invited talk 12:

Invited Talk 12:“Discovering Energy-Efficient High-Performance Computing Systems? WSU CAPPLab may help!”

ICIEV 2014Dhaka University, Bangladesh

May 23, 2014

Presented by:

Dr. Abu AsaduzzamanAssistant Professor in Computer Architecture and Director of CAPPLabDepartment of Electrical Engineering and Computer Science (EECS)

Wichita State University (WSU), USA

Dr. Zaman 2

“Discovering Energy-Efficient High-Performance Computing Systems? WSU CAPPLab may help!”

Outline ►■ Introduction

Single-Core to Multicore Architectures

■ Performance Improvement Simultaneous Multithreading (SMT) (SMT enabled) Multicore CPU with GPUs

■ Energy-Efficient Computing Dynamic GPU Selection

■ 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

A thread is a running “process”

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Introduction

Thread 1: Integer (INT) Operation(Pipelining Technique)

1: InstructionFetch

2: InstructionDecode

(3) Operand(s)Fetch

4: IntegerOperation

ArithmeticLogicUnit

(5) ResultWrite Back

FloatingPointOperation

Thread 1: Integer Operation

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Introduction

Thread 2: Floating Point (FP) Operation

(Pipelining Technique)

InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack


Thread 2: Floating Point Operation

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Introduction

Threads 1 and 2: INT and FP Operations


InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack




POSSIBLE?

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Performance

Threads 1 and 2: INT and FP Operations


InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack




POSSIBLE?

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Performance Improvement

Threads 1 and 3: Integer Operations

InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack




POSSIBLE?

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Threads 1 and 3: Integer Operations

(Multicore)

InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack


InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack




POSSIBLE?

Core 1

Core 2

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Threads 1, 2, 3, and 4: INT & FP Operations

(Multicore)InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack


InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack


Core 2





POSSIBLE?

Core 1

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More Performance?

Threads 1, 2, 3, and 4: INT & FP Operations

(Multicore)InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack


InstructionFetch

InstructionDecode

Operand(s)Fetch

IntegerOperation

ArithmeticLogicUnit

ResultWriteBack


Core 2





POSSIBLE?

Core 1

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■ Performance Improvement Simultaneous Multithreading (SMT) (SMT enabled) Multicore CPU with GPUs



■ 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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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

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Identify Challenges■ Sequential data-independent problems

C[] A[] + B[]♦ C[5] A[5] + B[5]

A’[] A[]♦ A’[5] A[5]

SMT capable multicore processor; CUDA/GPU Technology

Core 1 Core 2


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■ CUDA/GPU Programming■ GP-GPU Card

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


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CPU-GPU Technology■ Tasks/Data exchange mechanism

Serial Computations – CPU Parallel Computations - GPU

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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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(Step 2) CPU Sends function parameters and instructions to GPU

CUDA API:

myFunc<<<Blocks, Threads>>>(parameters)

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(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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Case Study 1 (data independent computation without GPU/CUDA)

■ Matrix Multiplication

Matrices Systems

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Case Study 1 (data independent computation without GPU/CUDA)

■ Matrix Multiplication

Execution Time Power Consumption

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Case Study 2 (data dependent computation without GPU/CUDA)

■ Heat Transfer on 2D Surface

Execution Time Power Consumption

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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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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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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

C[] A[] + B[]♦ C[5] A[5] + B[5]

A’[] A[]♦ A’[5] A[5]

SMT capable multicore processor; CUDA/GPU Technology

■ Sequential data-dependent problems B’[] B[]

♦ B’[5] {B[4], B[5], B[6]}

Communication needed♦ Core 1 and Core 2

Core 1 Core 2

Core 1 Core 2


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Develop Solutions■ Task Regrouping

Create threads

■ Data Regrouping Regroup data Data for each thread

Threads with G2s first Then, threads with G1s

(Step 2 of 5) CPU copies data to GPUOn CUDA API:

cudaMemCpy()


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Assess the Solutions■ What is the Key?■ Synchronization

With synchronization Without synchronization

♦ Fast Vs. Accuracy

Threads with G2s first Then, threads with G1s

(Step 2 of 5) CPU copies data to GPUOn CUDA API:

cudaMemCpy()


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■ Performance Improvement Simultaneous Multithreading (SMT) (SMT enabled) Multicore CPU with GP-GPU



■ Discussion

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Kansas Unique Challenge■ Climate and Energy

Protect environment from harms due to climate change

Save natural energy

Energy-Efficient Computing

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“Power” Analysis■ CPU with multiple GPU

GPU usages vary

■ Power Requirements NVIDIA GTX 460 (336-core) - 160W [1] Tesla C2075 (448-core) - 235W [2] Intel Core i7 860 (4-core, 8-thread) -

150-245W [3, 4]

■ Dynamic GPU Selection Depending on

♦ the “tasks”/threads

♦ GPU usages

CPU

GPU

GPUGPU


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CPU-to-GPU Memory Mapping■ GPU Shared Memory

Improves performance CPU to GPU global memory GPU global to shared

■ Data Regrouping CPU to GPU global memory


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Integrate Research into Education■ CS 794 – Multicore Architectures Programming

Multicore Architecture Simultaneous Multithreading Parallel Programming

Moore’s law Amdahl’s law Gustafson’s law Law of diminishing returns Koomey's law

Teaching Low-Power HPC Systems

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■ Performance Improvement Simultaneous Multithreading (SMT) (SMT enabled) Multicore CPU with GP-GPU



■ Discussion

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WSU CAPPLab

CAPPLab■ Computer Architecture & Parallel Programming

Laboratory (CAPPLab) Physical location: 245 Jabara Hall, Wichita State University URL: http://www.cs.wichita.edu/~capplab/ E-mail: [email protected]; [email protected] Tel: +1-316-WSU-3927

■ Key Objectives Lead research in advanced-level computer architecture, high-

performance computing, embedded systems, and related fields. Teach advanced-level computer systems & architecture, parallel

programming, and related courses.

http://www.cs.wichita.edu/~capplab/

mailto:[email protected]

mailto:[email protected]

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WSU CAPPLab

“People First”■ Students

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)

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WSU CAPPLab

Resources■ Hardware

3 CUDA Servers – CPU: Xeon E5506, 2x 4-core, 2.13 GHz, 8GB DDR3; GPU: Telsa C2075, 14x 32 cores, 6GB GDDR5 memory

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

ICIEV-2014; Dhaka, Bangladesh; 2014


Thank You!

QUESTIONS?

Contact: Abu AsaduzzamanE-mail: [email protected]

Phone: +1-316-978-5261http://www.cs.wichita.edu/~capplab/

invited talk 12:

Documents

multicore cpu

performance improvementthreads

multicore architecturecourtesy

ultimate performance

integer operationdr

wsu capplab

computer architecture

integer operationpossible