Slides - Par Lab

More documents

Recommendations

Info

Abstraction, cont." Reduction is one of the simplest parallel computations" Performance differentials are even starker as complexityincreases" There’s a need for abstractions at many levels" Primitive computations (BLAS, Data-parallel primitives)" Domain-specific languages" These abstractions make parallel programming moreefficient and more productive" Use libraries whenever possible!" CUBLAS, CUFFT, Thrust© 2011 NVIDIA Corporation 44/73
" A C++ template library for CUDA" Mimics the C++ STL" Containers" On host and device" Algorithms" Sorting, reduction, scan, etc.© 2011 NVIDIA Corporation 45/73
Page 1: An Introduction to CUDA/OpenCL a
Page 13 and 14: What is a CUDA Thread Block?
Page 15 and 16: Synchronization ¡ Threads within
Page 17 and 18: Scalability ¡ Manycore chips exi
Page 19 and 20: Memory model Thread Per-‐threa
Page 24 and 25: Using per-‐block shared memo
Page 26 and 27: CUDA: Runtime support ¡Explicit
Page 28 and 29: CUDA and OpenCL correspondence
Page 30 and 31: Imperatives for Efficient CUDA
Page 32 and 33: Mapping CUDA to a GPU, continu
Page 34 and 35: Profiling NVIDIA Visual Profiler©
Page 36 and 37: Memory, Memory, Memory ! Chapter
Page 38 and 39: Coalescing ¡ GPUs and CPUs both
Page 40 and 41: SoA, AoS ¡ Different data acces
Page 42 and 43: Efficiency and Productivity" Parall
Page 46 and 47: Diving In#include #include #include
Page 48 and 49: Containers" Concise and readable co
Page 50 and 51: Iterators" Iterators act like point
Page 52 and 53: Algorithms" Elementwise operations"
Page 54 and 55: Algorithms" Standard data types// a
Page 56 and 57: Custom Types & Operators// compare
Page 58 and 59: Recap" Containers manage memory" He
Page 60 and 61: Explicit versus implicit parallelis
Page 62 and 63: Explicit versus implicit parallelis
Page 64 and 65: Productivity Implications" Consider
Page 66 and 67: Productivity Implications" Consider
Page 68 and 69: Leveraging Parallel Primitives" Use
Page 70 and 71: Leveraging Parallel Primitives" Com
Page 72 and 73: Summary ¡ Throughput optimized p

Slides - Par Lab

Create successful ePaper yourself

Delete template?

Save as template?