Elster falch-gpu-cse-sem-oct2013

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http://research.idi.ntnu.no/hpc-lab

The Power of GPU Computing
Thomas L. Falch and Dr. Anne C. Elster(*)
HPC-Lab, Dept. Computer and Info. Science
Norwegian University of Science & Technology
Trondheim, Norway
(*)Elster also holds a 0% Visiting Scientist appointment at
ICES, UT Austin where she spends summer and sabatticals

Falch & Elster: The Power of GPU Computing

NTNU CSE Seminar Oct 2, 2013

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Thank yous to:


HPC-Lab Post Docs
and grad. students!

HPC-Lab 2012


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Dr. Elster´s HPC-Lab currently focuses
on research related to novel GPU
and multi-core architectures


> 40 Master students (since 2001)
> 15 masters projects on GPU for HPC
Parallelization of Seismic and Image
Related Applications on GPUs and
Multi-Cores
Modeling Heterogenous systems
Parallel and Distributed Algorithms and
Tools
Performance Evaluation and
Benchmarking

Collaborators / Supporters:

Adaptive and Auto-Tuneable
Algorithms and Implementations

AMD, ARM, CERN, NVIDIA, Statoil, GPU Computing
Falch & Elster: The Power of Schlumberger, GE-Healthcare, and others

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Outline


• Introduction to GPU computing
• Overview of GPU projects at the HPC-Lab
– 3D Real-Time Snow simulation
– Flow simulations (porous media)
– Surface extraction

• Visualization of scattered point data



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The “Walls” (refr. Dr. David Patterson)


To increase processor performance one can:
1. Increase the system clock speed -> Power Wall(*)
2. Increase memory bandwidth-> more complex
3. Parallelize -> more complex

(*) The Power Wall: Too much Heat and
transistor performance degrades
(more power leakage as power increases)!
 Now maxing out at 3-4GHz for general processors


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The TOP10 (June 2013)
Rank

Site

Manufacturer

1

National University of
Defense Technology

NUDT

2

DOE/SC/Oak Ridge
National Laboratory

Cray

3

DOE/NNSA/LLNL

IBM

4

5

RIKEN Advanced
Institute for
Computational
Science (AICS)
DOE/SC/Argonne
National Laboratory

Computer

Country

Cores

Rmax
[Tflops]

Power
[MW]

Tianhe-2 (MilkyWay-2) - THIVB-FEP Cluster, Intel Xeon
E5-2692 12C 2.200GHz, TH
China 3,120,000 33,862.7 17.8
Express-2, Intel Xeon Phi
31S1P
Titan - Cray XK7 , Opteron
6274 16C 2.200GHz, Cray
USA
560,640 17,590.0
8.2
Gemini interconnect, NVIDIA
K20x
Sequoia - BlueGene/Q, Power
USA 1,572,864 17,173.2
7.9
BQC 16C 1.60 GHz, Custom

Fujitsu

K computer, SPARC64 VIIIfx
2.0GHz, Tofu interconnect

Japan

IBM

Mira - BlueGene/Q, Power
BQC 16C 1.60GHz, Custom

USA

705,024 10,510.0 12.7
786,432

8,586.6

3.9

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Intel´s Xeon Phi


(aka MIC, Knights Ferry/Knights Corner, Larrabee)



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How to get to Exascale?


Limited by Power!
Solution?
(BOF at SC´10 by Elster, Vaquez-Poletti & Perhac:
Towards Exa-Scale: Heterogeneous Clouds
(CPUs, GPUs and Embedded Devices)



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Outline




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NTNU GPU Activities


NTNU is a NVIDIA CUDA Research & Teaching Center
• Elster teaches a Senior Parallel computing class with
50+ students
•

Elster´s HPC-lab has graduated 20+ Master
students in GPU computing (2007-2013)



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HPC-Lab History (last 6 yrs):

