Use Nvidia Performance Primitives (NPP) in Deep Learning Training. Yang Song
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1 Use Nvidia Performance Primitives (NPP) in Deep Learning Training Yang Song
2 Outline Introduction Function Categories Performance Results Deep Learning Specific Further Information
3 What is NPP? Image+Signal Library, runs on CUDA-enable GPUs Huge amount High performance Over 6,000 in CUDA 9.0, still growing 5x ~ 100x than CPU-only implementation No GPU kernels required. Ease of use Integrate well with the existing project. Primitives recombine to solve wide range of problems.
4 Function Categories (Image) Image Functions Amount Arithmetic and Logic 901 Color Conversion 416 Compression 25 Data Exchange and Initialization 695 Filtering 902 Geometry Transform 315 Morphological 98 Statistical 1523 Threshold and Compare 208 Total Amount 5083
5 Function Categories (Signal) Signal Functions Amount Arithmetic and Logic 370 Data Exchange and Initialization 108 Statistical 385 Threshold 60 Total Amount 923
6 Performance Results (Image) Image Speedup Chart Image/LogicalOperations/ImageRShif Image/ThresholdAndCompareOperati Image/StatisticsFunctionsTests/Norm Image/LogicalOperations/ImageXor_ Image/StatisticsFunctionsTests/Norm Image/StatisticsFunctionsTests/MinM Image/StatisticsFunctionsTests/Norm Image/LogicalOperations/ImageAnd_ Image/Arithmetic/ImageAdd_C3R_Te Image/FilterTests/FilterPrewittVert/ Image/LogicalOperations/ImageOr_A Image/ThresholdAndCompareOperati Image/StatisticsFunctionsTests/Norm Image/StatisticsFunctionsTests/DotP Image/FilterBorderTests/FilterGauss Image/LogicalOperations/ImageAnd_ Image/LogicalOperations/ImageOr_C Image/GeometryTransforms/Mirror/ Image/Arithmetic/ImageMul_C1R_Te Image/Arithmetic/ImageSubC_C4R_T Image/FilterBorderTests/GradientVe Image/ColorConversion/GammaCorre Image/FilterTests/Filter32f/16s_C4R Image/StatisticsFunctionsTests/MinIn Image/FilterTests/SumWindow/Colu Image/Arithmetic/ImageMulC_C1R_T Image/FilterTests/FilterLaplace/32f Image/StatisticsFunctionsTests/Mean Image/LogicalOperations/ImageOrC_ Image/FilterBorderTests/GradientVe Image/GeometryTransforms/Mirror/ Image/LogicalOperations/ImageRShif Image/Arithmetic/ImageMulCScale_C Image/FilterBorderTests/GradientVe Image/Arithmetic/ImageAddC_C3R_T Image/ThresholdAndCompareOperati Image/FilterBorderTests/FilterBorde Image/Arithmetic/ImageSubC_C4R_T Image/Arithmetic/ImageMulC_C4R_T Image/ColorProcessingTests/LUT_Cu Image/Arithmetic/ImageDiv_C4R_Te Image/Arithmetic/ImageDiv_Round_ Image/Arithmetic/ImageAddC_C1R_T Image/Arithmetic/ImageDiv_Round_ Image/Arithmetic/ImageDiv_Round_ Image/Arithmetic/ImageDiv_Round_ Image/FilterBorderTests/GradientVe Image/FilterBorderTests/GradientVe Image/Arithmetic/ImageDiv_Round_ Image/Arithmetic/ImageDiv_C1R_Te Image/Arithmetic/ImageDiv_Round_ Image/FilterBorderTests/GradientVe GPU: GTX 1080; CPU: NPP: 8.0; IPP: ; RAM: 64GB.
7 Performance Results (Signal) Signal Speedup Chart Signal/VectorInitializationTests/Se Signal/StatisticalFunctionTests/Nor Signal/Conversion/Convert/32f8u_ Signal/VectorInitializationTests/Sig Signal/Conversion/Convert/64f32s_ Signal/ArithmeticTests/SignalSubC Signal/StatisticalFunctionTests/Ma Signal/StatisticalFunctionTests/Ma Signal/Conversion/SignalThreshold Signal/ArithmeticTests/SignalAbsT Signal/ArithmeticTests/SignalSubC Signal/ArithmeticTests/SignalSubC Signal/ArithmeticTests/SignalAbsT Signal/ArithmeticTests/SignalMulT Signal/StatisticalFunctionTests/Dot Signal/ArithmeticTests/SignalMulT Signal/StatisticalFunctionTests/Nor Signal/StatisticalFunctionTests/Ma Signal/VectorInitializationTests/Se Signal/ArithmeticTests/SignalMulC Signal/ArithmeticTests/SignalAbsT Signal/VectorInitializationTests/Se Signal/StatisticalFunctionTests/Dot Signal/ArithmeticTests/SignalAddC Signal/ArithmeticTests/SignalMulT Signal/ArithmeticTests/SignalSubC Signal/ArithmeticTests/SignalAddC Signal/ArithmeticTests/SignalSqrtT Signal/StatisticalFunctionTests/Ma Signal/ArithmeticTests/SignalMulT Signal/ArithmeticTests/SignalMulC Signal/ArithmeticTests/SignalMulT Signal/ArithmeticTests/SignalMulC Signal/ArithmeticTests/SignalSubC Signal/ArithmeticTests/SignalSubC Signal/ArithmeticTests/SignalMulT Signal/ArithmeticTests/SignalDivCR Signal/StatisticalFunctionTests/Ma Signal/Conversion/Convert/16s64f_ Signal/ArithmeticTests/SignalSqrtT Signal/ArithmeticTests/SignalSqrtT Signal/Conversion/SignalThreshold Signal/ArithmeticTests/SignalMulC Signal/Conversion/Convert/16s8s_S Signal/ArithmeticTests/SignalDivCR GPU: GTX 1080; CPU: NPP: 8.0; IPP: ; RAM: 64GB.
8 GPU-based JPEG codec (coming in 9.0) Fixed bugs in jpeg routines Optimized performance JPEG decoder baseline: 1000 Mpixel/s* JPEG encoder baseline: 4000 Mpixel/s* Reference: jpegnpp sample * Numbers measured on GTX 1080
9 Batch Processing (coming in 9.0) bandwidth Resize Batch GPU: GTX Peak bandwidth: 320GB/s.
10 Batch Processing (coming in 9.0) bandwidth 250 ColorTwist Batch GPU: GTX Peak bandwidth: 320GB/s.
11 Batch Processing (coming in 9.0) bandwidth Mirror Batch GPU: GTX Peak bandwidth: 320GB/s.
12 Further Reading/Resources NPP is freely available as part of the CUDA Toolkit at Source code samples demonstrating use of the NPP library: jpegnpp cannyedgedetectornpp histequalizationnpp freeimageinteropnpp FilterBorderControlNPP Report/file bugs through Nvidia forum or bug report system.
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