Parallel computing with Gpu

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Parallel Computing With GPU

Rohit Khatana4344

Seminar guideProf. Aparna Joshi

ARMY INSTITUE OF TECHNOLOGY

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Content

1.What is parallel computing?

2.Gpu

3.CUDA

4.Application

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What is Parallel Computing?

Performing or Executing a task/program on more than one machine or processor.

In simple way dividing a job in a group.

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For example

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What kind of processors will we build?

(major design constraint: power)

Cpu: - Complex Control Hardware

Flexibility + Performance

Expensive in Terms of Power

GPU: - Simpler Control Hardware

More H/W for Computation

Potentially More power Efficient (ops/watt)

More Restrictive Programming Model

Modern GPU has more ALU’s

Graphics Logical Pipeline• The GPU receives geometry information

from the CPU as an input and provides a picture as an output

• Let’s see how that happens

Host Interface

• The host interface is the communication bridge between the CPU and the GPU

• It receives commands from the CPU and also pulls geometry information from system memory

• It outputs a stream of vertices in object space with all their associated information (normals, texture coordinates, per vertex color etc)

Vertex Processing• The vertex processing stage receives vertices from the

host interface in object space and outputs them in screen space

• This may be a simple linear transformation, or a complex operation involving morphing effects

• No new vertices are created in this stage, and no vertices are discarded (input/output has 1:1 mapping)

Triangle Setup

• In this stage geometry information becomes raster information (screen space geometry is the input, pixels are the output)

• Prior to rasterization, triangles that are backfacing or are located outside the viewing frustrum are rejected

Triangle Setup

• A fragment is generated if and only if its center is inside the triangle

• Every fragment generated has its attributes computed to be the perspective correct interpolation of the three vertices that make up the triangle

Fragment Processing

• Each fragment provided by triangle setup is fed into fragment processing as a set of attributes

(position, normal, texcoord etc), which are used to compute the final color for this pixel

• The computations taking place here include texture mapping and math operations

Memory Interface

• Fragments provided by the last step are written to the framebuffer.

• Before the final write occurs, some fragments are rejected by the zbuffer, stencil and alpha tests

Memory Model of GPU

Basic Architecture of GPU

CUDA(compute unified device Architecture)

• CUDA is a parallel computing platform and programming model.

• Created by NVIDIA and implemented by the GPUs that they produce.

CUDA

• CUDA gives developers access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs.

• CUDA supports standard programming languages , including C++,python , Fortran.

Programming Model

• Threads are organized into blocks.

• Blocks are organized into a grid.

• A multiprocessor executes one block at a time.

• A warp is the set of threads executed in parallel.

• 32 threads in a warp.

Typical CUDA/GPU Program

1. CPU allocates storage on GPU (cudaMalloc).

2. CPU copies input data from CPU GPU (cudaMemcpy).

3. CPU launches kernel on GPU to process the data.(Kernel function<<<no of threads>>>(parameter))

4. CPU copies results back to CPU from GPU (cudaMemcpy)

simply squaring the elements of an array

__global__ void square(float * d_out, float * d_in){

// Todo: Fill in this function

int idx = threadIdx.x;

float f = d_in[idx];

d_out[idx] = f*f

}

theadIdx.x =gives the current thread number

GPU/CUDA programming

Main program

int main(int argc, char **argv){

……………………

…………………….

float h_out[ARRAY_SIZE];

//declare GPU pointer

float * d_in;

float * d_out;

// allocate GPU memory

cudaMalloc( (void*) &d_in, ARRAY_BYTES);

cudaMalloc( (void*) &d_out, ARRAY_BYTES);

Main program(cont.)

// transfer the array to the GPU

cudaMemcpy(d_in, h_in, ARRAY_BYTES, cudaMemcpyHostToDevice);

// launch the kernel

square<<<1, ARRAY_SIZE>>>(d_out, d_in);

// copy back the result array to the CPU

cudaMemcpy(h_out, d_out, ARRAY_BYTES, cudaMemcpyDeviceToHost);

// print out the resulting array

for (int i =0; i < ARRAY_SIZE; i++) {

printf("%f", h_out[i]);

}

Programming Model

GPU vs CPU Code

Conclusion• GPU computing is a good choice for fine-

grained data-parallel programs with limited communication

• GPU computing is not so good for coarse-grained program with a lot of communication

• The GPU has become a co-processor to the CPU.

References

• 1.[‘IEEE’] Accelerating image processing capability using graphics processors Jason. Dalea, Gordon. Caina, Brad. ZellbaVision4ce Ltd. Crowthorne Enterprise Center, Crowthorne, Berkshire, UK, RG45 6AWbVision4ce LLC Severna Park, USA, MD2114

•  

• 2.Udacity cs344,Intro to parallel Programming with GPU

• 3.Wikipedia

• 4.Nividia docs