Tutorial CUDA

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Non-Coalesced Global Memory Accesses © NVIDIA Corporation 2008 t0 t1 t2 t3 t14 t15 128 132 136 140 144 184 188 192 Non-sequential float memory access t0 t1 t2 t3 t13 t14 t15 128 132 136 140 144 184 188 192 Misaligned starting address
Non-Coalesced Global Memory Accesses © NVIDIA Corporation 2008 t0 t1 t2 t3 t13 t14 t15 128 132 136 140 144 184 188 192 Non-contiguous float memory access t0 t1 t2 t3 t14 t15 12 bytes 128 140 152 164 176 296 308 320 Non-coalesced float3 memory access
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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
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 and 46: Hardware Implementation: Execution
Page 47 and 48: Host Synchronization All kernel lau
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: Coalesced Global Memory Accesses ©
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
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Reduce 4: Go Ahead! Open up reduce\
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Reduce 5: Unrolled Loop if (numThre
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Reduce 5: Final Unrolled Loop if (n
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Coming Up Soon CUDA 2.0 © NVIDIA C
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Extra Slides
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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
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A quick review device = GPU = set o
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Language Extensions: Function Type
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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
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CUDA Libraries CUBLAS CUFFT © NVID
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CUFFT Library Efficient implementat
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