high performance computing on graphics processing units: hgpu.org

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Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Lingyu Duan, Wei Sun, Xinfeng Zhang, Jie Chen, Jianxiong Yin, Simon See, Tiejun Huang, Alex C. Kot, Wen Gao

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Tags: Benchmarking, CUDA, Hybrid computing, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia Tegra TX1, Tesla M40

June 1, 2017 by hgpu

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

Xiaoming Chen, Jianxu Chen, Danny Z. Chen, Xiaobo Sharon Hu

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Tesla K40

June 1, 2017 by hgpu

A Unified Optimization Approach for Sparse Tensor Operations on GPUs

Bangtian Liu, Chengyao Wen, Anand D.Sarwate, Maryam Mehri Dehnavi

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, nVidia GeForce GTX Titan X, Sparse matrix

June 1, 2017 by hgpu

Accelerating Discrete Wavelet Transforms on GPUs

David Barina, Michal Kula, Michal Matysek, Pavel Zemcik

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Tags: ATI, ATI Radeon HD 6970, Computer science, Discrete Wavelet Transform, HLSL, Image processing, nVidia, nVidia GeForce GTX Titan X, OpenCL, Pixel shaders

May 24, 2017 by hgpu

Implementing Efficient, Portable Computations for Machine Learning

Matthew Walter Moskewicz

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Tags: Algorithms, AMD R9 Nano, ATI, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Thesis

May 24, 2017 by hgpu

Parallel and in-process compilation of individuals for genetic programming on GPU

Hakan Ayral, Songul Albayrak

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Tags: Computer science, CUDA, Genetic programming, nVidia, nVidia GRID K520, Operating systems, Package

May 24, 2017 by hgpu

Espresso: Efficient Forward Propagation for BCNNs

Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 960

May 24, 2017 by hgpu

GPUMap: A Transparently GPU-Accelerated Map Function

Ivan Pachev

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Tags: Computer science, CUDA, Distributed computing, Java, MapReduce, nVidia, nVidia GeForce GTX 980, Python, Thesis

May 22, 2017 by hgpu

MEDINA: MECCA Development in Accelerators – KPP Fortran to CUDA source-to-source Preprocessor

Michail Alvanos, Theodoros Christoudias

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Tags: Chemistry, Compilers, Computer science, CUDA, Fortran, nVidia, Package, Python

May 22, 2017 by hgpu

Inferring the Scheduling Policies of an Embedded CUDA GPU

Nathan Otterness, Ming Yang, Tanya Amert, James H. Anderson, F. Donelson Smith

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Tegra TX1, Package, Task scheduling

May 22, 2017 by hgpu

CLBlast: A Tuned OpenCL BLAS Library

Cedric Nugteren

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Tags: AMD Radeon R9 M370X, ARM, ATI, BLAS, Computer science, Intel HD 5100, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan X, OpenCL, Package

May 18, 2017 by hgpu

Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models

Saptarshi Das, Xi Chen, Michael P. Hobson

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Tags: CUDA, Differential equations, Earth and Space Sciences, Geophysics, Heterogeneous systems, nVidia, Partial differential equations, PDEs, Seismology, Spectral methods, Tesla C2075, Tesla K20, Visualization, Wave equation

May 18, 2017 by hgpu

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

exa-AMD: An Exascale-Ready Framework for Accelerating the Discovery and Design of Functional Materials

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high performance computing on graphics processing units: hgpu.org

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs

A Unified Optimization Approach for Sparse Tensor Operations on GPUs

Accelerating Discrete Wavelet Transforms on GPUs

Implementing Efficient, Portable Computations for Machine Learning

Parallel and in-process compilation of individuals for genetic programming on GPU

Espresso: Efficient Forward Propagation for BCNNs

GPUMap: A Transparently GPU-Accelerated Map Function

MEDINA: MECCA Development in Accelerators – KPP Fortran to CUDA source-to-source Preprocessor

Inferring the Scheduling Policies of an Embedded CUDA GPU

CLBlast: A Tuned OpenCL BLAS Library

Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models

Recent source codes

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

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