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PERSISTENT RNN SPEEDUP ON V100
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Classroom Contents
Improvements to NVIDIA CUDA and Deep Learning Libraries - Session 1
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- 1 Intro
- 2 CUDA DEVELOPMENT ECOSYSTEM
- 3 POWERING THE DEEP LEARNING ECOSYSTEM
- 4 TESLA UNIVERSAL ACCELERATION PLATFORM
- 5 ACCELERATED COMPUTING IS FULL-STACK OPTIMIZATION
- 6 INTRODUCING CUDA 10,0
- 7 16 GPUS WITH 32GB MEMORY EACH
- 8 NVSWITCH: ALL-TO-ALL CONNECTIVITY
- 9 UNIFIED MEMORY + DGX-2
- 10 2X HIGHER PERFORMANCE WITH NVSWITCH
- 11 NEW PROGRAMMING MODEL FEATURES
- 12 ASYNCHRONOUS TASK GRAPHS
- 13 NEW EXECUTION MECHANISM
- 14 EXECUTION OPTIMIZATIONS
- 15 PERFORMANCE IMPACT
- 16 THE PATH TO FUSION ENERGY
- 17 VOLTA TENSOR CORE
- 18 NEW TURING TENSOR CORE
- 19 NEW TURING WARP MATRIX FUNCTIONS
- 20 CUTLASS 1.1
- 21 NVIDIA NGX: DL FOR CREATIVE APPLICATIONS
- 22 IN ADOBE PHOTOSHOP
- 23 CUDNN: GPU ACCELERATED DEEP LEARNING
- 24 IMPROVED HEURISTICS FOR CONVOLUTIONS
- 25 PERSISTENT RNN SPEEDUP ON V100
- 26 STRIDED ACTIVATION GRADIENTS
- 27 TENSORCORES WITH FP32 MODELS
- 28 MORE TENSORCORE PERFORMANCE IMPROVEMENTS
- 29 GENERAL PERFORMANCE IMPROVEMENTS
- 30 FUTURE UPDATES