Fall 2006:
• First 2 student projects with GPU programming (Cg)
Christian Larsen (MS Fall Project, December 2006):
“Utilizing GPUs on Cluster Computers” (joint with Schlumberger)
Erik Axel Nielsen asks for FX 4800 card for project with GE Healthcare
•

Elster head of Computational Science & Visualization program and helped
NTNU acquire new IBM Supercomputer
(Njord, 7+ TFLOPS, proprietary switch)

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HPC-Lab History (contin.):

2007:

Erik Axel Nielsen (Masters thesis, June 2007):
“Real-time Wavelet Filtering on the GPU” -- joint project with GE Healthcare.
40 times GPU speedup of algorithm led to our implementation being adopted the same fall in
their high-end cardivascular ultrasound scanner.
Christian Larsen (Masters thesis, June 2007) Tore Fevang, Schlumberger (co-advisor):
"Framework for Polygonial Structures Computations on Clusters” (incl GPU parallelization)
Idar Borlaug (Masters thesis, June 2007):
“ Seismic Processing Using Parallel 3D FMM”
Thibault Collet (Masters thesis summer 2007):
"Massively Online Games with Food Chains"
Knut Imar Hagen (Masters thesis, June 2007)
“Fault-tolerance for MPI Codes on Computation
Clusters” (joint project with Statoil)
Nils Magnus Larsgård (Masters thesis summer 2007):
“Framework for Converting MPI Codes to Hybrid OpenMP/MPI Codes”
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2008:
•

Quadcore Supercomputer at UiTø (Stallo)
ca. 70 TF

•

HPC-LAB at IDI/NTNU opens in Oct. with
• several NVIDIA donation
• Several quad-core machines (1-2
donated by Schlumberger)

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2008:
HPC-LAB at IDI/NTNU opens in Oct. with
• several NVIDIA donation
• Several quad-core machines (1-2
donated by Schlumberger)

Atle Rudshaug (Masters thesis, June 2008): “Optimizing & Parallelizing a Large
Commercial Code for Modeling Oil-well Networks” -- joint project with Yggdrasil
Andreas Bach (Masters thesis, September 2008): “Profiling and Optimizing
a Seismic Application on Modern Architectures” -- joint project with Statoil
Rune Hovland (Masters project, Dec 2008) :
"Latency and Bandwidth Impact on GPU Systems" (ParCo 2009 w/ Elster)
Daniele Giuseppe Spampinato (Masters Project, December 2008):
"Linear Optimizations with CUDA (IPDPS MTAAP 2009 w/ Elster)
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Selected Master theses and Master reports
supervised by Dr. Elster in 2009
1) Robin Eidissen (Masters thesis, January 2009) :
"Utilizing GPUs for Real-Time Visualization of Snow” (demoed @ SC´08-SC´10)
Eirik Aksnes and Henrik Hesland (MS Project, Jan 2009) :
"GPU Techniques for Porous Rock Visualization”
2) Rune Erlend Jensen (Masters thesis, May 2009, currently PhD student at HPC-Lab) :
"Techniques and Tools for Optimizing Codes on Modern Architectures:
A Low-Level Approach” (NR MS Thesis Award!)
3) Rune Johan Hovland (Masters thesis, June 2009),
Dr. Magnus Lie Hetland (co-advisor): "Throughput Computing on Future GPUs”
4) Henrik Hesland (Masters thesis, June 2009) Thorvald Natvig (co-advisor):
"GPU-Enabled Interactive Pore Detection for 3D Rock Visualization "
5) Eirik Ola Aksnes (Masters thesis, July 2009)
Ståle Fjeldstand & Atle Rudshaug, Numerical Rocks (co-advisors):
"Simulation of Fluid Flow Through Porous Rocks on Modern GPUs" (ParCo 2009)
6) Daniel Haugen (Masters thesis, July 2009) Tore Fevang, Schlumberger (co-advisor):
"Seismic Data Compression and GPU Memory Latency"
7) Åsmund Herikstad (Masters thesis, July 2009) Svein-Erik Måsøy, MedTek, NTNU (co-advisor)
"Parallel Techniques for Estimation and Correction of Aberration in Medical Ultrasound Imaging"
8) Owe Johansen (Masters thesis, July 2009) John Hybertsen & Jon André Haugen, Statoil (coadvisors): "Seismic Shot Processing on GPU"
9) Daniele Giuseppe Spampinato (Masters thesis, July 2009; currently PhD student @ ETH)
"Modeling Communication on Multi-GPU Systems” (ParCo 2009)


