high performance computing on graphics processing units: hgpu.org

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Myths and Legends in High-Performance Computing

Satoshi Matsuoka, Jens Domke, Mohamed Wahib, Aleksandr Drozd, Torsten Hoefler

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Tags: Algorithms, Computer science, HPC

January 15, 2023 by hgpu

A Domain-Extensible Compiler with Controllable Automation of Optimisations

Thomas Kœhler

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Tags: Code generation, Computer science, DSL, Heterogeneous systems, Image processing, Machine learning, OpenCL, Package, Thesis

January 8, 2023 by hgpu

Cramming: Training a Language Model on a Single GPU in One Day

Jonas Geiping, Tom Goldstein

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Tags: Computer science, Deep learning, NLP, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A4000, nVidia RTX A6000, Package

January 8, 2023 by hgpu

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

Jianhui Li, Zhennan Qin, Yijie Mei, Jingze Cui, Yunfei Song, Ciyong Chen, Yifei Zhang, Longsheng Du, Xianhang Cheng, Baihui Jin, Jason Ye, Eric Lin, Dan Lavery

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Tags: Code generation, Compilers, Computer science, Deep learning, OpenCL, Package

January 8, 2023 by hgpu

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi

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Tags: Bayesian, Compilers, Computer science, FPGA, nVidia, OpenCL, Package, Performance, Programming Languages, Tesla K80

January 8, 2023 by hgpu

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

Manos Pavlidakis, Stelios Mavridis, Antony Chazapis, Giorgos Vasiliadis, Angelos Bilas

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce RTX 2080, OpenCL, Package

January 8, 2023 by hgpu

Understanding the Impact of Input Entropy on FPU, CPU, and GPU Power

Sridutt Bhalachandra, Brian Austin, Samuel Williams, Nicholas J. Wright

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Tags: Computer science, Hardware Architecture, nVidia, nVidia A100, Package, Performance

December 25, 2022 by hgpu

Extending MAGMA Portability with OneAPI

Anna Fortenberry, Stanimire Tomov

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce RTX 3060, oneAPI, Package, performance portability

December 25, 2022 by hgpu

mu-grind: A Framework for Dynamically Instrumenting HLS-Generated RTL

Parmida Vahdatniya, Amirali Sharifian, Reza Hojabr, Arrvindh Shriraman

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Tags: Computer science, FPGA, Heterogeneous systems, HLS, Package, Scala

December 25, 2022 by hgpu

GPU Load Balancing

Muhammad Osama

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, load balancing, Matrix multiplication, nVidia, nVidia A100, Package, Sparse, Thesis

December 25, 2022 by hgpu

Kernel-as-a-Service: A Serverless Interface to GPUs

Nathan Pemberton, Anton Zabreyko, Zhoujie Ding, Randy Katz, Joseph Gonzalez

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Tags: Cloud, Computer science, CUDA, Deep learning, Distributed computing, nVidia, Package, Tesla V100

December 25, 2022 by hgpu

A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware

Jiajian Xiao

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia GeForce GTX 1060, nVidia GeForce RTX 2080 Ti, OpenCL, Thesis

December 19, 2022 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Myths and Legends in High-Performance Computing

A Domain-Extensible Compiler with Controllable Automation of Optimisations

Cramming: Training a Language Model on a Single GPU in One Day

oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Arax: a runtime framework for decoupling applications from heterogeneous accelerators

Understanding the Impact of Input Entropy on FPU, CPU, and GPU Power

Extending MAGMA Portability with OneAPI

mu-grind: A Framework for Dynamically Instrumenting HLS-Generated RTL

GPU Load Balancing

A Framework to Generate High-Performance Time-stepped Agent-based Simulations on Heterogeneous Hardware

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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