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GPUHarbor: Testing GPU Memory Consistency at Large

Reese Levine, Mingun Cho, Devon McKee, Andrew Quinn, Tyler Sorensen

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Tags: AMD Radeon Pro 5500M, ATI, Computer science, Memory model, nVidia, nVidia GeForce RTX 3080, nVidia Tegra TX1, OpenCL, Performance

June 11, 2023 by hgpu

Accelerating 128-bit Floating-Point Matrix Multiplication on FPGAs

Fumiya Kono, Naohito Nakasato, Maho Nakata

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Tags: Benchmarking, Computer science, Extended precision, FPGA, Matrix multiplication, OpenCL

June 11, 2023 by hgpu

Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Tobias Groth, Sven Groppe, Thilo Pionteck, Franz Valdiek, Martin Koppehel

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Tags: APU, Computer science, CUDA, Hashing, nVidia A100, oneAPI, SYCL

June 4, 2023 by hgpu

Compiler Technologies in Deep Learning Co-Design: A Survey

Hongbin Zhang, Mingjie Xing, Yanjun Wu, Chen Zhao

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Tags: Compielrs, Computer science, Deep learning, HLS, OpenCL, Package, survey

June 4, 2023 by hgpu

Implementation Techniques for SPMD Kernels on CPUs

Joachim Meyer, Aksel Alpay, Sebastian Hack, Holger Fröning, Vincent Heuveline

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, performance portability

June 4, 2023 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Zhuren Liu, Shouzhe Zhang, Justin Garrigus, Hui Zhao

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Tags: Benchmarking, Bioinformatics, Biology, Computer science, CUDA, Genomics, nVidia, nVidia GeForce RTX 3070, Package

May 28, 2023 by hgpu

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study

Leonardo Solis-Vasquez, Edward Mascarenhas, Andreas Koch

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Tags: Chemistry, Computer science, CUDA, molecular docking, nVidia, nVidia A100, oneAPI, Package, SYCL

May 28, 2023 by hgpu

PyTorch Hyperparameter Tuning – A Tutorial for spotPython

Thomas Bartz-Beielstein

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Tags: Computer science, Deep learning, Package, Python, PyTorch, Tutorial

May 28, 2023 by hgpu

Communication-minimizing Asynchronous Tensor Parallelism

Siddharth Singh, Zack Sating, Abhinav Bhatele

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Tags: Computer science, CUDA, GPU cluster, Neural networks, nVidia, nVidia A100

May 28, 2023 by hgpu

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

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Tags: Code generation, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 3070

May 28, 2023 by hgpu

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Philip Salzmann, Fabian Knorr, Peter Thoman, Philipp Gschwandtner, Biagio Cosenza, Thomas Fahringer

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia V100, Package, SYCL

May 21, 2023 by hgpu

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Zhiyi Zhang, Pengfei Zhang, Qi Wang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, nVidia, nVidia GeForce RTX 3060 Ti, Package

May 21, 2023 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

GPUHarbor: Testing GPU Memory Consistency at Large

Accelerating 128-bit Floating-Point Matrix Multiplication on FPGAs

Hybrid CPU/GPU/APU accelerated query, insert, update and erase operations in hash tables with string keys

Compiler Technologies in Deep Learning Co-Design: A Survey

Implementation Techniques for SPMD Kernels on CPUs

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study

PyTorch Hyperparameter Tuning – A Tutorial for spotPython

Communication-minimizing Asynchronous Tensor Parallelism

ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time

An Asynchronous Dataflow-Driven Execution Model For Distributed Accelerator Computing

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

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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