high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia RTX A4000, Package, Signal processing

May 7, 2023 by hgpu

Simple and efficient GPU accelerated topology optimisation: Codes and applications

Erik A. Träff, Anton Rydahl, Sven Karlsson, Ole Sigmund, Niels Aage

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Tags: Computer science, CUDA, nVidia, nVidia A100, OpenCL, OpenMP, Package, Topology optimization problem

April 23, 2023 by hgpu

Fuzzing Loop Optimizations in Compilers for C++ and Data-Parallel Languages

Vsevolod Livinskii, Dmitry Babokin, John Regehr

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Tags: Code generation, Compilers, Computer science, oneAPI, Package, SYCL

April 23, 2023 by hgpu

Thread-safe lattice Boltzmann for high-performance computing on GPUs

Andrea Montessori, Marco Lauricella, Adriano Tiribocchi, Mihir Durve, Michele La Rocca, Giorgio Amati, Fabio Bonaccorso, Sauro Succi

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Tags: Algorithms, cfd, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia A100, Package

April 23, 2023 by hgpu

Optimizing High-Performance Linpack for Exascale Accelerated Architectures

Noel Chalmers, Jakub Kurzak, Damon McDougall, Paul T. Bauman

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Tags: AMD Radeon Instinct MI250X, ATI, Benchmarking, Computer science, Factorization, HIP, Linear Algebra, MPI, Package, Performance

April 23, 2023 by hgpu

GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Martin Swany, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia A100, nVidia RTX A4000, Package

April 23, 2023 by hgpu

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Zheming Jin, Jeffrey S. Vetter

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Tags: Benchmarking, Bioinformatics, Computer science, CUDA, Heterogeneous systems, nVidia, Package, performance portability, SYCL, Tesla V100

April 16, 2023 by hgpu

Kernel Tuning Toolkit

Filip Petrovič, Jiří Filipovič

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, Python, Vulkan

April 16, 2023 by hgpu

Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

Yehonatan Fridman, Guy Tamir, Gal Oren

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Tags: Benchmarking, Computer science, Intel, Intel Ponte Vecchio Max 1100, nVidia, nVidia A100, oneAPI, OpenMP, Package, performance portability

April 16, 2023 by hgpu

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

Xingfu Wu, Prasanna Balaprakash, Michael Kruse, Jaehoon Koo, Brice Videau, Paul Hovland, Valerie Taylor, Brad Geltz, Siddhartha Jana, Mary Hall

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Tags: Bayesian, Computer science, Energy-efficient computing, MPI, nVidia, OpenMP, Package, Tesla V100

April 2, 2023 by hgpu

Managing heterogeneous device memory using C++17 memory resources

S.N. Swatman, A. Krasznahorkay, P. Gessinger

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, Memory model, nVidia, Package, SYCL

April 2, 2023 by hgpu

PopSparse: Accelerated block sparse matrix multiplication on IPU

Zhiyi Li, Douglas Orr, Valeriu Ohan, Godfrey Da costa, Tom Murray, Adam Sanders, Deniz Beker, Dominic Masters

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, Matrix multiplication, nVidia, nVidia A100, Package, Sparse matrix

April 2, 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

Applications

FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

Simple and efficient GPU accelerated topology optimisation: Codes and applications

Fuzzing Loop Optimizations in Compilers for C++ and Data-Parallel Languages

Thread-safe lattice Boltzmann for high-performance computing on GPUs

Optimizing High-Performance Linpack for Exascale Accelerated Architectures

GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Kernel Tuning Toolkit

Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

Managing heterogeneous device memory using C++17 memory resources

PopSparse: Accelerated block sparse matrix multiplication on IPU

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

Most viewed papers (last 30 days)