high performance computing on graphics processing units: hgpu.org

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Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Shoaib Kamil, Derrick Coetzee, Armando Fox

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Python

December 12, 2011 by hgpu

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

Arturo Ribes, Senshan Ji, Arnau Ramisa, Ramon Lopez De Mantaras

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Tags: Clustering, Computer science, Computer vision, CUDA, nVidia, SIFT

December 11, 2011 by hgpu

Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research

Martin Peniak, Anthony Morse, Christopher Larcombe, Salomon Ramirez-Contla, Angelo Cangelosi

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 470, Package, Tesla C1060

December 11, 2011 by hgpu

Accelerating Swarm Intelligence Algorithms with GPU-Computing

Robin M. Weiss

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Tags: Algorithms, Cluster analysis, Clustering, Computer science, CUDA, Data mining, nVidia, Tesla C1060

December 11, 2011 by hgpu

Fast Face Detection Using Graphics Processor

Kailash Devrari, K.Vinay Kumar

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Tags: Algorithms, Computer science, Computer vision, CUDA, Image recognition, nVidia, nVidia Quadro FX 580

December 11, 2011 by hgpu

A Dynamic Approach to Weighted Suffix Tree Construction Algorithm

Binay Kumar Pandey, Rajdeep Niyogi, Ankush Mittal

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Tags: Algorithms, Cluster computing, Computer science, CUDA, nVidia, nVidia Quadro FX 3700, Suffix trees

December 11, 2011 by hgpu

Generalizing Execution of Vectorizable Computations by Generating Vector Oriented Byte Code

Troels Blum

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 210 M, nVidia GeForce GTX 460, Package, Python, Thesis

December 11, 2011 by hgpu

Evaluating graph coloring on GPUs

A.V. Pascal Grosset, Peihong Zhu, Shusen Liu, Suresh Venkatasubramanian, Mary Hall

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Programming techniques

December 11, 2011 by hgpu

Achieving High Throughput Sequencing with Graphics Processing Units

Su Chen, Chaochao Zhang, Feng Shen, Ling Bai, Hai Jiang, Damir Herman

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Tags: Algorithms, Bioinformatics, Computer science, CUDA, nVidia, Sequence alignment, Tesla C2050

December 10, 2011 by hgpu

Fast and Memory-Efficient Minimum Spanning Tree on the GPU

Scott Rostrup, Shweta Srivastava, Kishore Singhal

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Tags: Algorithms, Computer science, Graph theory, nVidia, Performance, Tesla S1070, Tesla T10

December 10, 2011 by hgpu

Playdoh: A lightweight Python library for distributed computing and optimisation

Cyrille Rossant, Bertrand Fontaine, Dan F.M. Goodman

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Tags: Computer science, CUDA, Differential equations, Distributed computing, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Package, Partial differential equations, PDEs, Python

December 10, 2011 by hgpu

Load Balancing Utilizing Data Redundancy in Distributed Volume Rendering

S. Frey, T. Ertl

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Rendering

December 9, 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

Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research

Accelerating Swarm Intelligence Algorithms with GPU-Computing

Fast Face Detection Using Graphics Processor

A Dynamic Approach to Weighted Suffix Tree Construction Algorithm

Generalizing Execution of Vectorizable Computations by Generating Vector Oriented Byte Code

Evaluating graph coloring on GPUs

Achieving High Throughput Sequencing with Graphics Processing Units

Fast and Memory-Efficient Minimum Spanning Tree on the GPU

Playdoh: A lightweight Python library for distributed computing and optimisation

Load Balancing Utilizing Data Redundancy in Distributed Volume Rendering

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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