Evolution of the NVIDIA GPU Architecture

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CUDA Programming∗ Extensions to the C language∗ With some C++ support∗ Programming Support∗ Windows –Visual Studio∗ Linux/Mac – Eclipse∗ Programming paradigm where each computationtake place on a separate thread∗ Requires NVIDIA GPU for acceleration∗ Simulators are used for research purposes
Example – Vector AdditionCfor( int i = 0; i < SIZE; ++i ) {c[ i ] = a[ i ] + b[ i ];}CUDA__global__ void addVectors( float* a, float* b,float* c ) {int id = threadIdx.x;if( id < SIZE ) {c[ id ] = a[ id ] + b[ id ];}}
Page 4: Graphics Pipeline
Page 8 and 9: Streaming Multiprocessor Architectu
Page 10 and 11: Architectural Memory Hierarchy
Page 12 and 13: Fermi Improvements∗ Increase the
Page 15 and 16: Kepler SM Design15
Page 17 and 18: Kepler Memory Architecture∗ Share
Page 19 and 20: Kepler Hardware Features∗ Dynamic
Page 21: Use for Computation∗ Historically
Page 25: Synchronization and Performance∗
Page 28 and 29: Conclusions∗ GPUs are massively p

Evolution of the NVIDIA GPU Architecture

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