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Data Parallelism Exploiting for H.264 Encoder

Mei Wen, Ju Ren, Nan Wu, Huayou Su, Changqing Xun, Chunyuan Zhang

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Tags: Algorithms, Computer science, Data parallelism, DSP, H.264/AVC, Video encoding

August 12, 2011 by hgpu

Performance Comparison with OpenMP Parallelization for Multi-core Systems

Chao-Tung Yang, Tzu-Chieh Chang, Hsien-Yi Wang, William C.C. Chu, Chih-Hung Chang

Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, Fortran, nVidia, OpenCL, OpenMP, Performance

August 8, 2011 by hgpu

Unifying stream based and reconfigurable computing to design application accelerators

Bruno Francisco, Frederico Pratas, Leonel Sousa

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Tags: Bioinformatics, Biology, Computer science, Data parallelism, FPGA, nVidia, nVidia GeForce GTX 285

August 2, 2011 by hgpu

A parallel accelerator for semantic search

Abhinandan Majumdar, Srihari Cadambi, Srimat T. Chakradhar, Hans Peter Graf

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Databases, FPGA, nVidia, Semantic indexing, Tesla C1060

July 30, 2011 by hgpu

On the Robust Mapping of Dynamic Programming onto a Graphics Processing Unit

Shucai Xiao, A.M. Aji, Wu-chun Feng

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Tags: Algorithms, Bioinformatics, Biology, Cell processor, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 280, Sequence alignment, Smith-Waterman algorithm

July 23, 2011 by hgpu

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Byunghyun Jang, Dana Schaa, Perhaad Mistry, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, Brook, Computer science, CUDA, Data parallelism, Memory model, nVidia, nVidia GeForce GTX 285, Review

June 17, 2011 by hgpu

Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs

Lifan Xu, Michela Taufer, Stuart Collins, Dionisios G. Vlachos

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Tags: Chemistry, CUDA, Data parallelism, GPU cluster, Monte Carlo simulation, nVidia, Physics, Tesla S1070

June 9, 2011 by hgpu

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

K.A. Hawick, A. Leist, and D.P. Playne

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Tags: Cell processor, Computer science, CUDA, Data parallelism, Finite difference, nVidia, nVidia GeForce GTX 295, OpenCL, Partial differential equations, PDEs, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

Data-parallel algorithms and data structures

John Owens

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Tags: Algorithms, Computer science, Data parallelism, Presentation

January 6, 2011 by hgpu

Data-parallel computing

Chas. Boyd

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Tags: Computer science, Data parallelism, Review

January 5, 2011 by hgpu

Scientific Programming for Heterogeneous Systems – Bridging the Gap between Algorithms and Applications

Alexey Lastovetsky

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Tags: Algorithms, Computer science, Data parallelism

December 15, 2010 by hgpu

MPI within a GPU

Bobby Dalton Young

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Tags: Computer science, CUDA, Data parallelism, MPI, nVidia, nVidia GeForce 8800 GTS, Thesis, Virtualization

November 5, 2010 by hgpu

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

See all packages

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high performance computing on graphics processing units: hgpu.org

Data Parallelism Exploiting for H.264 Encoder

Performance Comparison with OpenMP Parallelization for Multi-core Systems

Unifying stream based and reconfigurable computing to design application accelerators

A parallel accelerator for semantic search

On the Robust Mapping of Dynamic Programming onto a Graphics Processing Unit

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Parallelization of tau-leap coarse-grained Monte Carlo simulations on GPUs

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

Data-parallel algorithms and data structures

Data-parallel computing

Scientific Programming for Heterogeneous Systems – Bridging the Gap between Algorithms and Applications

MPI within a GPU

Recent source codes

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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