high performance computing on graphics processing units: hgpu.org

hgpu.org » OpenMP

MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring

Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak, Tania Banerjee, Jaemoon Lee, Anand Rangarajan, Lipeng Wan, Nicolas Vidal, Qing Liu, Ana Gainaru, Norbert Podhorszki, Richard Archibald, Sanjay Ranka, Scott Klasky

View

Tags: Compression, Computer science, CUDA, HIP, HPC, Numerical Analysis, nVidia, nVidia V100, OpenMP, Package, SYCL

January 21, 2024 by hgpu

Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Mihai Capota, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

View

Tags: AI, Code generation, Computer science, Heterogeneous systems, HPC, nVidia, nVidia A40, OpenMP, Package, Python

January 7, 2024 by hgpu

Scalable Tuning of (OpenMP) GPU Applications via Kernel Record and Replay

Konstantinos Parasyris, Giorgis Georgakoudis, Esteban Rangel, Ignacio Laguna, Johannes Doerfert

View

Tags: AMD Radeon Instinct MI250X, ATI, Auto-Tuning, Computer science, Heterogeneous systems, nVidia, nVidia V100, OpenMP, Package

December 18, 2023 by hgpu

Precision and Performance Analysis of C Standard Math Library Functions on GPUs

Anton Rydahl, Joseph Huber, Ethan Luis McDonough, Johannes Doerfert

View

Tags: AMD Radeon Instinct MI250X, AMD Radeon Instinct Mi50, ATI, Benchmarking, Compilers, Computer science, CUDA, HIP, Numerical Analysis, OpenMP, Performance, Tesla A100, Tesla V100

December 18, 2023 by hgpu

Application Performance Profiling on Intel GPUs with Oneprof and Onetrace

Mariam Umar, Maxwell Jong

View

Tags: Computer science, Intel, Intel Data Center GPU Max 1550, OpenCL, OpenMP, Package, Performance

December 18, 2023 by hgpu

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

View

Tags: Benchmarking, Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX A6000, OpenMP, Package

November 19, 2023 by hgpu

ExaNBody: a HPC framework for N-Body applications

Thierry Carrard, Raphaël Prat, Guillaume Latu, Killian Babilotte, Paul Lafourcade, Lhassan Amarsid, Laurent Soulard

View

Tags: Astrophysics, Computer science, CUDA, MPI, N-body simulation, nVidia, nVidia A100, OpenMP, performance portability

November 19, 2023 by hgpu

An approach to performance portability through generic programming

Andreas Hadjigeorgiou, Christodoulos Stylianou, Michele Weiland, Dirk Jacob Verschuur, Jacob Finkenrath

View

Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, HPC, nVidia, OpenMP, Package, Performance, performance portability, Portability, Tesla V100

November 12, 2023 by hgpu

An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark

Jovan Ðukic, Marko Mišic

View

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce RTX 3080 Ti, OpenACC, OpenCL, OpenMP

November 12, 2023 by hgpu

A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models

Leonard Apanasevich, Yogesh Kale, Himanshu Sharma, Ana Marija Sokovic

View

Tags: Benchmarking, Bioinformatics, Biology, Chemistry, Computer science, CUDA, Heterogeneous systems, Molecular dynamics, nVidia, nVidia A100, nVidia P100, nVidia V100, OpenCL, OpenMP, Performance, SYCL

November 5, 2023 by hgpu

Exploring the Limits of Generic Code Execution on GPUs via Direct (OpenMP) Offload

S. Tian, B. Chapman, J. Doerfert

View

Tags: Compilers, Computer science, LLVM, OpenMP

October 8, 2023 by hgpu

Advancing the distributed Multi-GPU ChASE library through algorithm optimization and NCCL library

Xinzhe Wu, Edoardo Di Napoli

View

Tags: Algorithm optimization, Computer science, CUDA, MPI, nVidia, nVidia A100, OpenMP, OpenMPI, Package

October 8, 2023 by hgpu

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

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

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

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

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: