high performance computing on graphics processing units: hgpu.org

Thomas C.P. Chau, Maciej Kurek, James S. Targett, Jake Humphrey, George Skouroupathis, Alison Eele, Jan Maciejowski, Benjamin Cope, Kathryn Cobden, Philip Leong, Peter Y.K. Cheung, Wayne Luk

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, nVidia, Package, Tesla C2070

May 29, 2014 by hgpu

Engineering a static verification tool for GPU kernels

Ethel Bardsley, Adam Betts, Nathan Chong, Peter Collingbourne, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema, Daniel Liew, Shaz Qadee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Python

May 25, 2014 by hgpu

GPU accelerated toolbox for real-time beam-shaping in multimode fibres

M. Ploschner, B. Straka, K. Dholakia, T. Cizmar

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Tags: Computer holography, CUDA, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 780, Optics, Package, Physics

May 25, 2014 by hgpu

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 480, Package, Python, Sequence alignment, Tesla K10, Tesla K20

May 25, 2014 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

Medusa: A Parallel Graph Processing System on Graphics Processors

Synergia CUDA: GPU-accelerated accelerator modeling package

Performance Improvement of Data Mining in Weka through GPU Acceleration

A Fast Batched Cholesky Factorization on a GPU

Mapping parallel programs to heterogeneous multi-core systems

Adventures in the microlensing cloud: large datasets, eResearch tools, and GPUs

Loo.py: transformation-based code generation for GPUs and CPUs

An implementation of a reordering approach for increasing the product of diagonal entries in a sparse matrix

SMCGen: Generating Reconfigurable Design for Sequential Monte Carlo Applications

Engineering a static verification tool for GPU kernels

GPU accelerated toolbox for real-time beam-shaping in multimode fibres

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

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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