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Two Algorithms for Sorting On Heterogeneous Clusters

Kyle Spafford, Jeremy Meredith, Jeffrey Vetter, Aparna Chandramowlishwaran, David Noble, Richard Vuduc

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, OpenMP, Sorting, Tesla M2070

June 19, 2012 by hgpu

Parallel Rendering on Hybrid Multi-GPU Clusters

S. Eilemann, A. Bilgili, M. Abdellah, J. Hernando, M. Makhinya, R. Pajarola, F. Schurmann

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Tags: 3D Graphics and Realism, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 580, OpenGL, Optimization, Rendering, Visualization

June 19, 2012 by hgpu

Optimizing dataflow applications on heterogeneous environments

George Teodoro, Timothy D. R. Hartley, Umit V. Catalyurek, Renato Ferreira

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 260, Tesla C2050

June 19, 2012 by hgpu

Gdev: First-Class GPU Resource Management in the Operating System

Shinpei Kato, Michael McThrow, Carlos Maltzahn, Scott Brandt

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Operating systems, Package

June 18, 2012 by hgpu

An Improved CUDA-Based Implementation of Differential Evolution on GPU

A. K. Qin, Federico Raimondo, Florence Forbes, Yew Soon Ong

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Tags: Computer science, CUDA, Differential evolution, nVidia, nVidia GeForce GTX 560

June 18, 2012 by hgpu

OpenCL for programming shared memory multicore CPUs

Akhtar Ali, Usman Dastgeer, Christoph Kessler

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla M2050

June 18, 2012 by hgpu

OpenACC – First Experiences with Real-World Applications

Sandra Wienke, Paul Springer, Christian Terboven, Dieter an Mey

Tags: Computer science, Fortran, Heterogeneous systems, nVidia, OpenACC, OpenCL, Tesla C2050

June 18, 2012 by hgpu

GPUmotif: An Ultra-Fast and Energy-Efficient Motif Analysis Program Using Graphics Processing Units

Pooya Zandevakili, Ming Hu, Zhaohui Qin

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Tags: Algorithms, Biochemistry, Biology, Chemistry, Computational biology, Computer science, CUDA, Genetics, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, Package

June 17, 2012 by hgpu

Branch and Data Herding: Reducing Control and Memory Divergence for Error-tolerant GPU Applications

John Sartori, Rakesh Kumar

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

June 17, 2012 by hgpu

ScatterAlloc: Massively Parallel Dynamic Memory Allocation for the GPU

Steinberger Markus, Kenzel Michael, Kainz Bernhard, Schmalstieg Dieter

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Tags: Computer science, CUDA, Hashing, Memory, nVidia, nVidia Quadro FX 6000, Package

June 16, 2012 by hgpu

E-MOGA: A General Purpose Platform for Multi Objective Genetic Algorithm running on CUDA

Sami M. Salamin, Mohammed Aldasht, Hashem Tamimi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9500 GTX, Optimization

June 16, 2012 by hgpu

Accelerating Lambert’s Problem on the GPU in MATLAB

Nathan L. Parrish

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Tags: Algorithms, Computer science, CUDA, Fortran, nVidia, nVidia GeForce GT 430, nVidia GeForce GT 540 M, nVidia GeForce GTX 560 Ti, Package

June 16, 2012 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

Two Algorithms for Sorting On Heterogeneous Clusters

Parallel Rendering on Hybrid Multi-GPU Clusters

Optimizing dataflow applications on heterogeneous environments

Gdev: First-Class GPU Resource Management in the Operating System

An Improved CUDA-Based Implementation of Differential Evolution on GPU

OpenCL for programming shared memory multicore CPUs

OpenACC – First Experiences with Real-World Applications

GPUmotif: An Ultra-Fast and Energy-Efficient Motif Analysis Program Using Graphics Processing Units

Branch and Data Herding: Reducing Control and Memory Divergence for Error-tolerant GPU Applications

ScatterAlloc: Massively Parallel Dynamic Memory Allocation for the GPU

E-MOGA: A General Purpose Platform for Multi Objective Genetic Algorithm running on CUDA

Accelerating Lambert’s Problem on the GPU in MATLAB

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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