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HPC-Lab -- Spring 2010

Dr. Anne C. Elster
Lab Director

Dr. John P. Ryan
Post Doc

Dr. Jan Perhac
Post Doc

Jan Christian
Meyer (PhD stud)

Thorvald Natvig
(PhD stud.)

Rune E. Jensen
(PhD stud.)

Master Students – Spring 2010

Ahmed Aqrawi
Assist. TDT 4200

Aleksander
Gjermundsen

Affiliates /Visitors

Andreas
Hysing

Øystein Krog

Holger Ludvigsen
Assist TDT 4205

+ 2 Cybernetics students
+ 3 visualization students
+ 1-2 || arch/multicore students
+ 1 Marine student

Eirik O. Aksnes
(tentative PhD,
Now consultant
for Statoil)

Refsnaes & Singh did FEM on GPU -NTNU CSE
Kvamsdal & Elster Seminar Oct 2, 2013

Gagandeep
Collarborations between
Singh (Math)

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2010:
-

NVIDIA Fermi-based card(470, c2050, c2070(fall))
More on OpenCL

Ahmed A. Aqwari (Masters thesis, June 2010):
“Effects of Compression on Data Intensive Algorithms”
Aleksander Gjermundsen (Masters thesis, July 2010):
“Audio Processing on GPU”
Andreas Hysing (Masters thesis, Aug 2010): Parallel Inversion code (w/Statoil)
Øystein Krog (Masters thesis, June 2010):
“GPU-based Real-Time Snow Avalanche Simulations” (SPH)
Holger Ludvigsen (Masters thesis, June 2010, Dr. Frank Lindseth (co-advisor):
“Real-Time GPU-Based 3D Ultrasound Reconstruction
and Visualization”
Thorvald Natvig (PhD Dec 2010) “Automatic Run-Time Communication and I/O”
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HPC-Lab -- 2011
(Elster on sabatical 2010/11)

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Dr. Anne C. Elster
Lab Director

Dr. Ian Karlin
Post Doc

Jan Christian
Meyer (PhD stud)

Rune E. Jensen
(PhD stud.)


Erik Smistad
(PhD stud.)
Elster co-advisor

Master Students – Spring 2011

Fredrik Fossum
GPU Rigid body
simulation

Yngve S. Lindal
(GPU proj
@ CERN)

Affiliates /Visitors

Ole-Martin
Brende
(MedTech)

Ove
Stinessen
(Statoil proj)

Jarle
Stensland
(OpenCL BLAS)

Thor Kristian
Valderhaug
(Numerical Rocks
proj Multi-GPU LBM)

Geir Jostein
Lien
(2-yr Master
Informatics,
graduated
2012)

Miguel Martinez-delAmor (PhD student from
Spain,
Falch & Elster: The Power of GPU Computing Fall 2011 NTNU CSE Seminar Oct 2, 2013

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HPC-Lab: Master theses 2012

Kjetil Babington: Terrain Rendering Techniques for the HPC-Lab Snow Simulato

Thomas Løfsgaard Falch: 3D Visualization of X-ray Diffraction Data

Geir Josten Lien: Auto-tunable GPU BLAS
Jan Magne Rovde: Real-Time Granular Flow Simulation Using
the PCISPH Method on GPGPU Devices Using CUDA
Frederik Magnus Johansen Vestre:
Enhancing and Porting the HPC-Lab Snow Simulator to OpenCL
on Mobile Platforms
Jan Christian Meyer, PhD Theses (December):
Performance Modeling of Heterogeneous Systems


