Programming

hgpu.org » Programming

GPU acceleration and performance of the particle-beam-dynamics code Elegant

J.R. King, I.V. Pogorelov, K.M. Amyx, M. Borland, R. Soliday

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Tags: Computational Physics, CUDA, MPI, nVidia, Physics, Tesla K20

October 24, 2017 by hgpu

Revisiting the Case of ARM SoCs in High-Performance Computing Clusters

Tyler Fox

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, SoC, Thesis

October 21, 2017 by hgpu

Parallel Matching and Clustering Algorithms on GPUs

Md. Naim

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla K40, Thesis

October 21, 2017 by hgpu

How to distribute most efficiently a computation intensive calculation on an Android device to external compute units with an Android API

Lander Beckers, Henning Lakiere

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Tags: Android, Computer science, FPGA, Java, Matrix multiplication, OpenCL, SoC, Thesis

October 21, 2017 by hgpu

Computation of gray-level co-occurrence matrix based on CUDA and its optimization

Huichao Hong, Lixin Zheng, Shuwan Pan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 1050 Ti, Performance

October 21, 2017 by hgpu

Flexible FPGA design for FDTD using OpenCL

Tobias Kenter, Jens Forstner, Christian Plessl

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Tags: Computer science, FDTD, Finite-difference time-domain, FPGA, OpenCL

October 15, 2017 by hgpu

Accelerating Genomics Research with OpenCL and FPGAs

Chris Rauer, George S. Powley, Mir Ahsan, Nicholas Finamore

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Tags: Algorithms, Biology, Computer science, FPGA, Genomics, OpenCL

October 15, 2017 by hgpu

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

Li-Wen Chang

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, OpenCL, performance portability, Tesla C2050, Tesla K20, Thesis

October 15, 2017 by hgpu

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Adam Stooke, Pieter Abbeel

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, nVidia DGX-1, Package, Tesla P100, Theano

October 15, 2017 by hgpu

SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

Holger Homann, Francois Laenen

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Tags: Benchmarking, Computational Physics, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 755 M, Package, Performance, Physics, Tesla M2050

October 15, 2017 by hgpu

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Ole Martin Skafsa

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Tags: CNN, Computer science, Deep learning, FPGA, Machine learning, Neural networks, NLP, nVidia, nVidia Tegra TX1, OpenCL, Thesis

October 3, 2017 by hgpu

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

Satya P. Jammy, Christian T. Jacobs, David J. Lusher, Neil D. Sandham

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Finite difference, Intel Xeon Phi, nVidia, Tesla K40

October 3, 2017 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

GPU acceleration and performance of the particle-beam-dynamics code Elegant

Revisiting the Case of ARM SoCs in High-Performance Computing Clusters

Parallel Matching and Clustering Algorithms on GPUs

How to distribute most efficiently a computation intensive calculation on an Android device to external compute units with an Android API

Computation of gray-level co-occurrence matrix based on CUDA and its optimization

Flexible FPGA design for FDTD using OpenCL

Accelerating Genomics Research with OpenCL and FPGAs

Toward Performance Portability for CPUs and GPUs Through Algorithmic Compositions

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

FPGA implementation of a Convolutional Neural Network for "Wake up word" detection

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

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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