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Efficient Particle-Mesh Spreading on GPUs

Xiangyu Guo, Xing Liu, Peng Xu, Zhihui Du, Edmond Chow

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Tesla K40

October 29, 2014 by hgpu

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Lokendra Singh Panwar

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Computational Complexity, Computer science, CUDA, Finite difference, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenCL, Seismology, Tesla C1060, Tesla C2050, Thesis

October 29, 2014 by hgpu

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K10, Tesla K20

October 29, 2014 by hgpu

Testing and Exposing Weak Graphics Processing Unit Memory Models

Tyler Rey Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, PTX, Tesla C2075, Thesis

October 27, 2014 by hgpu

Finding Longest Common Subsequences by GPU-Based Parallel Ant Colony Optimization

David Markvica

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Tags: Algorithms, Biology, Computational biology, Computer science, Heterogeneous systems, Metaheuristics, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Optimization, Pattern recognition, Thesis

October 27, 2014 by hgpu

Contract-Based General-Purpose GPU Programming

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer

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Source codes

Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package, Software Engineering

October 27, 2014 by hgpu

GPGPU Acceleration for Skeletal Animation-comparing OpenCL with CUDA and GLSL

Shousheng Liu, Ge Chen, Chunyong Ma, Yong Han

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Tags: ATI, ATI Radeon HD 7750, Computer science, CUDA, GLSL, nVidia, nVidia GeForce GTS 250, nVidia GeForce GTX 670, OpenCL, OpenGL, Performance, Rendering

October 25, 2014 by hgpu

Evacuation Route Modeling and Planning with General Purpose GPU Computing

David D. Prentiss

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Tags: Algorithms, ATI, ATI Radeon HD 7770, Computer science, OpenCL, Path problems, Thesis

October 25, 2014 by hgpu

CUVLE: Variable-Length Encoding on CUDA

Jose M Gonzalez-Linares, Antonio Fuentes-Alventosa, Juan Gomez-Luna, Nicolas Guil

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 550 Ti

October 25, 2014 by hgpu

Query Optimization in Heterogeneous CPU/GPU Environment for Time Series Databases

Piotr Przymus

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Tags: Algorithms, Compression, Computer science, CUDA, Data mining, Databases, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, Tesla C2070, Tesla M2070, Thesis

October 24, 2014 by hgpu

Gaussian Process Models with Parallelization and GPU acceleration

Zhenwen Dai, Andreas Damianou, James Hensman, Neil Lawrence

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Tags: Algorithms, Computer science, Machine learning, nVidia, nVidia GeForce GTX 480

October 24, 2014 by hgpu

Monitoring Large-scale Microblog on GPUs

Yunpeng Cao

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9400 GT, String matchning

October 24, 2014 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

Efficient Particle-Mesh Spreading on GPUs

Performance Modeling, Optimization, and Characterization on Heterogeneous Architectures

Parallel training of Deep Neural Networks with Natural Gradient and Parameter Averaging

Testing and Exposing Weak Graphics Processing Unit Memory Models

Finding Longest Common Subsequences by GPU-Based Parallel Ant Colony Optimization

Contract-Based General-Purpose GPU Programming

GPGPU Acceleration for Skeletal Animation-comparing OpenCL with CUDA and GLSL

Evacuation Route Modeling and Planning with General Purpose GPU Computing

CUVLE: Variable-Length Encoding on CUDA

Query Optimization in Heterogeneous CPU/GPU Environment for Time Series Databases

Gaussian Process Models with Parallelization and GPU acceleration

Monitoring Large-scale Microblog on GPUs

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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