Programming

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On the Way to Future’s High Energy Particle Physics Transport Code

Gabor Biro, Gergely Gabor Barnafoldi, Endre Futo

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Tags: Computational Physics, Computer science, nVidia, OpenCL, Physics, SYCL, Tesla C2050

December 23, 2015 by hgpu

SqueezCL: Squeezing OpenCL Kernels for Approximate Computing on Contemporary GPUs

Atieh Lotf, Abbas Rahimi, Hadi Esmaeilzadeh, Rajesh K. Gupta

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, OpenCL

December 23, 2015 by hgpu

On the Development and Implementation of High-Order Flux Reconstruction Schemes for Computational Fluid Dynamics

Freddie David Witherden

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Tags: AMD FirePro W9100, ATI, CUDA, Finite difference, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

December 23, 2015 by hgpu

OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures

Konstantinos Krommydas, Wu-chun Feng, Christos D. Antonopoulos, Nikolaos Bellas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, OpenCL, Package

December 22, 2015 by hgpu

Parallel FDTD Arithmetic Simulation Based on Distributed Heterogeneous Cluster System

Feng Yuan, Tang Xiaobin, Gong Xiaoyan

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Tags: Computer science, Electrodynamics, FDTD, Finite-difference time-domain, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenCL, Physics, Tesla K20, Tesla M2070

December 22, 2015 by hgpu

Performance and Productivity of Parallel Python Programming: A study with a CFD Test Case

Achim Basermann, Melven Rohrig-Zollner, Joachim Illmer

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Tags: CUDA, Fluid dynamics, Fortran, nVidia, Performance, Python, Tesla C2075

December 22, 2015 by hgpu

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Dumitrel Loghin, Lavanya Ramapantulu, Oana Barbu, Yong Meng Teo

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 750 Ti, nVidia Jetson TK1

December 22, 2015 by hgpu

Autotuning Stencils Codes with Algorithmic Skeletons

Chris Cummins

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Package

December 19, 2015 by hgpu

Investigation of the SYCL for OpenCL Programming Model

Tim Beattie

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, OpenCL, Performance, Thesis

December 19, 2015 by hgpu

Study, Modelling and Implementation of the Level Set Method Used in Micromachining Processes

Carles Montoliu Alvaro

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 560, nVidia GeForce GTX Titan, Thesis

December 19, 2015 by hgpu

Challenges Adapting CUDA PIC Codes to multiple GPUs

Olav Emil Eiksund

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Tags: Benchmarking, CUDA, Electrostatics, FFT, nVidia, nVidia GeForce GTX 980, OpenMP, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Tesla K20, Thesis

December 19, 2015 by hgpu

Efficient Query Processing in Co-Processor-accelerated Databases

Sebastian Bress

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Tags: Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 640, Package, Tesla C1060, Tesla C2050, Thesis

December 19, 2015 by hgpu

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

CONCUR: Benchmarking LLMs for Concurrent Code Generation

HIPRT: Ray Tracing using HIP

Ray Tracing using HIP

MXFP4 Training Support Codebase

Practical FP4 Training for Large-Scale MoE Models on Hopper GPUs

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

On the Way to Future’s High Energy Particle Physics Transport Code

SqueezCL: Squeezing OpenCL Kernels for Approximate Computing on Contemporary GPUs

On the Development and Implementation of High-Order Flux Reconstruction Schemes for Computational Fluid Dynamics

OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures

Parallel FDTD Arithmetic Simulation Based on Distributed Heterogeneous Cluster System

Performance and Productivity of Parallel Python Programming: A study with a CFD Test Case

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Autotuning Stencils Codes with Algorithmic Skeletons

Investigation of the SYCL for OpenCL Programming Model

Study, Modelling and Implementation of the Level Set Method Used in Micromachining Processes

Challenges Adapting CUDA PIC Codes to multiple GPUs

Efficient Query Processing in Co-Processor-accelerated Databases

Recent source codes

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

CONCUR: a benchmark designed to evaluate multithreaded Java code generated by LLMs

HIPRT: Ray Tracing using HIP

MXFP4 Training Support Codebase

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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