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Anti-parallel Patterns in Fine-grain Data-parallel Programs

Jan Cornelis

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Tags: Benchmarking, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce 8500 GT, nVidia GeForce GTX 280, Performance, Thesis

October 10, 2011 by hgpu

CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems

Aleksandar Ilic, Leonel Sousa

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Tags: Computer science, CUDA, Data parallelism, FFT, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Signal processing

October 2, 2011 by hgpu

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

Ashwin Prasad, Jayvant Anantpur, R. Govindarajan

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Tags: Benchmarking, Clustering, Computer science, CUDA, Data parallelism, Flow analysis, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTS, Optimization

September 20, 2011 by hgpu

A data parallel view on polyhedral process networks

Ana Balevic, Bart Kienhuis

Tags: Computer science, Data parallelism, Heterogeneous systems

September 11, 2011 by hgpu

Reliability modeling of MEMS devices on CUDA based HPC setup

Rohit Pathak, Satyadhar Joshi

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Tags: Computer science, CUDA, Data parallelism, nVidia, Performance

August 31, 2011 by hgpu

Collaborative execution environment for heterogeneous parallel systems

Aleksandar Ilic, Leonel Sousa

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Tags: Computer science, CUBLAS, CUDA, Data parallelism, FFT, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Presentation

August 28, 2011 by hgpu

Accelerating tetrahedral interpolation with data-level and Thread-Level Parallel optimization

Jaewoo Ahn, Becksang Seong, Wonyong Sung

Tags: Algorithms, Data parallelism, Image processing, Optimization

August 26, 2011 by hgpu

Experience of parallelizing cryo-EM 3D reconstruction on a CPU-GPU heterogeneous system

Linchuan Li, Xingjian Li, Guangming Tan, Mingyu Chen, Peiheng Zhang

Tags: Algorithms, Computer science, Cryo-EM, CUDA, Data parallelism, GPU cluster, Heterogeneous systems, MPI, nVidia, Performance, Tesla C2050

August 24, 2011 by hgpu

Accelerating Haskell array codes with multicore GPUs

Manuel M.T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, Vinod Grover

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Tags: Code generation, Computer science, CUBLAS, CUDA, Data parallelism, nVidia, Performance, Programming Languages, Tesla T10

August 22, 2011 by hgpu

Auto-tuning SkePU: a multi-backend skeleton programming framework for multi-GPU systems

Usman Dastgeer, Johan Enmyren, Christoph W. Kessler

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Tags: Computer science, CUDA, Data parallelism, nVidia, OpenCL, OpenMP, Package, Performance, Programming techniques, Tesla C1060

August 19, 2011 by hgpu

SkePU: a multi-backend skeleton programming library for multi-GPU systems

Johan Enmyren, Christoph W. Kessler

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Source codes

Tags: Computer science, CUDA, Data parallelism, nVidia, OpenCL, OpenMP, Package, Performance, Programming techniques, Tesla C1060

August 18, 2011 by hgpu

Simulating Biological-Inspired Spiking Neural Networks with OpenCL

Jorn Hoffmann, Karim El-Laithy, Frank Guttler, Martin Bogdan

Tags: Computer science, Data parallelism, Neural networks, OpenCL

August 18, 2011 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

Anti-parallel Patterns in Fine-grain Data-parallel Programs

CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

A data parallel view on polyhedral process networks

Reliability modeling of MEMS devices on CUDA based HPC setup

Collaborative execution environment for heterogeneous parallel systems

Accelerating tetrahedral interpolation with data-level and Thread-Level Parallel optimization

Experience of parallelizing cryo-EM 3D reconstruction on a CPU-GPU heterogeneous system

Accelerating Haskell array codes with multicore GPUs

Auto-tuning SkePU: a multi-backend skeleton programming framework for multi-GPU systems

SkePU: a multi-backend skeleton programming library for multi-GPU systems

Simulating Biological-Inspired Spiking Neural Networks with OpenCL

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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