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GPU Research Capabilities at Seneca

FSOSS

2012-10-26 Dr. Chris Szalwinski

Professor School of Information and Communication Technology Seneca College, Toronto, Canada

A Fresh Initiative

From Some Personal History To Heterogeneous Computing

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The 80287

A Fresh Initiative

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Floating-Point Co-Processor (1985) A Fresh Initiative

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ATI 3D Rage II Co-Processor (1996) A Fresh Initiative

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A Paradigm Shift In Programming

A Fresh Initiative

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The Turn Towards Concurrency Paradigm Shift

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Paradigm Shift

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Can still increase

 transistor density – but it's getting more expensive

Paradigm Shift

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Can still increase

 transistor density – but it's getting more expensive

Can't increase

 processor frequencies < 10 GHz chips

Paradigm Shift

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Can still increase

 transistor density – but it's getting more expensive

Can't increase

  processor frequencies < 10 GHz chips power consumption – can't melt chips

Paradigm Shift

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Paradigm Shift Can still increase

 transistor density – but it's getting more expensive

Can't increase

  processor frequencies < 10 GHz chips power consumption – can't melt chips

The Free Lunch is Over

 we can't just wait for improvement like we did before  we need new routes to improvement 12

Paradigm Shift

Use Different Computational Units For Distinctly Different Tasks

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Heterogeneous Computing Intel Core i7 (2008), NVIDIA GeForce GTX580 (2010)

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Heterogeneous Computing

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Heterogeneous Computing

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Serial processing

+ Heterogeneous Computing

Parallel processing 17

Heterogeneous Computing NVIDIA many-core GPUs vs Intel multi-core CPUs

 Floating point operations per sec (GFLOP/s)  Memory bandwidth (GB/s) 18

Industry Momentum STI (Sony + Toshiba + IBM)

 Broadband Cell Processor – CPU + GPU on one chip 19

Industry Momentum STI (Sony + Toshiba + IBM)

 Broadband Cell Processor – CPU + GPU on one chip

Intel

 Xeon Phi – MIC (Many Integrated Core) 20

Industry Momentum STI (Sony + Toshiba + IBM)

 Broadband Cell Processor – CPU + GPU on one chip

Intel

 Xeon Phi – MIC (Many Integrated Core)

AMD

 APUs (Fusion) – CPU + GPU on a single chip 21

Industry Momentum STI (Sony + Toshiba + IBM)

 Broadband Cell Processor – CPU + GPU on one chip

Intel

 Xeon Phi – MIC (Many Integrated Core)

AMD

  APUs (Fusion) – CPU + GPU on a single chip HSA Foundation (2012) – AMD + ARM + TI + Imagination + MediaTek + Samsung + Ateris + Multicore Ware + Apical + Sonics + Symbio + Vivante 22

Industry Momentum STI (Sony + Toshiba + IBM)

 Broadband Cell Processor – CPU + GPU on one chip

Intel

 Xeon Phi – MIC (Many Integrated Core)

AMD

  APUs (Fusion) – CPU + GPU on a single chip HSA Foundation (2012) – AMD + ARM + TI + Imagination + MediaTek + Samsung + Ateris + Multicore Ware + Apical + Sonics + Symbio + Vivante  Radeon – Discrete GPUs 23

Industry Momentum STI (Sony + Toshiba + IBM)

 Cell Processor – CPU + GPU on one chip

Intel

 Xeon Phi – MIC (Many Integrated Core)

AMD

  APUs (Fusion) – CPU + GPU on a single chip HSA Foundation (2012) – AMD + ARM + TI + Imagination + MediaTek + Samsung + Ateris + Multicore Ware + Apical + Sonics + Symbio + Vivante  Radeon – Discrete GPUs

NVIDIA – Discrete GPUs

 GeForce (digital gaming)   Quadro (engineering workstations - graphics) Tesla (scientific computations – double precision) 24

Industry Momentum Discrete GPUs - Add-in board shipments

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Predictions

Industry Momentum

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Industry Predictions Computer Graphics Market 1974-2015

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Industry Predictions Computer Graphics Market 1974-2015

 Traditional processors + low-cost graphics processors enable combinations of science and entertainment 28

Industry Predictions Embedded Graphics Processors (EGPs) are killing off Integrated Graphics Processors (IGPs)

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Industry Predictions Embedded Graphics Processors (EGPs) are no threat to Discrete Graphics

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Programming Heterogeneous Computers Concurrency-Oriented Programming

Core Languages

   Fortran C C++ 31

Programming Heterogeneous Computers Concurrency-Oriented Programming (COP)

 

Core Languages

   Fortran C C++

Extensions for COP

   Cilk Plus (Intel) OpenCL (Khronos Group – AMD and HSA) CUDA  C/C++ (NVIDIA)  Fortran 2008, C-x86 (PGI)  DirectCompute (Microsoft) 32

Programming Heterogeneous Computers CUDA Teaching Centers in Ontario

McMaster University (2010)

 High Performance Parallel Computing on Graphical Processing Units – ECE709 – part of Master's Degree 

University of Toronto (2011)

 Special Topics in Software Engineering: Programming Massively Parallel Graphics Processors – ECE1724H – part of Master's Degree 

Seneca College (2012)

 Introduction to Parallel Programming – Professional Option – GPU610/DPS915 – CPA Diploma and BSD Degree 33

Programming Heterogeneous Computers

School of Information and Communications Technology (ICT) Our Capabilities and Plans

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ICT Facilities Fully Equipped Teaching Classroom and Lab

 40 seats  38 CUDA enabled desktops with GTX480s (480 cores)

Maximus Workstation

 Quadro 600 for visualization  Tesla C2075 for computation

SCI-Net Research

 Accelerator Research Cluster – research testbed  8 x [2 Intel Xeon X5550 + 2 NVIDIA Tesla M2070] 35

The 80287

ICT Facilities

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ICT Courses Introductory Course – Student Skill Set

 Solid tested background in both C and C++  Profile for computationally intensive code  Move critical code to the GPU using CUDA  Optimize to hide memory latency with computations

Programmer Training Workshops – on demand Advanced Course – (in the planning stage)

 Interactive Real-Time Computations + Visualization  Parallelizing Fortran Applications  OpenGL, DirectX Graphics Interoperability 37

Areas of Interest or Domain Expertise

    Big Data – Geocomputation Cognition – Cognitive Tutors Intrusion Detection – Information Security Finite Element Analysis – Soft Matter

ICT Faculty

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Areas of Application (source: NVIDIA)

 Image Processing  Big Data Mining  Gaming  Advertising  Genetics  Quantum Chemistry  Mathematics  Product Design  Scientific Computing  Computational Finance

ICT Scope

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GPU Research Capabilities at Seneca

FSOSS

2012-10-26 Dr. Chris Szalwinski

Professor School of Information and Communication Technology Seneca College, Toronto, Canada