high performance computing on graphics processing units: hgpu.org

Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, Yori Zwols

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Tags: Computer science, Deep learning, Machine learning, Mathematics, Neural networks, nVidia

December 27, 2020 by hgpu

CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Alireza Ghaffari, Yvon Savaria

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Tags: Computer science, Design space exploration, FPGA, Machine learning, Neural networks, OpenCL

December 27, 2020 by hgpu

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera, Maurizio Martina, Muhammad Shafique

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Tags: Artificial intelligence, Benchmarking, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Medicine, Neural networks, nVidia, Tesla V100

December 27, 2020 by hgpu

Exploiting BSP Abstractions for Compiler Based Optimizations of GPU Applications on multi-GPU Systems

Alexander Matz

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Tags: Benchmarking, Code generation, Compilers, Computer science, CUDA, nVidia, Package, Thesis

December 27, 2020 by hgpu

ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

Anderson Faustino da Silva, Bruno Conde Kind, Jose Wesley de Souza Magalhaes, Jeronimo Nunes Rocha, Breno Campos Ferreira Guimaraes, Fernando Magno Quintao Pereira

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Tags: Algorithms, Benchmarking, Code generation, Compiler, Computer science, LLVM, Package

December 20, 2020 by hgpu

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Xuewen Cui

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Tags: Artificial intelligence, Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, OpenMP, Performance, Tesla K40, Tesla P100, Tesla V100, Thesis

December 20, 2020 by hgpu

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high performance computing on graphics processing units: hgpu.org

Applications

Design, Implementation and Test of Efficient GPU to GPU Communication Methods

Interactive Parallelization of C Programs in SAPFOR

I/O Lower Bounds for Auto-tuning of Convolutions in CNNs

Thermal Safety and Real-Time Predictability on Heterogeneous Embedded SoC Platforms

Fast CUDA-Aware MPI Datatypes without Platform Support

When Machine Learning Meets Quantum Computers: A Case Study

Solving Mixed Integer Programs Using Neural Networks

CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Exploiting BSP Abstractions for Compiler Based Optimizations of GPU Applications on multi-GPU Systems

ANGHABENCH: a Suite with One Million Compilable C Benchmarks for Code-Size Reduction

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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