Tutorial CUDA

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Reduce 5: More Optimizations through Unrolling Parallel reduction inner loop: for (int stride = numThreadsPerBlock / 2; stride > 0; stride /= 2) { __syncthreads(); if (threadID < stride) sresult[threadID] += sresult[threadID + stride]; } There are only so many values for numThreadsPerBlock: Multiple of 32, less or equal to 512 So, templatize on numThreadsPerBlock: template __global__ void reduce_kernel(const float* valuesIn, uint numValues, float* valuesOut) And unroll: © NVIDIA Corporation 2008
Reduce 5: Unrolled Loop if (numThreadsPerBlock >= 512) { __syncthreads(); if (threadID < 256) sresult[threadID] += sresult[threadID + 256]; } if (numThreadsPerBlock >= 256) { __syncthreads(); if (threadID < 128) sresult[threadID] += sresult[threadID + 128]; } if (numThreadsPerBlock >= 128) { __syncthreads(); if (threadID < 64) sresult[threadID] += sresult[threadID + 64]; } if (numThreadsPerBlock >= 64) { __syncthreads(); if (threadID < 32) sresult[threadID] += sresult[threadID + 32]; } if (numThreadsPerBlock >= 32) { __syncthreads(); if (threadID < 16) sresult[threadID] += sresult[threadID + 16]; } … if (numThreadsPerBlock >= 4) { __syncthreads(); if (threadID < 2) sresult[threadID] += sresult[threadID + 2]; } if (numThreadsPerBlock >= 2) { __syncthreads(); if (threadID < 1) sresult[threadID] += sresult[threadID + 1]; } All code in blue will be evaluated at compile time! © NVIDIA Corporation 2008
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Tutorial CUDA Cyril Zeller NVIDIA D
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© NVIDIA Corporation 2008 GPU Comp
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Parallel Computing’s Dark Age But
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Enter the GPU GPU = Graphics Proces
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Enter CUDA CUDA is a scalable paral
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110-240X © NVIDIA Corporation 2008
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GPUs Are Getting Faster, Faster ©
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© NVIDIA Corporation 2008 CUDA Pro
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Heterogeneous Programming CUDA = se
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Hierarchy of Concurrent Threads Thr
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Memory Hierarchy Sequential Kernels
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CUDA Language: C with Minimal Exten
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Example: Increment Array Elements C
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Example: Host Code // allocate host
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More on Memory Spaces Each thread c
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Compiling CUDA for NVIDIA GPUs Any
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Device Emulation Mode Pitfalls Emul
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Reduction Exercise At the end of ea
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Reduce 1: Blocking the Data Block I
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Reduce 1: Multi-Pass Reduction Bloc
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© NVIDIA Corporation 2008 CUDA Imp
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Hardware Implementation: A Set of S
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Hardware Implementation: Execution
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Host Synchronization All kernel lau
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Multiple CPU Threads and CUDA CUDA
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Performance Optimization Expose as
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Expose Parallelism: CPU/GPU Paralle
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Minimize CPU ↔ GPU Data Transfers
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Global Memory Reads/Writes Global m
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Coalesced Global Memory Accesses ©
Page 61 and 62: Non-Coalesced Global Memory Accesse
Page 63 and 64: Avoiding Non-Coalesced Accesses For
Page 65 and 66: Profiler Signals Events are tracked
Page 67 and 68: Back to Reduce Exercise: Profile wi
Page 69 and 70: Reduce 2 Thread IDs Block IDs Distr
Page 71 and 72: Maximize Use of Shared Memory Share
Page 73 and 74: Example: Square Matrix Multiplicati
Page 75 and 76: Example: Avoiding Non-Coalesced flo
Page 81 and 82: Execution Configuration: Constraint
Page 83 and 84: Execution Configuration: Heuristics
Page 85 and 86: Back to Reduce Exercise: Problem wi
Page 87 and 88: Parallel Reduction Complexity Takes
Page 89 and 90: Reduce 3: Go Ahead! Open up reduce\
Page 91 and 92: Arithmetic Instruction Throughput f
Page 93 and 94: Runtime Math Library There are two
Page 95 and 96: What You Need To Know FP64 instruct
Page 97 and 98: Mixed Precision Arithmetic Research
Page 99 and 100: Single Precision Floating Point ©
Page 101 and 102: Instruction Predication Comparison
Page 103 and 104: Shared Memory Is Banked Bandwidth o
Page 105 and 106: Bank Addressing Examples 2-way bank
Page 107 and 108: Back to Reduce Exercise: Problem wi
Page 109 and 110: Reduce 3: Bank Conflicts Showed for
Page 111: Reduce 4: Go Ahead! Open up reduce\
Page 115 and 116: Reduce 5: Final Unrolled Loop if (n
Page 117 and 118: Coming Up Soon CUDA 2.0 © NVIDIA C
Page 119 and 120: Extra Slides
Page 121 and 122: Applications - Condensed 3D image a
Page 123 and 124: Performance GPU Electromagnetic Fie
Page 125 and 126: EvolvedMachines 130X Speed up Brain
Page 127 and 128: nbody Astrophysics Astrophysics res
Page 129 and 130: A quick review device = GPU = set o
Page 131 and 132: Language Extensions: Function Type
Page 133 and 134: Language Extensions: Execution Conf
Page 135 and 136: Common Runtime Component Provides:
Page 137 and 138: Common Runtime Component: Mathemati
Page 139 and 140: Host Runtime Component Provides fun
Page 141 and 142: Host Runtime Component: Memory Mana
Page 143 and 144: Host Runtime Component: Interoperab
Page 145 and 146: Host Runtime Component: Error Handl
Page 147 and 148: Device Runtime Component: Mathemati
Page 149 and 150: Device Runtime Component: Texture F
Page 151 and 152: Compilation Any source file contain
Page 153 and 154: Role of Open64 Open64 compiler give
Page 155 and 156: CUDA Libraries CUBLAS CUFFT © NVID
Page 157: CUFFT Library Efficient implementat
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Tutorial CUDA

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