VPU TECHNOLOGY &GPGPU COMPUTING

Arka Ghosh(9007900477a@gmail.com)B.Tech

Computer Science & EngineeringDELIVERED AT

Seacom Engineering College,CSE DeptDATE

7th April’2011

What Is VPU?VPU is Visual Processing Unit it is more generally known as Graphics Processing Unit or GPU. The Graphics Processing Unit is a MASSIVELY PARALAL & MASSIVELY MULTITHREADED microprocessor. • HyBrid Solutions

• NVIDIA SLI• ATI Raedon CROSSFIREX

Why GPU?GPU is used for high performance Computing.Long time ago work of GPU was to offload & accelerate graphics rendering from the CPU, but now a days the scene has changed.GPU has capability to work like a CPU,in some complex computational cases it beats the CPU.

GPU Solutions:-We can get GPU in two forms

1.Integrated GPUIt is integrated on the chipset of MotherBoard.It has low memory bandwidth & its latency time is much more than Dedicated ones.i.e-NVIDIA 730a Chipset provides 8200GT GPU with 540Mhz core.

2.Discrete or Dedicated GPUIt is the most power full form of GPU.it is generally installed on PCIe or AGP port of MotherBoard.It has its own memory module. i.e-ATI Raedon HD 5970 X2 has Compute power of 4.64 TeraFlops with 3200 Stream Processors & 1 Ghz core © Arka Ghosh 2011

What is PPU?PPU is physics processing unit. which specialized for calculation of rigid bodydynamics, soft body dynamics, collision detection, fluid dynamics, hair and clothing simulation, finite elementanalysis, and fracturing of objects.

• LARRABEE

• FUSION

The Main Leader of PPU is AGIA PhysX.It consists of a general purpose RISC core controlling

an array of custom SIMD floating point VLIW processors

working in local banked memories, with a switch-fabric to manage transfers between them. There is no

cache-hierarchy as in a CPU or GPU.

GPUs vs PPUs:-The drive toward GPGPU is making GPUs more and more suitable for the job of a PPU.

ULTIMATE FATE OF GPU:-1.Intel’s LARRABEE2.AMD’s FUSION

© Arka Ghosh 2011

-:INTO THE ARCHITECTURE:-• Use of SPM:-SPM or SCRATCHPAD MEMORY is a high-speed internal memory used for temporary storage of calculations, data, and other work in progress.Inreference to

a microprocessor ("CPU"), scratchpad refers to a special high-speed memory circuit used to hold smallitems of data for rapid retrieval.• EXAMPLE:-• NVIDIA's 8800 GPU running under CUDA provides 16KiB of Scratchpad per thread-bundle when being used for• gpgpu tasks.

• STREAM PROCESSING:• The stream processing paradigm simplifies parallel software and hardware by restricting the parallel computation that can be performed. 1.Uniform Stream.

Applications:-1. Compute Intensity2. Data Parallelism3. Data Locality• Conventional, sequential paradigm Parallel SIMD paradigm, packed registers (SWAR)

for(int el = 0; el < 100; el++) // for each vectorvector_sum(result[el], source0[el], source1[el]);

for(int i = 0; i < 100 * 4; i++)result[i] = source0[i] + source1[i];

© Arka Ghosh 2011

Graphics Pipeline• The graphics pipeline typically accepts some representation of a three-dimensional scene as an input and results in a 2D raster image

as output. OpenGL and Direct3D are two notable graphics pipeline models accepted as widespread industry standards.

• Stages of the graphics pipeline:->1.Transformation2.Per-vertex lighting3.Viewing transformation4.Primitives generation5.Projection transformation6.Clipping7.Viewport transformation8.Scan conversion or rasterization9.Texturing, fragment shading10.Display

• Shader• Shaders are used to program the graphics processing unit (GPU) programmable rendering pipeline, which has mostly superseded the fixed-function

pipeline that allowed only common geometry transformation and pixel-shading functions; with shaders, customized effects can be used.

• <<<Types Of Shader>>>• Vertex shaders.• Pixel shaders• Geometrical shaders

USEFULLNESS OF SHADER:-1.Simplified graphic processing unit pipeline2.Parallel processing

• Programming shaders• We can programe shader by using OpenGL,Cg & Microsoft HLSL.

© Arka Ghosh 2011

GPU CLUSTER•What is Cluster?•GPU CLUSTER•Each node of the cluster is GPU.

1.Homogeneous2.Heterogeneous

ComponentsHardware (Other):-•Interconnector

Software:-1. Operating System2. GPU driver for the each type of GPU present in each cluster node.3. Clustering API (such as the Message Passing Interface, MPI).

..Algorithm mapping

• GPU SWITCHING• Means Switching from one cluster node to another.

• WINDOWS Switching.• LINUX Switching.

