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Machine Learning from Streaming Data in Heterogeneous Computing Environments

Yusuf Guven Toprakkiran

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 980, OpenCL, Thesis

April 11, 2017 by hgpu

Acceleration of Linear Finite-Difference Poisson-Boltzmann Methods on Graphics Processing Units

Ruxi Qi, Wesley M. Botello-Smith, Ray Luo

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Tags: Biomolecules, Biophysics, Boltzmann equation, Computational Physics, CUBLAS, CUDA, Electrostatics, Molecular simulation, nVidia, nVidia GeForce GTX 980 Ti, Physics, Poisson Boltzmann

April 11, 2017 by hgpu

Performance and energy optimization of the iterative solution of sparse linear systems on multicore processors

Maria Barreda Vaya

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OmpSs, Tesla C2050, Thesis

April 11, 2017 by hgpu

A modular GPU raytracer using OpenCL for non-interactive graphics

Henrique Nunes Jung, Vinicius Jurinic Cassol

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Package, Raytracing, Rendering

April 11, 2017 by hgpu

Vectorization of Hybrid Breadth First Search on the Intel Xeon Phi

Mireya Paredes, Graham Riley, Mikel Lujan

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Tags: Algorithms, Computer science, Intel Xeon Phi, OpenMP, Parallelization

April 11, 2017 by hgpu

Design Exploration of AES Accelerators on FPGAs and GPUs

Vincenzo Conti, Salvatore Vitabile

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Tags: AES, Computer science, FPGA, nVidia, nVidia GeForce GT 520, nVidia GeForce GT 555 M, OpenCL, Security

April 7, 2017 by hgpu

Load Balancing for Constraint Solving with GPUs

Pedro Roque, Vasco Pedro, Salvador Abreu

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Tags: ATI, ATI Radeon HD 7990, Computer science, Heterogeneous systems, Intel Xeon Phi, load balancing, nVidia, nVidia GeForce GTX 980 M, OpenCL, Tesla K20

April 7, 2017 by hgpu

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

Siyang Qin, Roberto Manduchi

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Tags: Algorithms, Caffe, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan X

April 7, 2017 by hgpu

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

Julian Romera

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenMPI, Performance, Tesla K80, Tesla M2090, Thesis

April 7, 2017 by hgpu

Merge or Separate? Multi-job Scheduling for OpenCL Kernels on CPU/GPU Platforms

Yuan Wen, Michael F.P. O'Boyle

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Tags: AMD Radeon HD 7970, ATI, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 780, OpenCL

April 3, 2017 by hgpu

Evaluation of Libraries for Parallel Computing in Haskell – A Case Study with a Super-resolution Application

Takuya Matsumoto, Kiminori Matsuzaki

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Tags: Computer science, CUDA, Haskell, Image processing, MRI, nVidia, nVidia GeForce GTX 750 Ti

April 3, 2017 by hgpu

Sparse Matrix-Vector Multiplication on GPGPUs

Salvatore Filippone, Valeria Cardellini, Davide Barbieri, Alessandro Fanfarillo

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Tags: Computer science, CUDA, Fortran, nVidia, nVidia GeForce GTX 660, Package, Review, Sparse matrix, Tesla K20, Tesla K40, Tesla M2070

April 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

Machine Learning from Streaming Data in Heterogeneous Computing Environments

Acceleration of Linear Finite-Difference Poisson-Boltzmann Methods on Graphics Processing Units

Performance and energy optimization of the iterative solution of sparse linear systems on multicore processors

A modular GPU raytracer using OpenCL for non-interactive graphics

Vectorization of Hybrid Breadth First Search on the Intel Xeon Phi

Design Exploration of AES Accelerators on FPGAs and GPUs

Load Balancing for Constraint Solving with GPUs

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

Merge or Separate? Multi-job Scheduling for OpenCL Kernels on CPU/GPU Platforms

Evaluation of Libraries for Parallel Computing in Haskell – A Case Study with a Super-resolution Application

Sparse Matrix-Vector Multiplication on GPGPUs

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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