high performance computing on graphics processing units: hgpu.org

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Benchmarking the Intel Xeon Phi Coprocessor

F. Masci

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Performance

February 11, 2014 by hgpu

Point Rendering in CUDA Path Tracer

Hsuan-Yueh Peng

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 780, Rendering

February 11, 2014 by hgpu

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Ping Guo, Liqiang Wang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Performance, Sparse matrix, Tesla C2050, Tesla K20, Tesla M2090

February 11, 2014 by hgpu

Implementing the Projected Spatial Rich Features on a GPU

Andrew D. Ker

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Tags: Computer science, CUDA, Machine learning, nVidia, Tesla K20

February 11, 2014 by hgpu

Genetically Improved CUDA C++ Software

W. B. Langdon, M. Harman

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 580, nVidia Quadro NVS 290, Package, Tesla C2050, Tesla K20, Tesla T10

February 11, 2014 by hgpu

GPGPU-Assisted Subpixel Tracking Method for Fiducial Markers

Naoki Shibata, Shinya Yamamoto

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Tags: Augmented reality, Computer science, Java, nVidia, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 670, OpenCL

February 9, 2014 by hgpu

Benchmarks for Intel MIC Architecture

Emprise Y.K. Chan

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Monte Carlo simulation

February 9, 2014 by hgpu

Extending the SkelCL Skeleton Library for Stencil Computations on Multi-GPU Systems

Stefan Breuer, Michel Steuwer, Sergei Gorlatch

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Tags: Computer science, nVidia, OpenCL, Tesla S1070

February 9, 2014 by hgpu

A Scalable Multi-Path Microarchitecture for Efficient GPU Control Flow

Ahmed ElTantawy, Jessica Wenjie Ma, Mike O'Connor, Tor M. Aamodt

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Tags: Computer science, Hardware Architecture, Performance

February 9, 2014 by hgpu

Fast 3D Graphics Rendering Technique with CUDA Parallel Processing

Ji-Hoon Kang, Syung-Og An, Shin-Jin Kang, Seok-Hun Kim, SooKyun Kim

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Tags: 3D Graphics and Realism, Computer science, CUDA, GLSL, nVidia, nVidia GeForce GTX 285, OpenGL, Raytracing, Rendering

February 8, 2014 by hgpu

Developmental Directions in Parallel Accelerators

K.A. Hawick, D.P. Playne

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan

February 8, 2014 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 5, 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

Benchmarking the Intel Xeon Phi Coprocessor

Point Rendering in CUDA Path Tracer

Accurate Cross-Architecture Performance Modeling for Sparse Matrix-Vector Multiplication (SpMV) on GPUs

Implementing the Projected Spatial Rich Features on a GPU

Genetically Improved CUDA C++ Software

GPGPU-Assisted Subpixel Tracking Method for Fiducial Markers

Benchmarks for Intel MIC Architecture

Extending the SkelCL Skeleton Library for Stencil Computations on Multi-GPU Systems

A Scalable Multi-Path Microarchitecture for Efficient GPU Control Flow

Fast 3D Graphics Rendering Technique with CUDA Parallel Processing

Developmental Directions in Parallel Accelerators

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

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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