Tutorial CUDA

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Hardware Implementation: Execution Model Each active block is split into warps in a welldefined way Warps are time-sliced In other words: Threads within a warp are executed physically in parallel Warps and blocks are executed logically in parallel © NVIDIA Corporation 2008 Host Kernel 1 Kernel 2 Block (1, 1) Thread (0, 0) Thread (0, 1) Thread (0, 2) … Device Grid 1 Block (0, 0) Block (0, 1) Grid 2 Thread (31, 0) Block (1, 0) Block (1, 1) Warp 0 Warp 1 Thread (32, 0) … Warp 2 Warp 3 … Thread (31, 1) Thread (32, 1) … Warp 4 Warp 5 … Thread (31, 2) Thread (32, 2) … Block (2, 0) Block (2, 1) Thread (63, 0) Thread (63, 1) Thread (63, 2)
Host Synchronization All kernel launches are asynchronous © NVIDIA Corporation 2008 control returns to CPU immediately kernel executes after all previous CUDA calls have completed cudaMemcpy is synchronous control returns to CPU after copy completes copy starts after all previous CUDA calls have completed cudaThreadSynchronize() blocks until all previous CUDA calls complete 47
Page 1 and 2: Tutorial CUDA Cyril Zeller NVIDIA D
Page 3 and 4: © NVIDIA Corporation 2008 GPU Comp
Page 5 and 6: Parallel Computing’s Dark Age But
Page 7 and 8: Enter the GPU GPU = Graphics Proces
Page 9 and 10: Enter CUDA CUDA is a scalable paral
Page 11 and 12: 110-240X © NVIDIA Corporation 2008
Page 13 and 14: GPUs Are Getting Faster, Faster ©
Page 15 and 16: © NVIDIA Corporation 2008 CUDA Pro
Page 17 and 18: Heterogeneous Programming CUDA = se
Page 19 and 20: Hierarchy of Concurrent Threads Thr
Page 21 and 22: Memory Hierarchy Sequential Kernels
Page 23 and 24: CUDA Language: C with Minimal Exten
Page 25 and 26: Example: Increment Array Elements C
Page 27 and 28: Example: Host Code // allocate host
Page 29 and 30: More on Memory Spaces Each thread c
Page 31 and 32: Compiling CUDA for NVIDIA GPUs Any
Page 33 and 34: Device Emulation Mode Pitfalls Emul
Page 35 and 36: Reduction Exercise At the end of ea
Page 37 and 38: Reduce 1: Blocking the Data Block I
Page 39 and 40: Reduce 1: Multi-Pass Reduction Bloc
Page 41 and 42: © NVIDIA Corporation 2008 CUDA Imp
Page 43 and 44: Hardware Implementation: A Set of S
Page 45: Hardware Implementation: Execution
Page 49 and 50: Multiple CPU Threads and CUDA CUDA
Page 51 and 52: Performance Optimization Expose as
Page 53 and 54: Expose Parallelism: CPU/GPU Paralle
Page 55 and 56: Minimize CPU ↔ GPU Data Transfers
Page 57 and 58: Global Memory Reads/Writes Global m
Page 59 and 60: 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 and 112:
Reduce 4: Go Ahead! Open up reduce\
Page 113 and 114:
Reduce 5: Unrolled Loop if (numThre
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
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Performance GPU Electromagnetic Fie
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EvolvedMachines 130X Speed up Brain
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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
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Common Runtime Component Provides:
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Common Runtime Component: Mathemati
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Host Runtime Component Provides fun
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Host Runtime Component: Memory Mana
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Host Runtime Component: Interoperab
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Host Runtime Component: Error Handl
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Device Runtime Component: Mathemati
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Device Runtime Component: Texture F
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Compilation Any source file contain
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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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