Invited Talk 5: “Discovering Energy-Efficient High- Performance Computing Systems? WSU CAPPLab may help!” ICIEV 2014 Dhaka, Bangladesh Dr. Abu Asaduzzaman, Assistant Professor and Director Wichita State University (WSU) Computer Architecture & Parallel Programming Laboratory (CAPPLab) Wichita, Kansas, USA May 23, 2014
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Invited Talk 5: “Discovering Energy-Efficient High-Performance Computing Systems? WSU CAPPLab may help!” ICIEV 2014 Dhaka, Bangladesh Dr. Abu Asaduzzaman,
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■ CAPPLab “People First” Resources Research Grants/Activities
■ Discussion
QUESTIONS? Any time, please!
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Introduction
Single-Core to Multicore Architecture■ History of Computing
Word “computer” in 1613 (this is not the beginning) Von Neumann architecture (1945) – data/instructions memory Harvard architecture (1944) – data memory, instruction memory
■ Single-Core Processors In most modern processors: split CL1 (I1, D1), unified CL2, … Intel Pentium 4, AMD Athlon Classic, …
■ Popular Programming Languages C, …
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(Single-Core to) Multicore Architecture
Courtesy: Jernej Barbič, Carnegie Mellon University
Input Process/Store Output
Multi-tasking Time sharing (Juggling!)
Cache not shown
Introduction
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Single-Core “Core”
Introduction
a single core
Courtesy: Jernej Barbič, Carnegie Mellon University
■ CAPPLab “People First” Resources Research Grants/Activities
■ Discussion
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Parallel/Concurrent Computing
Parallel Processing – It is not fun!Let’s play a game: Paying the lunch bill together
Started with $30; spent $29 ($27 + $2)Where did $1 go?
Friend Before Eating
Total Bill
Return Tip After Paying
A $10 $1
B $10 $25 $5 $2 $1
C $10 $1
Total $30 $2
Total Spent
$9
$9
$9
$27
SMT enabled Multicore CPU with Manycore GPU for Ultimate Performance!
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Performance Improvement
Simultaneous Multithreading (SMT)■ Thread
A running program (or code segment) is a process Process processes / threads
■ Simultaneous Multithreading (SMT) Multiple threads running in a single-processor at the same time Multiple threads running in multiple processors at the same time
■ Multicore Programming Language supports OpenMP, Open MPI, CUDA, …C
A GPU card with 16 streaming multiprocessors (SMs)
Inside each SM:• 32 cores
• 64KB shared memory
• 32K 32bit registers
• 2 schedulers
• 4 special function units
■ CUDA GPGPU Programming Platform
Performance Improvement
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Performance Improvement
CPU-GPU Technology■ Tasks/Data exchange mechanism
Serial Computations – CPU Parallel Computations - GPU
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Performance Improvement
GPGPU/CUDA Technology■ The host (CPU) executes a kernel in GPU in 4 steps
(Step 1) CPU allocates and copies data to GPUOn CUDA API:
cudaMalloc()cudaMemCpy()
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Performance Improvement
GPGPU/CUDA Technology■ The host (CPU) executes a kernel in GPU in 4 steps
(Step 2) CPU Sends function parameters and instructions to GPU
CUDA API:
myFunc<<<Blocks, Threads>>>(parameters)
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Performance Improvement
GPGPU/CUDA Technology■ The host (CPU) executes a kernel in GPU in 4 steps
(Step 3) GPU executes instruction as scheduled in warps
(Step 4) Results will need to be copied back to Host memory (RAM) using cudaMemCpy()
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Performance Improvement
Case Study 1 (data independent computation without GPU/CUDA)
■ Matrix Multiplication
Matrices Systems
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Performance Improvement
Case Study 1 (data independent computation without GPU/CUDA)
■ Matrix Multiplication
Execution Time Power Consumption
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Performance Improvement
Case Study 2 (data dependent computation without GPU/CUDA)
■ Heat Transfer on 2D Surface
Execution Time Power Consumption
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Performance Improvement
Case Study 3 (data dependent computation with GPU/CUDA)
■ Fast Effective Lightning Strike Simulation The lack of lightning strike protection for the composite materials
limits their use in many applications.
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Performance Improvement
Case Study 3 (data dependent computation with GPU/CUDA)
■ Fast Effective Lightning Strike Simulation■ Laplace’s Equation■ Simulation
CPU OnlyCPU/GPU w/o shared memoryCPU/GPU with shared memory
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Performance Improvement
Case Study 4 (MATLAB Vs GPU/CUDA)■ Different simulation modelsTraditional sequential programCUDA program (no shared memory)CUDA program (with shared memory)Traditional sequential MATLABParallel MATLAB
CUDA/C parallel programming of the finite difference method based Laplace’s equation demonstrate up to 257x speedup and 97% energy savings over a parallel MATLAB implementation while solving a 4Kx4K problem with reasonable accuracy.
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Identify More Challenges■ Sequential data-independent problems
Kishore Konda Chidella, PhD Student Mark P Allen, MS Student Chok M. Yip, MS Student Deepthi Gummadi, MS Student
■ Collaborators Mr. John Metrow, Director of WSU HiPeCC Dr. Larry Bergman, NASA Jet Propulsion Laboratory (JPL) Dr. Nurxat Nuraje, Massachusetts Institute of Technology (MIT) Mr. M. Rahman, Georgia Institute of Technology (Georgia Tech) Dr. Henry Neeman, University of Oklahoma (OU)
2 CUDA PCs – CPU: Xeon E5506, … Supercomputer (Opteron 6134, 32 cores per node, 2.3 GHz, 64
GB DDR3, Kepler card) via remote access to WSU (HiPeCC) 2 CUDA enabled Laptops More …
■ Software CUDA, OpenMP, and Open MPI (C/C++ support) MATLAB, VisualSim, CodeWarrior, more (as may needed)
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WSU CAPPLab
Scholarly Activities■ WSU became “CUDA Teaching Center” for 2012-13
Grants from NSF, NVIDIA, M2SYS, Wiktronics Teaching Computer Architecture and Parallel Programming
■ Publications Journal: 21 published; 3 under preparation Conference: 57 published; 2 under review; 6 under preparation Book Chapter: 1 published; 1 under preparation
■ Outreach USD 259 Wichita Public Schools Wichita Area Technical and Community Colleges Open to collaborate
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WSU CAPPLab
Research Grants/Activities■ Grants
WSU: ORCA NSF – KS NSF EPSCoR First Award M2SYS-WSU Biometric Cloud Computing Research Grant Teaching (Hardware/Financial) Award from NVIDIA Teaching (Hardware/Financial) Award from Xilinx
■ Proposals NSF: CAREER (working/pending) NASA: EPSCoR (working/pending) U.S.: Army, Air Force, DoD, DoE Industry: Wiktronics LLC, NetApp Inc, M2SYS Technology