6x
Job Speedup
Multiphase
1.9x
Solver Speedup
Thermal
3.0x
Pharma CFD
2.2x
OpenFOAM CPU-Only
Process CFD
4.7x
Efficiency
31
FluiDyna Culises: CFD Solver for OpenFOAM
Culises: A Library for Accelerated CFD on Hybrid GPU-CPU Systems
Dr. Bjoern Landmann, FluiDyna
developer.download.nvidia.com/GTC/PDF/GTC2012/PresentationPDF/S0293-GTC2012-Culises-Hybrid-GPU.pdf
www.fluidyna.de
DrivAer: Joint Car Body Shape by BMW and Audi
http://www.aer.mw.tum.de/en/research-groups/automotive/drivaer
Mesh Size - CPUs
Solver speedup of 7x
for 2 CPU + 4 GPU
GPUs
• 36M Cells (mixed type)
• GAMG on CPU
Job Speedup
9M - 2 CPU 18M - 2 CPU
36M - 2 CPU
+1 GPU
+2 GPUs
+4 GPUs
2.5x
4.2x
6.9x
1.36x
1.52x
1.67x
• AMGPCG on GPU
32
Computational Structural Mechanics
ANSYS Mechanical
33
CSM Model Feature Recommendations for GPUs
Model should be at least 500 KDOF or greater, more is better
Ensures enough computational work to justify use of a GPU
Models with solid FE’s will speedup more than shell FE’s
Generally not enough computational work in 2D shell elements
Direct solvers: moderate GPU memory and heavy system memory
System memory needs capacity for entire system matrix (in-core)
GPU memory needs capacity for a single matrix front
Iterative solvers: large GPU memory and moderate system memory
GPU memory needs capacity for entire system matrix (in-core)
34
ANSYS and NVIDIA Collaboration Roadmap
Release
ANSYS Mechanical
13.0
SMP, Single GPU, Sparse
and PCG/JCG Solvers
Dec 2010
14.0
ANSYS Fluent
ANSYS EM
ANSYS Nexxim
+ Distributed ANSYS;
+ Multi-node Support
Radiation Heat Transfer
(beta)
ANSYS Nexxim
+ Radiation HT;
+ GPU AMG Solver (beta),
Single GPU
ANSYS Nexxim
Nov 2012
+ Multi-GPU Support;
+ Hybrid PCG;
+ Kepler GPU Support
15.0
+ CUDA 5 Kepler Tuning
+ Multi-GPU AMG Solver;
+ CUDA 5 Kepler Tuning
ANSYS Nexxim
ANSYS HFSS (Transient)
Dec 2011
14.5
Dec 2013
35
ANSYS Mechanical15.0 on Tesla GPUs
600
576
K40
K20
ANSYS Mechanical jobs/day
V14sp-5 Model
2.2X
2.1X
363
324
K40
3.9X
3.5X
93
2 CPU cores 2 CPU cores
+ Tesla K20
275
275
K20
93
2 CPU cores 2 CPU cores
+ Tesla K40
Simulation productivity (with a HPC license)
Turbine geometry
2,100,000 DOF
SOLID187 FEs
Static, nonlinear
Distributed ANSYS 15.0
Direct sparse solver
Higher
is
Better
8 CPU cores
7 CPU cores
+ Tesla K20
8 CPU cores 7 CPU cores
+ Tesla K40
Simulation productivity (with a HPC Pack)
Distributed ANSYS Mechanical 15.0 with Intel Xeon E5-2697 v2 2.7 GHz CPU; Tesla K20
GPU and a Tesla K40 GPU with boost clocks.
36
ANSYS Mechanical15.0 on Tesla K40
315
ANSYS Mechanical jobs/day
V14sp-6 Model
1.8X
180
172
2.9X
59
2 CPU cores
Higher
is
Better
2 CPU cores
+ Tesla K40
Simulation productivity
(with a HPC license)
4,900,000 DOF
Static, nonlinear
Distributed ANSYS 15.0
Direct sparse solver
8 CPU cores
7 CPU cores
+ Tesla K40
Simulation productivity
(with a HPC Pack)
Distributed ANSYS Mechanical 15.0 with Intel Xeon E5-2697 v2 2.7 GHz CPU and a Tesla
K40 GPU with boost clocks.
