high performance computing on graphics processing units: hgpu.org

Martin Bauer, Sebastian Eibl, Christian Godenschwager, Nils Kohl, Michael Kuron, Christoph Rettinger, Florian Schornbaum, Christoph Schwarzmeier, Dominik Thönnes, Harald Köstler, Ulrich Rüde

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Tags: Code generation, CUDA, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Package, Particle simulation, performance portability, Physics, Tesla P100

October 6, 2019 by hgpu

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

Maria Papadaki, Yannis Sfakianakis, Christos Kozanitis, Angelos Bilas

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Tags: Computer science, CUDA, GPU cluster, Grid, nVidia, nVidia Quadro K 2200

October 6, 2019 by hgpu

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

Abhinav Jangda, Arjun Guha

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Tags: CUDA, Image procesing, nVidia, nVidia GeForce GTX 1080 Ti, Python, Tesla V100

September 22, 2019 by hgpu

ALPyNA: Acceleration of Loops in Python for Novel Architectures

Dejice Jacob, Jeremy Singer

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 1060, Python

September 22, 2019 by hgpu

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Speech recognition

September 22, 2019 by hgpu

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high performance computing on graphics processing units: hgpu.org

Programming

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

AI Benchmark: All About Deep Learning on Smartphones in 2019

Blink: Fast and Generic Collectives for Distributed ML

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

waLBerla: A block-structured high-performance framework for multiphysics simulations

Syntix: A Profiling Based Resource Estimator for CUDA Kernels

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

ALPyNA: Acceleration of Loops in Python for Novel Architectures

Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

Recent source codes

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

EngineCL for Stratix10 MX

Theseus: A Library for Differentiable Nonlinear Optimization

OmpCluster

PipeCNN: An OpenCL-based FPGA Accelerator for Convolutional Neural Networks

hls4ml: Machine learning on FPGAs using HLS

MocCUDA: Prototype to run Pytorch/CUDA on Fugaku

Rodinia Benchmarks for DPC++

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