Parallel computing with Gpu
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Parallel computing with Gpu

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Parallel computing with Gpu Parallel computing with Gpu Presentation Transcript

  • Rohit khatana Parallel Computing With GPU Rohit Khatana 4344 Seminar guide Prof. Aparna Joshi ARMY INSTITUE OF TECHNOLOGY
  • Rohit khatana Content 1.What is parallel computing? 2.Gpu 3.CUDA 4.Application
  • 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.
  • Rohit khatana For example
  • Rohit khatana
  • 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