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High-level GPU computing with jacket for MATLAB and C/C++

Gallagher Pryor, Brett Lucey, Sandeep Maddipatla, Chris McClanahan, John Melonakos, Vishwanath Venugopalakrishnan, Krunal Patel, Pavan Yalamanchili, James Malcolm

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Tags: Computer science, CUDA, FFT, Image processing, Linear Algebra, nVidia, nVidia GeForce 9600, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenGL, Package, PTX, Tesla C1060, Tesla C2070

September 26, 2011 by hgpu

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Peng Du, Rick Weber, Piotr Luszczek, Stanimire Tomov, Gregory Peterson, Jack Dongarra

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Performance, Tesla C2050

September 26, 2011 by hgpu

Identifying scalar behavior in CUDA kernels

Sylvain Collange

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Tags: Benchmarking, Code generation, Compilers, Computer science, CUDA, nVidia, PTX

September 26, 2011 by hgpu

Putting Automatic Polyhedral Compilation for GPGPU to Work

Soufiane Baghdadi, Armin Grosslinger, Albert Cohen

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, Package

September 26, 2011 by hgpu

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Code generation, Computer science, nVidia, OpenCL, Optimization, Statistics, Tesla C1060

September 25, 2011 by hgpu

Harnessing the Power of GPUs without Losing Abstractions in SaC and ArrayOL: A Comparative Study

Jing Guo, Wendell Rodrigues, Jeyarajan Thiyagalingamz, Frederic Guyomarch, Pierre Boulet, Sven-Bodo Scholz

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Video encoding

September 25, 2011 by hgpu

Accelerating image recognition on mobile devices using GPGPU

Miguel Bordallo Lopez, Henri Nykanen, Jari Hannuksela, Olli Silven, Markku Vehvilainen

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Tags: Algorithms, Computer science, Image processing, Image recognition, OpenCL, OpenGL

September 25, 2011 by hgpu

GPGPU workload analysis and media performance studies

Ashwini Simha

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Tags: ATI, Benchmarking, Computer science, OpenCL, Performance, Thesis

September 25, 2011 by hgpu

Numerical Accuracy Differences in CPU and GPGPU Codes

Devon Yablonski

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Tags: Computer science, CUDA, Floating point error, nVidia, Tesla C1060, Thesis

September 25, 2011 by hgpu

The Test and Evaluation Uses of Heterogeneous Computing: GPGPUs and Other Approaches

Dan M. Davis, Gene Wagenbreth, Robert F. Lucas, Paul C. Gregory

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Tags: Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, Overview, Tesla C1060

September 25, 2011 by hgpu

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Keun Soo Yim, Cuong Pham, Mushfiq Saleheen, Zbigniew Kalbarczyk, Ravishankar Iyer

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Tags: Computer science, CUDA, Error recovery, Fault tolerance, nVidia, Tesla S1070

September 25, 2011 by hgpu

Utilising OpenCL Framework for Ray-Tracing Acceleration

Pavel Hradsky

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Tags: 3D Graphics and Realism, Computer science, nVidia, nVidia GeForce GTS 250, OpenCL, Raytracing, Thesis

September 24, 2011 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

See all packages

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high performance computing on graphics processing units: hgpu.org

High-level GPU computing with jacket for MATLAB and C/C++

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Identifying scalar behavior in CUDA kernels

Putting Automatic Polyhedral Compilation for GPGPU to Work

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Harnessing the Power of GPUs without Losing Abstractions in SaC and ArrayOL: A Comparative Study

Accelerating image recognition on mobile devices using GPGPU

GPGPU workload analysis and media performance studies

Numerical Accuracy Differences in CPU and GPGPU Codes

The Test and Evaluation Uses of Heterogeneous Computing: GPGPUs and Other Approaches

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Utilising OpenCL Framework for Ray-Tracing Acceleration

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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