high performance computing on graphics processing units: hgpu.org

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Performance Improvement of Data Mining in Weka through GPU Acceleration

Tiago Augusto Engel, Andrea Schwertner Charao, Manuele Kirsch-Pinheiro, Luiz-Angelo Steffenel

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Tags: Comuter science, CUDA, Data mining, Java, Matrix multiplication, nVidia, nVidia Quadro K 2000, Package, Tesla K20, Tesla M2050

June 13, 2014 by hgpu

Enabling CP2K Application for Exascale Computing with Accelerators using OpenACC and OpenCL

Mariusz Uchronski, Agnieszka Kwiecien, Marcin Gebarowski

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Tags: ATI, ATI Radeon HD 6950, Chemistry, CUDA, Matrix multiplication, Molecular simulation, MPI, nVidia, OpenACC, OpenCL, Sparse matrix, Tesla M2075

May 16, 2014 by hgpu

Preliminary results of autotuning GEMM kernels for the NVIDIA Kepler architecture-GeForce GTX 680

Jakub Kurzak, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 680

May 14, 2014 by hgpu

Toward optimised skeletons for heterogeneous parallel architecture with performance cost model

Khari A. Armih

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MapReduce, Matrix multiplication, nVidia, nVidia GeForce GT 520, Thesis

April 21, 2014 by hgpu

On optimization techniques for the matrix multiplication on hybrid CPU+GPU platforms

Luis-Pedro Garcia, Javier Cuenca, Domingo Gimenez

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Tags: Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 590, Tesla K20

April 16, 2014 by hgpu

Optimising OpenCL kernels for the ARM Mali-T600 GPUs

Johan Gronqvist, Anton Lokhmotov

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Tags: Algorithms, ARM, Computer science, Matrix multiplication, OpenCL

March 18, 2014 by hgpu

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

Tarun Beri, Sorav Bansal, Subodh Kumar

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Tags: Computer science, CUDA, FFT, GPU cluster, Heterogeneous systems, Matrix multiplication, Memory model, nVidia, Prefetch, Task scheduling, Tesla M2070

February 11, 2014 by hgpu

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

Ashis Kumar Dash

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Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, nVidia Quadro FX 3700

January 29, 2014 by hgpu

Opportunities for Parallelism in Matrix Multiplication

Tyler M. Smith, Robert A. van de Geijn, Mikhail Smelyanskiy, Jeff R. Hammond and Field G. Van Zee

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Tags: Computer science, Intel Xeon Phi, Matrix multiplication

January 5, 2014 by hgpu

OpenCL Programming by Example

Ravishekhar Banger, Koushik Bhattacharyya

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Tags: Book, Computer science, Image processing, Matrix multiplication, Memory model, Nearest neighbour, OpenCL, OpenGL, Pattern recognition, Tutorial

December 27, 2013 by hgpu

Optimization of the Oktay-Kronfeld Action Conjugate Gradient Inverter

Yong-Chull Jang, Jon A. Bailey, Weonjong Lee, Carleton DeTar, Mehmet B. Oktay, Andreas S. Kronfeld

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Tags: CUDA, High Energy Physics - Lattice, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Physics

November 22, 2013 by hgpu

Anatomy of High-Performance Many-Threaded Matrix Multiplication

Tyler M. Smith, Robert van de Geijn, Mikhail Smelyanskiy, Jeff R. Hammond, Field Van Zee

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Tags: Computer science, Intel Phi, Intel Xeon Phi, Matrix multiplication, Performance

November 13, 2013 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

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

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high performance computing on graphics processing units: hgpu.org

Performance Improvement of Data Mining in Weka through GPU Acceleration

Enabling CP2K Application for Exascale Computing with Accelerators using OpenACC and OpenCL

Preliminary results of autotuning GEMM kernels for the NVIDIA Kepler architecture-GeForce GTX 680

Toward optimised skeletons for heterogeneous parallel architecture with performance cost model

On optimization techniques for the matrix multiplication on hybrid CPU+GPU platforms

Optimising OpenCL kernels for the ARM Mali-T600 GPUs

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

Opportunities for Parallelism in Matrix Multiplication

OpenCL Programming by Example

Optimization of the Oktay-Kronfeld Action Conjugate Gradient Inverter

Anatomy of High-Performance Many-Threaded Matrix Multiplication

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

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

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