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HPC-Lab: 2012/2013

Jan Christian Meyer, PhD Theses (December):
Performance Modeling of Heterogeneous Systems



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HPC-Lab: Master Theses 2013


Lark Kirkholt Melhus (June):
Analyzing Contextual Bias of Program Execution on Modern CPUs
Magnus Mikalsen (June): OpenACC-based Snow Simulation
Andreas Nordahl (June): Enhancing the HPC-Lab Snow Simulator
with More Realistic Terrains and Other Interactive Features
Lars Espen Nordhus (June): Ray Tracing for Simulation of Wireless Networks
in 3D Scenes
Stian Aaraas Pedersen (June): Progressive Photon Mapping on GPUs
Andreas Skomedal (June): Heterogeneous FTDT for Seismic Processing
Henrik Holenbakken Knutsen (Sept): Enhancing Software Portability
with Hardware Parametrized Autotuning


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HPC-Lab: 2013/2014


Anne C. Elster – Director
Malik Khan – Post Doc to start Nov 1, 2013
PhD students:

●

Rune Jensen,

●

Johannes Kvam

●

Thomas Falch,

Samira Pakdel,

Ruben Spaans

Co-supervised by Elster:

●
●
●
●
●

Johannes Kvam, Erik Smistad, Mehdi Bozorgi, Lane Holloway (UT ECE Student)

●

+ 8 master students & 2 MedTech PhD students


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Outline






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Snow Simulation:
calc. 4+ million particles in real-time
using multi-core CPU + GPU



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Snow Simulation – Wind field



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Add more real-time features



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Add Road Generation
(Used A* algorithm, Demo @ SC11)




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Add Ray-Tracing




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Outline






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Simulations of Fluid Flow through
Porous Rocks using GPUs


Eirik Ola Aksnes & A.C: Elster (ParCo 2009)
+ current work with Thor Kristian Valderhaug using OpenCL
In collaboration with :
Numerical Rocks & NTNU Chemistry Dept.
Use Lattice Bolzmann Method
a.k.a LBM



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Outline






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3D Surface Extraction


(w/ Dr. Frank Lindseth (SINTEF MedTek and NTNU,
and MS/PhD student Erik Smistad



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(w/ Dr. Frank Lindseth (SINTEF MedTek and NTNU,
and MS/PhD student Erik Smistad



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3D Surface Extraction on GPUs


• Use Marching Cubes – algorithm for extracting a 3D
surface from a set of sampled scalars
• Algorithm used extensively for visualizing and analyzing
medical data (X-ray, MR) and the result of 3D
segmentation.
• Completely data parallel
• Challenge:
How to store the result of each
cube in parallel on GPU


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-- Histogram data
•


Challenge: How to store the result of each cube in parallel on GPU?
In serial implementation this is simple – just use a stack and
add the vertex data to the stack

• GPU Solution: Histogram Pyramids [1]
• A datastructure that:
•
•
•
•

Filters out cubes that has no triangle (stream reduction)
Returns total sum of triangles
Provides each cube with an index for memory storage
Can be efficiently used by means of textures yielding large speed-ups

[1] G. Ziegler et al: On-the-fly Point Clouds through Histogram Pyramids; Vision, Modeling, and Visualization 2006



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-- Histogram Pyramids: Construction & Traversal

HP Construction

HP Traversal



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-- Results:
HPMC Dyken et al.
Size

Exec.
time

512^3

vs.

