DirectCompute Optimizations and Best Practices - Nvidia

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Contents • Introduction • Best Practices for GPU Optimization • Parallel Reduction Optimization Example
Why GPUs • GPUs are throughput oriented processors – GPUs a lot of parallel processing units (FLOPs) – Memory latencies are covered with more workload • Problems with a lot of work can achieve good speedups • Must provide enough work to GPUs for efficiency and performance • DirectCompute is an API allowing Compute Shaders on the GPU hardware efficiently
Page 1: DirectCompute Optimizations and Bes
Page 5 and 6: Advantages of DirectCompute • Dir
Page 7 and 8: Parallel Execution Model Thread Gro
Page 9 and 10: Memory Coalescing • A coordinated
Page 11 and 12: Uncoalesced Access: Reading floats
Page 13 and 14: Compute 1.2+ Coalesced Access: Read
Page 15 and 16: Thread Group Shared Memory (TGSM)
Page 17 and 18: Shared Memory Bank Addressing • N
Page 19 and 20: What is Occupancy GPUs typical
Page 21 and 22: Maximizing HW Occupancy • Registe
Page 23 and 24: DirectCompute Optimization Example
Page 25 and 26: Parallel Reduction • Tree-based a
Page 27 and 28: Solution: Shader Decomposition •
Page 29 and 30: Reduction #1: Interleaved Addressin
Page 31 and 32: Performance for 4M element reductio
Page 33 and 34: What is Thread Divergence • Diver
Page 35 and 36: Parallel Reduction: Interleaved Add
Page 37 and 38: Thread Group Shared Memory Bank Con
Page 39 and 40: Reduction #3: Sequential Addressing
Page 41 and 42: Idle Threads Problem: for (unsigned
Page 45 and 46: Unrolling the Last Warp • As redu
Page 49 and 50: Reduction #6: Completely Unrolled #
Page 51 and 52: Reduction #7: Multiple Adds / Threa
Page 53 and 54:
Performance for 4M element reductio
Page 55 and 56:
Time (ms) Performance Comparison 10
Page 58 and 59:
Extra Slides
Page 60 and 61:
Memory Coalescing (Matrix Multiply)
Page 62:
Matrix Multiplication (cont.) Optim
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DirectCompute Optimizations and Best Practices - Nvidia

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