CUDA Libraries and Ecosystem Overview - GPU Technology ...

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cuRAND
Random Number Generation on GPU • Generating high quality random numbers in parallel is hard — Don’t do it yourself, use a library! • Large suite of generators and distributions — XORWOW, MRG323ka, MTGP32, (scrambled) Sobol — uniform, normal, log-normal — Single and double precision • Two APIs for cuRAND — Host: Ideal when generating large batches of RNGs on GPU — Device: Ideal when RNGs need to be generated inside a kernel
Page 1 and 2: CUDA Libraries and Ecosystem Overvi
Page 3 and 4: Constant progress on library develo
Page 5 and 6: Linear Algebra
Page 7 and 8: Multi Node Single Node A Birds Eye
Page 9 and 10: Multi Node Single Node .. but there
Page 11 and 12: NVIDIA CUDA Library Approach • Pr
Page 13 and 14: cuBLAS Level 3 Performance Up to 1
Page 15 and 16: cuBLAS: Legacy and Version 2 Interf
Page 17 and 18: Version 2 Interface helps reducing
Page 19 and 20: Single Node Parallel The cuSPARSE -
Page 21 and 22: Speedup over MKL Up to 40x faster w
Page 23 and 24: Multi Node Single Node Third Partie
Page 25 and 26: Different Approaches to Linear Alge
Page 27 and 28: Toolkits are increasingly supportin
Page 29 and 30: Common Tasks in Signal Processing F
Page 31 and 32: Basic concepts of cuFFT • Interfa
Page 33 and 34: High 1D transform performance is ke
Page 35 and 36: cufftPlanMany: Transformation on co
Page 37 and 38: NPP features a large set of functio
Page 39: Multi-GPU FFT • Many problems too
Page 43 and 44: cuRAND Performance compared to Inte
Page 45 and 46: Thurst: STL-like CUDA Template Libr
Page 47 and 48: Explore the CUDA (Libraries) Ecosys
Page 49: Thank you

CUDA Libraries and Ecosystem Overview - GPU Technology ...

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