37
Computational Structural Mechanics
Abaqus/Standard
38
SIMUILA and Abaqus GPU Release Progression
Abaqus 6.11, June 2011
Direct sparse solver is accelerated on the GPU
Single GPU support; Fermi GPUs (Tesla 20-series, Quadro 6000)
Abaqus 6.12, June 2012
Multi-GPU/node; multi-node DMP clusters
Flexibility to run jobs on specific GPUs
Fermi GPUs + Kepler Hotfix (since November 2012)
Abaqus 6.13, June 2013
Un-symmetric sparse solver on GPU
Official Kepler support (Tesla K20/K20X)
39
Rolls Royce: Abaqus 3.5x Speedup with 5M DOF
Sandy Bridge + Tesla K20X Single Server
Elapsed Time in seconds
20000
4.71M DOF (equations); ~77 TFLOPs
Nonlinear Static (6 Steps)
Direct Sparse solver, 100GB memory
Speed up relative to 8 core
3
2.42x
15000
3.5
2.5
10000
2.11x
2
5000
1.5
0
1
8c
8c + 1g
8c + 2g
16c
Speed up relative to 8 core (1x)
•
•
•
16c + 2g
Server with 2x E5-2670, 2.6GHz CPUs, 128GB memory, 2x Tesla K20X, Linux RHEL 6.2, Abaqus/Standard 6.12-2
40
Rolls Royce: Abaqus Speedups on an HPC Cluster
•
•
•
Sandy Bridge + Tesla K20X for 4 x Servers
4.71M DOF (equations); ~77 TFLOPs
Nonlinear Static (6 Steps)
Direct Sparse solver, 100GB memory
Elapsed Time in seconds
9000
2.2x
6000
2.04X
1.9x
1.8X
3000
1.8x
0
24c
24c+4g
2 Servers
36c
36c+6g
3 Servers
48c
48c8g
4 Servers
Servers with 2x E5-2670, 2.6GHz CPUs, 128GB memory, 2x Tesla K20X, Linux RHEL 6.2, Abaqus/Standard 6.12-2
41
Computational Structural Mechanics
MSC Nastran
42
MSC Nastran Release 2013 for GPUs
MSC Nastran Direct Equation Solver is GPU accelerated
Sparse direct factorization with no limit on model size
Real, Complex, Symmetric, Un-symmetric
Impacts several solution sequences:
High impact (SOL101, SOL108), Mid (SOL103), Low (SOL111, SOL400)
Support of multi-GPU and for Linux and Windows
NVIDIA GPUs include Tesla 20-series, Tesla K20/K20X, Quadro 6000
43
43
MSC Nastran 2013 and GPU Performance
SMP + GPU acceleration of SOL101 and SOL103
6X
6
Higher
is
Better
serial
4c
4c+1g
4.5
3
2.8X
2.7X
1.9X
1.5
1X
1X
0
SOL101, 2.4M rows, 42K front
SOL103, 2.6M rows, 18K front
Lanczos solver (SOL 103)
Sparse matrix factorization
Iterate on a block of vectors (solve)
Orthogonalization of vectors
Server node: Sandy Bridge E5-2670 (2.6GHz), Tesla K20X GPU, 128 GB memory
44
MSC Nastran 2013 and NVH Simulation on GPUs
Coupled Structural-Acoustics simulation with SOL108
Europe Auto OEM
1X
Elapsed Time (mins)
1000
Lower is
Better
800
710K nodes, 3.83M elements
100 frequency increments (FREQ1)
Direct Sparse solver
600
2.7X
400
4.8X
5.2X
200
5.5X
11.1X
0
serial
1c + 1g
4c (smp)
4c + 1g
8c (dmp=2)
8c + 2g
(dmp=2)
Server node: Sandy Bridge 2.6GHz, 2x 8 core, Tesla 2x K20X GPU, 128GB memory
45
Computational Structural Mechanics
Altair OptiStruct
46
GPU Performance of OptiStruct PCG Solver
Problem: Hood of a car with pressure loads, displacements and stresses
Benchmark
2,2 Millions of Degrees of Freedom, 62 Millions of non zero
2 x GPU on 1 Node  7.5X
1200
380000 Shells + 13000 Solids + 1100 RBE3
SMP 6-core
5300 iterations
Dual NVIDIA M2090 GPUs, Cuda v3.2
Intel Westmere 2x6 X5670@2,93Ghz
Hybrid 2 MPI x 6 SMP
Elapsed(s)
NVIDIA PSG Cluster – 2 nodes with:
1000
Linux RHEL 5.4 with Intel MPI 4.0
SMP 6 + 1 GPU
800
572
600
Hybrid 2 MPI x 6 SMP + 2 GPUs
400
254
4.3X*
200
0
143
7.5X*
4 x GPU on 2 Nodes  13X!
350
306
300
Hybrid 4 MPI x 6 SMP
Hybrid 4 MPI x 6 SMP + 4 GPUs
250
Elapsed(s)
Platform
1106
200
150
100
50
0
85
13X*
47
Summary of GPU Progress for CAE
GPUs provide significant speedups for solver intensive simulations
Improved product quality with higher fidelity modeling
Shorten product engineering cycles with faster simulation turnaround
Simulations recently considered impractical now possible
FEA: Larger DOFs in model, more complex material behavior, FSI
CFD: Unsteady RANS, LES simulations practical in cost and time
Effective parameter optimization from large increase in number of jobs
48
Stan Posey
NVIDIA, Santa Clara, CA, USA; sposey@nvidia.com