Our OpenCL implementation

FPS
(avg)

Memory

Size

Exec.
time

FPS
(avg)

Memory

3324 ms

0.3

490 MB

512^3

34 ms

0.3

121 MB

256^3

5 ms

223

122 MB

256^3

10 ms

105

40 MB

128^3

3 ms

394

44 MB

128^3

4 ms

233

26 MB

64^3

2 ms

519

22MB

64^3

3 ms

319

22MB

Our Test system:
•
•
•
•

Intel i5 750, 4GB RAM
ATI Radeon 5870 (1GB RAM)
AMD Catalyst 11.2 graphics driver
APP SDK 2.3 w/ OpenCL 1.1

Note:
OpenCL-OpenGL
Synch measured to
be 2-20ms, i.e. 7090<% for smallest
datasets



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Outline






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Scattered Point Data

(a )

(b)



(c)


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Examples


• Sensor networks
• Simulations (n-body, SPH)
• Post-processing/streaming
over network



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X-ray Diffraction


Detector

X-ray source
Q
^i
k

^f
k

Specimen


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Volume Ray Casting
Eye/camera


image

Volume

ray



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Volume Ray Casting of
Scattered Point Data


Eye/camera

Image

Ray
Bounding box



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Interpolation

r2

r3
r1



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Finding Neighbors




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Optimizations
• Empty space skipping
• Early ray termination
• Filtering

C
B
A

C
B
A



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GPU Implementation
•
•
•
•


CUDA C, almost same code
One thread for each ray/pixel
Remove recursion (for older hardware)
Texture memory



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Multi GPU


• Load distribution challenging
– Different hardware
– Different amount of work per thread (ray/pixel)

• Use previous image to divide work for next
• Ray length as proxy for amount of work



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Results




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Results




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Results




55

Results




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Results




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Results




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Results




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Questions?



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Thank yous to:


HPC-Lab Post Docs
and grad. students!
@ SC´07

Spring 2007

Spring 2010
Spring 2009



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Modeling Heterogeneous Systems
“Optimized Barriers for
Heterogeneous Systems
Using MPI”

Jan Christian Meyer
PhD Student
Finishing summer 2011

(to be presented at
IEEE IPDPS 2011, HCW)



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Dealing with bandwidth issues:
Compression of Large Seismic
Datasets on GPU (Aqrawi & Elster IPDPS 2011)




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Motivation


Locality & I/O – challenge for data intensive algorithms
Look at techniques for reducing Mem. Bandwidth
– Hardware: HDD, SSD
– Compression: JPEG, MPEG, MP3 ...

–

Explore GPU compression capabilities

Seismic filtering process
–
Transform coding works well for signal data*
* [H.S.Malvar 1992], [L.C.Duval 2000], [C.Larsen 2006], [D.Haugen 2009]



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Results GPU acceleration
Execution time comparison to FERMI arcitechture
700

600

Execution time (s)

500
Intel i7 Single
Intel i7 Quad
Nvidia Tesla c1060
Nvidia Tesla c2050

400

300

200

100

0
DCT 3D

DCT AAN 3D

LOT 1D

Algorithm



3D AAN(GPU)

2D AAN(GPU)

1D AAN(GPU)

2D DCT (GPU)

1D DCT (GPU)

3D AAN(Quad)

2D AAN(Quad)

1D AAN(Quad)

2D DCT (Quad)

I/O Speedup HDD

1D DCT (Quad)

3D AAN (Single)

2D AAN (Single)

1D AAN (Single)

2D DCT (Single)

1D DCT (Single)

Huffman (GPU)

Huffman (Quad)

Huffman (Single)

RLE(Quad)

RLE (Single)

I/O speed up compa red to pla tform

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Results I/O Speedup
I/O Speedup SSD

7

6

5

4

3

2

1

0

Compression algor ithm


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Summary Compression


–
–

When optimizing for I/O need efficent compression rate
AND fast compression algorithm
Compression can give up to:
– 6.2 I/O speedup on HDD (70MB/s)
– 3.9 I/O speedup on SSD (140MB/s)

–

Achieved through
– Transform coding
– CPU & GPU co-op
– Asynch I/O

–
–

Predictive model accurate within 5%
Seismic compression library



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