Evolution of the NVIDIA GPU Architecture

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Programming Requirements∗ Explicit Memory Operations to allocate and copy datafrom the CPU to GPU∗ Some exceptions do apply∗ All kernels execute asynchronously of the CPU∗ Explicit synchronization barriers between the processors
Synchronization and Performance∗ To meet data dependencies,∗ Synchronization Primitives∗ __syncthreads() –Synchronizes all threads in a block∗ Atomic Operations –Depending on compute/CUDAversion, these are possible on global and shared memory∗ Performance is dictated by memory operations andsynchronization cost∗ Memory Coalescence∗ Warp Divergence
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 and 22: Use for Computation∗ Historically
Page 23: Example - Vector AdditionCfor( int
Page 28 and 29: Conclusions∗ GPUs are massively p

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