Accelerating High Performance Computing

http://www.nvidia.com/tesla

Marco Piattelli

Channel sales

South Europe

2

The ‘Super’ Computing Company SuperPhones to SuperComputers

3

c

VISUALIZATION

PARALLEL

COMPUTING PERSONAL

COMPUTING

4

CUDA GPU

Hundreds of parallel cores

CPU

Several sequential cores

CUDA GPU Accelerates Computing Choose the Right Processor for the Right Job

5

146X

Medical Imaging

U of Utah

36X

Molecular Dynamics

U of Illinois, Urbana

18X

Video Transcoding

Elemental Tech

50X

Matlab Computing

AccelerEyes

100X

Astrophysics

RIKEN

149X

Financial Simulation

Oxford

47X

Linear Algebra

Universidad Jaime

20X

3D Ultrasound

Techniscan

130X

Quantum Chemistry

U of Illinois, Urbana

30X

Gene Sequencing

U of Maryland

GPUs Accelerate Science

6

#2 : Tianhe-1A 7168 Tesla GPU’s 2.5 PFLOPS

#4 : Nebulae 4650 Tesla GPU’s 1.2 PFLOPS

We not only created the world's fastest computer, but also implemented a heterogeneous computing architecture incorporating CPU and GPU, this is a new innovation. ” Premier Wen Jiabao

Public comments acknowledging Tianhe-1A

“

#5 : Tsubame 2.0 4224 Tesla GPU’s 1.194 PFLOPS

Tesla GPUs Power 3 of Top 5 Supercomputers

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Titan at Oak Ridge

World’s Top Open Science Computing Research Facility

2x Faster, 3x More Energy Efficient

than Current #1 (K Computer)

18,000 Tesla GPUs

20+ PetaFlops

~90% of flops from GPUs

8 Source: ORNL

S3D Model Combusion

for higher efficiency fuels & engines

LAMMPS Model biofuels. Reduce carbon

emissions & dependence on

foreign oil

CAM-SE Model global climate

change & explore mitigation strategies

Denovo Simulate radiation transport for safe,

clean, fusion energy

Titan Will Have Huge Societal Impact

9

CUDA By the Numbers:

CUDA Capable GPUs >375,000,000

Toolkit Downloads >1,000,000

Active Developers >120,000

Universities Teaching CUDA >500

New CUDA Toolkit Downloads / Hour 98

10

Workstations Servers & Blades

Tesla Data Center & Workstation GPU Solutions

Tesla M-series GPUs M2090 | M2075/0 | M2050

Tesla C-series GPUs C2075/0 | C2050

M2090 M2075/0 M2050

Cores 512 448 448

Memory 6 GB 6 GB 3 GB

Memory bandwidth

(ECC off) 177.6 GB/s 150 GB/s 148.8 GB/s

Peak

Perf

Gflops

Single

Precision 1331 1030 1030

Double

Precision 665 515 515

C2075/0 C2050

448 448

6 GB 3 GB

148.8 GB/s 148.8 GB/s

1030 1030

515 515

11

Getting Started with GPUs

TRY

Try CUDA 4.0 on your Notebook or

Desktop with CUDA Enabled GPU

DEVELOP

Optimize HPC Apps on Compute

Workstation with Tesla GPUs

DEPLOY

Run apps in production with GPU

Compute Cluster

12

Rich Toolchain & Ecosystem for Fast Ramp-up on GPUs

Debuggers & Profilers

cuda-gdb NV Visual Profiler

Parallel Nsight Visual Studio

Allinea TotalView

MATLAB Mathematica NI LabView

pyCUDA

Numerical Packages

C C++

Fortran OpenCL

DirectCompute Java

Python

GPU Compilers

PGI Accelerator CAPS HMPP

mCUDA OpenMP

Parallelizing Compilers

BLAS FFT

LAPACK NPP

Sparse Imaging

RNG

Libraries

OEM Solution Providers GPGPU Consultants & Training

ANEO GPU Tech

13

Programming GPUs

14

Enabling Developer Ecosystem

Tools & Libraries

TotalView Debugger

Thrust C++ Template Lib

R-Stream

Reservoir Labs

NVIDIA NPP Perf

Primitives

Bright Cluster Manager

CAPS HMPP

PBSWorks

PGI CUDA Fortran

GPU Packages For R Stats

Pkg

IMSL

TauCUDA Perf Tools

pyCUDA

Platform LSF Cluster

Manager

MAGMA

MOAB

Adaptive Computing

Torque

Adaptive Computing

EMPhotonics CULAPACK

NVIDIA Video

Libraries CUDA C/C++

Parallel Nsight Vis Studio IDE

Allinea DDT Debugger

ParaTools VampirTrace

PGI Accelerators

Acceleware EM Library

Open CV CUDA Beta

CUDA X86

GPU.Net

C++-AMP

Available NVIDIA

15

3 Ways to Accelerate Applications

Libraries Directives Programming

Languages

Applications

Easiest Approach for 2x to 10x

Acceleration

Maximum

Performance

16

NVIDIA cuBLAS NVIDIA cuRAND NVIDIA cuSPARSE NVIDIA NPP

Vector Signal Image Processing

GPU Accelerated Linear Algebra

Matrix Algebra on GPU and Multicore NVIDIA cuFFT

C++ STL Features for CUDA

Sparse Linear Algebra

Building-block Algorithms for CUDA IMSL Library

GPU Accelerated Libraries “Drop-in” Acceleration for Your Applications

17

Directives: Simple Hints for the Compiler

main() {

…

<serial code>

…

#pragma acc region

{

<compute intensive code>

}

…

}

CPU GPU

Your original

C/Fortran code

Compiler hints

Compiler Parallelizes code

Works on multicore CPUs &

many core GPUs

Open Standard: OpenACC

Compiler

Hint

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OpenACC: Open Parallel Programming Standard

Buddy Bland Titan Project Director

Oak Ridge National Lab

Dr. Kerry Black Associate Professor,

University of Melbourne

Easy Open Safe

19

Directives or CUDA C/C++/Fortran

Develop your own

parallel algorithms

Automatically parallelize

your loops

CUDA

C/C++/Fortran

20

Opening the CUDA Platform

CUDA Compiler Source for

Researchers & Tools Vendors

Enables

New Language Support

New Processor Support

CUDA Compiler LLVM

C C++ Fortran

New Language Support

PGI NVIDIA

x86 CPUs

NVIDIA GPUs

New Processor Support

21

Opening the CUDA Platform

Liszt

22

Moving Forward: Standards & Compilers

MPI

23

PGI CUDA x86 CUDA Now Available for CPUs and GPUs

Single CUDA

C / C++

Codebase

NVIDIA C / C++ Compiler

PGI CUDA X86 Compiler

C / C++ Support

GPU

CPU

24

CUDA GPU Roadmap

16

2

4

6

8

10

12

14

DP G

FLO

PS p

er

Watt

2007 2010 2012 2014

Tesla Fermi

Kepler

Maxwell

25

CUDA for ARM Development Kit

Research development board

Quad-core ARM based NVIDIA Tegra 3 processor

NVIDIA CUDA GPU

Gigabit Ethernet

CUDA software development kit

Available: 1H 2012

SECO Hardware Development Kit

CUDA GPU Tegra ARM CPU

http://www.secoqseven.com/en/item/secocq7-mxm/

26

CUDA Everywhere:

Supercomputers to Superphones

27

Thank You, Questions ?