© Arka Ghosh 2011

What Is GPGPU?GPGPU stands for general purpose graphics processin unit computing.Using GPU as CPU is the GPGPU computing

NVIDIA CUDA:-It is a GPGPU Computing architecture.It provides heterogeneous computing environment.

• Why GPU Computing?

• To achive high performance computing.• Minimize ERROR• LOW power Consumption..GO GREEN.

NVIDIA FLEXES TESLA MUSCLE

CUDA Kernels and Threads

•Parallel portions of an application are executed on

the device as kernels

•One kernel is executed at a time

•Many threads execute each kernel

•Differences between CUDA and CPU threads

• CUDA threads are extremely lightweight

•CUDA uses 1000s of threads to achieve efficiency

•Multi-core CPUs can use only a few

•Definitions

•Device = GPU

•Host = CPU

•Kernel = function that runs on the device

Data Movement Example

int main(void)

{

float *a_h, *b_h; // host datafloat *a_d, *b_d; // device dataint N = 14, nBytes, i ;

nBytes = N*sizeof(float);

a_h = (float *)malloc(nBytes);b_h = (float *)malloc(nBytes);cudaMalloc((void **) &a_d, nBytes);

cudaMalloc((void **) &b_d, nBytes);

for (i=0, i<N; i++) a_h[i] = 100.f + i;

cudaMemcpy(a_d, a_h, nBytes, cudaMemcpyHostToDevice);

cudaMemcpy(b_d, a_d, nBytes, cudaMemcpyDeviceToDevice);cudaMemcpy(b_h, b_d, nBytes, cudaMemcpyDeviceToHost);

for (i=0; i< N; i++) assert( a_h[i] == b_h[i] );free(a_h); free(b_h); cudaFree(a_d); cudaFree(b_d);

return 0;}

© Arka Ghosh 2011

© Arka Ghosh2011

10-Series Architecture

240 thread processors execute kernel threads

30 multiprocessors, each contains8 thread processors

One double-precision unit

Shared memory enables thread cooperation

ThreadProcessors

Multiprocessor

SharedMemory

Double

Execution ModelSoftware Hardware

Threads are executed by thread processors

Thread

ThreadProcessor

Multiprocessor

Thread blocks are executed on multiprocessors

Thread blocks do not migrate

Several concurrent thread blocks can reside onThreadBlock

...

Grid Device

one multiprocessor - limited by multiprocessorresources (shared memory and register file)

A kernel is launched as a grid of thread blocks

Only one kernel can execute on a device atone time

© Arka Ghosh2011

Tesla Architecture

© Arka Ghosh 2011

Time

GigaThread Hardware Thread SchedulerConcurrent Kernel Execution + Faster Context Switch

Serial Kernel Execution Parallel Kernel Execution

Kernel 1 Kernel 1 Kernel 2

Kernel 2Ker

4

nel

Kernel 3

Kernel 5

Kernel 3

Kernel 4

Kernel 5

Kernel 2

Kernel 2

© Arka Ghosh2011

EXAMPLE:->MATLAB CODE FOR SIMPLE FFT(CPU HOST MODE)

FOR DEVICE( nVidia QUADRO Fx 5200*2)

clear ALL;t1=cputime;x=rand(2^20,1);f=fft(x);t2=cputime;t3=t2-t1;Here t3=0.4056

Clear ALL;t1=cputime;x=rand(2^20,1);gx=gpuArray(x);f=fft(gx);t2=cputime;t3=t2-t1;Here t3=0.006056

clear ALL t1=cputime; x=rand(50);y=rand(50);z=rand(50); a=10;b=20;c=30;d=40;f=a*(x^2)+b*(x*y)+c*(y^3)+d*(z^4); net=feedforwardnet(800);net=trainlm(net,x,f); t2=cputime;t3=t2-t1;

MATLAB code For Simple ANN

For CPU t3=250.2154For GPU t3=122.25

So we can see that The GPU is nearabout 204% efficient than CPU. © Arka Ghosh 2011

CONCLUSION• C for the GPU• Multi-GPU Computing• Massively Multi-threaded Computing Architecture• Compatible with Industry Standard Architectures

• WHERE GPGPU IS USED?• MIT-for educational & Scientific Research Purpose• Stanford University--for educational & Scientific Research Purpose• NCSA (National Center for Supercomputing Applications)• NASA• Machine Learning & AI field• Machine Vision(Mainly Robot Vision)• Tablets

© Arka Ghosh 2011

Acknowledgement

• Mriganka Chakraborty(prof. Secom Engineering College)• Saibal Chakraborty• Dr.Nicolas Pinto .prof. of MIT.-Advanced Supercomputing Dept• T.Halfhill-NVIDIA Corp Developer Guide• GOOGLE

THANK YOU