Parallel computing with Gpu


Published on

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Parallel computing with Gpu

  1. 1. Rohit khatana Parallel Computing With GPU Rohit Khatana 4344 Seminar guide Prof. Aparna Joshi ARMY INSTITUE OF TECHNOLOGY
  2. 2. Rohit khatana Content 1.What is parallel computing? 2.Gpu 3.CUDA 4.Application
  3. 3. Rohit khatana 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.
  4. 4. Rohit khatana For example
  5. 5. Rohit khatana
  6. 6. 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
  7. 7. Modern GPU has more ALU’s
  8. 8. 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
  9. 9. 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)
  10. 10. 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)
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. Memory Model of GPU
  16. 16. Basic Architecture of GPU
  17. 17. 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.
  18. 18. 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.
  19. 19. 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.
  20. 20. 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)
  21. 21. 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
  22. 22. 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);
  23. 23. 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]); }
  24. 24. Programming Model
  25. 25. GPU vs CPU Code
  26. 26. 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.
  27. 27. 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
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.