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Characterization and Analysis of Dynamic Parallelism in Unstructured GPU Applications

Jin Wang, Sudhakar Yalamanchili

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX Titan, Tesla K20, Tesla K40

September 11, 2014 by hgpu

Optimistic Parallelism on GPUs

Min Feng, Rajiv Gupta, Laxmi N. Bhuyan

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Tags: Computer science, CUDA, Data parallelism, nVidia, Performance, Tesla C1060

September 2, 2014 by hgpu

GPU Computing to Improve Game Engine Performance

Abu Asaduzzaman, Hin Y. Lee

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Tags: Computer science, CUDA, Data parallelism, Games, Image processing, nVidia, nVidia GeForce 8800 GTX, Performance

July 28, 2014 by hgpu

Solution Level Parallelization of Local Search Metaheuristic Algorithm on GPU

S. V. Ghorpade, S. M. Kamalapur

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Metaheuristics, nVidia, nVidia GeForce GTX 680, Search

July 23, 2014 by hgpu

Data Parallel Quadtree Indexing and Spatial Query Processing of Complex Polygon Data on GPUs

Jianting Zhang, Simin You, Le Gruenwald

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Databases, nVidia, nVidia GeForce GTX Titan

July 20, 2014 by hgpu

Combining Data Parallelism and Task Parallelism for Efficient Performance on Hybrid CPU and GPU Systems

Aditya Deshpande

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Tags: Algorithms, Compression, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 580, nVidia Quadro FX 3700, Package, Sorting, Sparse matrix, Tesla K20, Thesis

July 11, 2014 by hgpu

targetDP: an Abstraction of Lattice Based Parallelism with Portable Performance

Alan Gray, Kevin Stratford

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Tags: CUDA, Data parallelism, Fluid dynamics, Intel Xeon Phi, Lattice Boltzmann model, nVidia, Tesla K40

May 20, 2014 by hgpu

Investigating the Impact of Data Parallelism and GPU Technology on Computer Gaming

Abu Asaduzzaman, Deepthi Gummadi

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Tags: Computer science, CUDA, Data parallelism, Games, nVidia, Tesla C2075

May 17, 2014 by hgpu

A Language for Nested Data Parallel Design-space Exploration on GPUs

Bo Joel Svensson, Mary Sheeran, Ryan R. Newton

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Tags: Computer science, CUDA, Data parallelism, Haskell, nVidia, nVidia GeForce GTX 680, Tesla C2070

April 11, 2014 by hgpu

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

Ashis Kumar Dash

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Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, nVidia Quadro FX 3700

January 29, 2014 by hgpu

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang

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Tags: Computer science, Computer vision, CUDA, Data parallelism, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K20

December 24, 2013 by hgpu

Heterogeneous Programming with Single Operation Multiple Data

Herve Paulino, Eduardo Marques

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Tags: Computer science, CUDA, Data parallelism, Heterogeneous systems, Java, nVidia, nVidia GeForce GT 320 M, Tesla C2050

December 19, 2013 by hgpu

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

See all packages

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high performance computing on graphics processing units: hgpu.org

Characterization and Analysis of Dynamic Parallelism in Unstructured GPU Applications

Optimistic Parallelism on GPUs

GPU Computing to Improve Game Engine Performance

Solution Level Parallelization of Local Search Metaheuristic Algorithm on GPU

Data Parallel Quadtree Indexing and Spatial Query Processing of Complex Polygon Data on GPUs

Combining Data Parallelism and Task Parallelism for Efficient Performance on Hybrid CPU and GPU Systems

targetDP: an Abstraction of Lattice Based Parallelism with Portable Performance

Investigating the Impact of Data Parallelism and GPU Technology on Computer Gaming

A Language for Nested Data Parallel Design-space Exploration on GPUs

A Performance Criteria for parallel Computation on basis of block size using CUDA Architecture

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Heterogeneous Programming with Single Operation Multiple Data

Recent source codes

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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