high performance computing on graphics processing units: hgpu.org

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Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

Mina Ashoury, Mohammad Loni, Farshad Khunjush, Masoud Daneshtalab

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Tags: Computer science, CUDA, Energy efficiency, Linear Algebra, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1650, Sparse matrix

February 26, 2023 by hgpu

Orca: FSS-based Secure Training with GPUs

Neha Jawalkar, Kanav Gupta, Arkaprava Basu, Nishanth Chandran, Divya Gupta, Rahul Sharma

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Tags: Computer science, CUDA, nVidia, nVidia RTX A6000, Security

February 26, 2023 by hgpu

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

Uzmar Gomez, Gonzalo Brito Gadeschi, Tobias Weinzierl

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Tags: Algorithms, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, nVidia GeForce RTX 2080 Ti, OpenMP, Partial differential equations, PDEs

February 26, 2023 by hgpu

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang

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Tags: Computer science, CUDA, Deep learning, nVidia, Optimization, Package, Tesla K80, Tesla T4

February 12, 2023 by hgpu

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Rendering, Robotics

February 12, 2023 by hgpu

Evaluation of Rust for GPGPU high-performance computing

Viktor Franzén, Carl Östling

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Tags: Computer science, CUDA, HPC, nVidia, nVidia GeForce GTX 1660 Ti, nVidia GeForce RTX 2080 Ti, Package, PTX, Thesis

February 5, 2023 by hgpu

CMLCompiler: A Unified Compiler for Classical Machine Learning

Xu Wen, Wanling Gao, Anzheng Li, Lei Wang, Zihan Jiang, Jianfeng Zhan

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Tags: Compilers, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia Titan RTX

February 5, 2023 by hgpu

Revisiting Query Performance in GPU Database Systems

Jiashen Cao, Rathijit Sen, Matteo Interlandi, Joy Arulraj, Hyesoon Kim

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Tags: Computer science, CUDA, Databases, nVidia, nVidia A100

February 5, 2023 by hgpu

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

Kazuaki Matsumura, Simon Garcia De Gonzalo, Antonio J. Peña

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, OpenACC, Package, PTX, Tesla K40, Tesla K80, Tesla V100

February 5, 2023 by hgpu

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Seongyeon Park, Hajin Kim, Tanveer Ahmad, Nauman Ahmed, Zaid Al-Ars, H. Peter Hofstee, Youngsok Kim, Jinho Lee

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Tags: Bioinformatics, Biology, CUDA, Databases, nVidia, nVidia GeForce GTX 1650, nVidia GeForce RTX 3090, Sequence alignment, Smith-Waterman algorithm, String matching

January 29, 2023 by hgpu

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks

Kun Wu, Mert Hidayetoğlu, Xiang Song, Sitao Huang, Da Zheng, Israt Nisa, Wen-mei Hwu

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 3090

January 22, 2023 by hgpu

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

Jinfan Chen, Shigang Li, Ran Gun, Jinhui Yuan, Torsten Hoefler

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Tags: Computer science, CUDA, Deep learning, Distributed computing, nVidia, nVidia A100, Package, Tesla P100

January 22, 2023 by hgpu

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

Faial: finds bugs in CUDA kernels

Sound and Partially-Complete Static Analysis of Data-Races in GPU Programs

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Intel® SHMEM: Device initiated shared memory based communication library

Intel(R) SHMEM: GPU-initiated OpenSHMEM using SYCL

miniLB: Lattice Botlzmann miniapp w/SYCL

miniLB: A Performance Portability Study of Lattice-Boltzmann Simulations

AFOCL

Bitstream Database-Driven FPGA Programming Flow Based on Standard OpenCL

2domination

HPC acceleration of large (min, +) matrix products to compute domination-type parameters in graphs

See all packages

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high performance computing on graphics processing units: hgpu.org

Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

Orca: FSS-based Secure Training with GPUs

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

TLP: A Deep Learning-based Cost Model for Tensor Program Tuning

ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills

Evaluation of Rust for GPGPU high-performance computing

CMLCompiler: A Unified Compiler for Classical Machine Learning

Revisiting Query Performance in GPU Database Systems

A Symbolic Emulator for Shuffle Synthesis on the NVIDIA PTX Code

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

Recent source codes

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

EnergyUCB-Bandit

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

Faial: finds bugs in CUDA kernels

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Intel® SHMEM: Device initiated shared memory based communication library

miniLB: Lattice Botlzmann miniapp w/SYCL

AFOCL

2domination

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