high performance computing on graphics processing units: hgpu.org

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Range query processing in a multi-GPU environment

Ricardo J. Barrientos, Jose I. Gomez, Christian Tenllado, Manuel Prieto Matias, Mauricio Marin

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Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Databases, GPU cluster, nVidia, OpenMP, Tesla C1060

June 11, 2012 by hgpu

Using the GPGPU for Scaling Up Mining Software Repositories

Rina Nagano, Hiroki Nakamura, Yasutaka Kamei, Bram Adams, Kenji Hisazumi, Naoyasu Ubayashi, Akira Fukuda

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Tags: Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 480, Package

June 10, 2012 by hgpu

CUDA Kernel Design for GPU-Based Beam Dynamics Simulations

K. Amyx, J. Balasalle, I. Pogorelov, M. Borland, R. Soliday, Y.Wang

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Tags: Computer science, CUDA, nVidia, Tesla C2070

June 10, 2012 by hgpu

S-buffer: Sparsity-aware Multi-fragment Rendering

Andreas A. Vasilakis, Ioannis Fudos

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Tags: Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, OpenGL, Package, Rendering

June 10, 2012 by hgpu

Measuring the Impact of Configuration Parameters in CUDA Through Benchmarking

Yuri Torres, Arturo Gonzalez-Escribano, Diego R. Llanos

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

June 10, 2012 by hgpu

Scaling Fast Multipole Methods up to 4000 GPUs

Rio Yokota, Lorena Barba, Tetsu Narumi, Kenji Yasuoka

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Tags: Algorithms, Computer science, CUDA, Fast multipole method, MPI, N-body simulation, nVidia, Tesla M2050

June 9, 2012 by hgpu

Autotuning Stencil-Based Computations on GPUs

Azamat Mametjanov, Daniel Lowell, Ching-Chen May, Boyana Norris

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Tags: Computer science, CUDA, Differential equations, nVidia, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C1060, Tesla C2070

June 9, 2012 by hgpu

Encapsulated synchronization and load-balance in heterogeneous programming

Yuri Torres, Arturo Gonzalez-Escribano, Diego Llanos

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8500 GT, nVidia GeForce 9600 GT, Performance

June 9, 2012 by hgpu

Sparse LU Factorization for Parallel Circuit Simulation on GPU

Ling Ren, Xiaoming Chen, Yu Wang, Chenxi Zhang, Huazhong Yang

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Tags: ATI, ATI Radeon HD 5870, CAD, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

June 9, 2012 by hgpu

Ameliorating Memory Contention of OLAP operators on GPU Processors

Evangelia A. Sitaridi, Kenneth A. Ross

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Tags: Computer science, CUDA, Databases, nVidia, Tesla C2070

June 8, 2012 by hgpu

A Comparison of Algebraic Multigrid Preconditioners using Graphics Processing Units and Multi-Core Central Processing Units

Markus Wagner, Karl Rupp, Josef Weinbub

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Tags: Computer science, nVidia, nVidia GeForce GTX 470, OpenCL, Performance

June 8, 2012 by hgpu

Parallel random variates generator for GPUs based on normal numbers

Gleb Beliakov, Michael Johnstone, Doug Creighton, Tim Wilkin

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, Numerical Analysis, nVidia, Probability, Pseudo-random number generators, Tesla C2050

June 8, 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

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high performance computing on graphics processing units: hgpu.org

Range query processing in a multi-GPU environment

Using the GPGPU for Scaling Up Mining Software Repositories

CUDA Kernel Design for GPU-Based Beam Dynamics Simulations

S-buffer: Sparsity-aware Multi-fragment Rendering

Measuring the Impact of Configuration Parameters in CUDA Through Benchmarking

Scaling Fast Multipole Methods up to 4000 GPUs

Autotuning Stencil-Based Computations on GPUs

Encapsulated synchronization and load-balance in heterogeneous programming

Sparse LU Factorization for Parallel Circuit Simulation on GPU

Ameliorating Memory Contention of OLAP operators on GPU Processors

A Comparison of Algebraic Multigrid Preconditioners using Graphics Processing Units and Multi-Core Central Processing Units

Parallel random variates generator for GPUs based on normal numbers

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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