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MetaFork: A Compilation Framework for Concurrency Models Targeting Hardware Accelerators and Its Application to the Generation of Parametric CUDA Kernels

Changbo Chen, Xiaohui Chen, Abdoul-Kader Keita, Marc Moreno Maza, Ning Xie

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Tags: Code generation, Computer science, CUDA, nVidia, Package

October 18, 2015 by hgpu

Sapporo2: A versatile direct N-body library

Jeroen Bedorf, Evghenii Gaburov, Simon Portegies Zwart

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Tags: Astrophysics, ATI, ATI Radeon HD 7970, CUDA, Instrumentation and Methods for Astrophysics, Mathematical Software, N-body simulation, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Tesla K20

October 16, 2015 by hgpu

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

Felix Weninger, Johannes Bergmann, Bjorn Schuller

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 560, Package, Speech recognition

October 8, 2015 by hgpu

Kinematic Modelling of Disc Galaxies using Graphics Processing Units

Georgios Bekiaris, Karl Glazebrook, Christopher J. Fluke, Roberto Abraham

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Tags: Algorithms, Astrophysics, CUDA, nVidia, Package, Tesla C2070, Tesla K40

October 8, 2015 by hgpu

GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection

Gil Shapira, Tal Hassner

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Tags: Algorithms, Computer science, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 720, Package

October 8, 2015 by hgpu

MAGMA Embedded: Towards a Dense Linear Algebra Library for Energy Efficient Extreme Computing

Azzam Haidar, Stanimire Tomov, Piotr Luszczek, Jack Dongarra

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia Jetson TK1, Package

October 6, 2015 by hgpu

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Rahul Garg

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Tags: AMD Radeon HD 8650, ATI, ATI Radeon HD 7970, Benchmarking, Code generation, Computer science, Intel HD Graphics 4000, Matlab, Matrix multiplication, nVidia, nVidia GeForce GT 650 M, OpenCL, Package, Python, Tesla C2050, Thesis

October 6, 2015 by hgpu

Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards

Ang Li, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Package, Sparse direct solvers, Tesla K20

September 30, 2015 by hgpu

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

John-Alexander M. Assael

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Tags: CNN, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Tesla K40, Thesis

September 26, 2015 by hgpu

A GPU accelerated Barnes-Hut Tree Code for FLASH4

Gunther Lukat, Robi Banerjee

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Tags: Astrophysics, CUDA, Fortran, Gravitation, Instrumentation and Methods for Astrophysics, nVidia, Package, Python, Tesla C2075

September 26, 2015 by hgpu

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

Vlad Olaru, Mihai Florea, Cristian Sminchisescu

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Tags: Algorithms, Computer science, Computer vision, CUDA, Matlab, nVidia, nVidia GeForce GTX Titan Black, Package, Tesla K40

September 24, 2015 by hgpu

CLTune: A Generic Auto-Tuner for OpenCL Kernels

Cedric Nugteren, Valeriu Codreanu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Intel HD 5100, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Performance, Tesla K40

September 17, 2015 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

MetaFork: A Compilation Framework for Concurrency Models Targeting Hardware Accelerators and Its Application to the Generation of Parametric CUDA Kernels

Sapporo2: A versatile direct N-body library

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

Kinematic Modelling of Disc Galaxies using Graphics Processing Units

GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection

MAGMA Embedded: Towards a Dense Linear Algebra Library for Energy Efficient Extreme Computing

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Analysis of A Splitting Approach for the Parallel Solution of Linear Systems on GPU Cards

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks

A GPU accelerated Barnes-Hut Tree Code for FLASH4

A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation

CLTune: A Generic Auto-Tuner for OpenCL Kernels

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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