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Parallel Acceleration on Manycore Systems and Its Performance Analysis: OpenCL Case Study

Rafael Alejandro Vejarano, Phuong Thi Yen, Jeong-Gun Lee

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Tags: Computer science, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 460, OpenCL, Performance

June 4, 2013 by hgpu

Analyzing Locality of Memory References in GPU Architectures

Saurabh Gupta, Ping Xiang, Huiyang Zhou

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Tags: Computer science, GPGPU-sim, Matrix multiplication, nVidia, nVidia GeForce GTX 480

May 16, 2013 by hgpu

A portable and high-performance matrix operations library for CPUs, GPUs and beyond

Rahul Garg, Laurie Hendren

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Tags: ATI, ATI Radeon HD 5850, ATI Radeon HD 7970, Computer science, Matrix multiplication, nVidia, nVidia GeForce GT 650 M, OpenCL, Tesla C2050

May 11, 2013 by hgpu

GPU Sparse Matrix Multiplication with CUDA

Sean Rose

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tutorial

May 9, 2013 by hgpu

Improving Numerical Accuracy for Non-Negative Matrix Multiplication on GPUs using Recursive Algorithms

Matthew Badin, Paolo D'Alberto, Lubomir Bic, Michael Dillencourt, Alexandru Nicolau

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Tags: Algorithms, BLAS, Computer science, CUDA, Matrix multiplication, nVidia, Performance, Tesla C2070

April 30, 2013 by hgpu

Modeling and Optimization of Parallel Matrix-based Computations on GPU

Andrew White

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Optimization, Tesla S1070, Thesis

April 27, 2013 by hgpu

Co-processing SPMD Computation on GPUs and CPUs on Shared Memory System

Hui Li, Geoffrey Fox, Gregor Laszewski, Zhenhua Guo, Judy Qiu

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Tags: Clustering, CUDA, Heterogeneous systems, MapReduce, Matrix multiplication, nVidia

April 13, 2013 by hgpu

Optimizing Sparse Matrix-Matrix Multiplication for the GPU

Steven Dalton, Nathan Bell, Luke N. Olson

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Sorting, Sparse matrix, Tesla C2075

April 7, 2013 by hgpu

Parallel Implementation of Dynamic Programming Algorithm Using Graphics Processing Unit

M. Khurram Rehman, M. Umer Sarwar, M. Ramzan Talib, M. Salman Mansoor, M. Bilal Sarwar

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GT 525 M, nVidia GeForce GT 610 M, Sequence alignment

April 6, 2013 by hgpu

Scaling up scientific computations by using map-reduce-like control flow on NUMA architectures

Vlad Slavici

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, MapReduce, Matrix multiplication, nVidia, Tesla M2090, Thesis

April 3, 2013 by hgpu

Accelerated Dictionary Learning with GPU/Multicore CPU and Its Application to Music Classification

Boyang Gao, Emmanuel Dellandrea, Liming Chen

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Tags: Clustering, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Signal processing

March 26, 2013 by hgpu

Parallel Interpretation of L-system Based on CUDA

Sulan Zhang, Qingsheng Zhu, Ji Liu, Lingqiu Zeng

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Tags: Computer science, CUDA, Image generation, Matrix multiplication, nVidia, nVidia GeForce GTS 250

March 24, 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

Parallel Acceleration on Manycore Systems and Its Performance Analysis: OpenCL Case Study

Analyzing Locality of Memory References in GPU Architectures

A portable and high-performance matrix operations library for CPUs, GPUs and beyond

GPU Sparse Matrix Multiplication with CUDA

Improving Numerical Accuracy for Non-Negative Matrix Multiplication on GPUs using Recursive Algorithms

Modeling and Optimization of Parallel Matrix-based Computations on GPU

Co-processing SPMD Computation on GPUs and CPUs on Shared Memory System

Optimizing Sparse Matrix-Matrix Multiplication for the GPU

Parallel Implementation of Dynamic Programming Algorithm Using Graphics Processing Unit

Scaling up scientific computations by using map-reduce-like control flow on NUMA architectures

Accelerated Dictionary Learning with GPU/Multicore CPU and Its Application to Music Classification

Parallel Interpretation of L-system Based on CUDA

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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