DirectCompute Optimizations and Best Practices - Nvidia

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Memory Coalescing (Matrix Multiply) Each iteration, threads access the same element in A. Un-coalesced in CC
Matrix Multiplication (cont.) Optimization GeForce GTX 280 GeForce GTX 8800 No optimization Coalesced using local memory to store a tile of A Using thread group shared memory to eliminate redundant reads of a tile of B 8.8 GBps 0.7 GBps 14.3 GBps 8.2 GBps 29.7 GBps 15.7 GBps
Page 1 and 2:
DirectCompute Optimizations and Bes
Page 3 and 4:
Why GPUs • GPUs are throughput or
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 55 and 56: Time (ms) Performance Comparison 10
Page 58 and 59: Extra Slides
Page 62: Matrix Multiplication (cont.) Optim
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DirectCompute Optimizations and Best Practices - Nvidia

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