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CrowdCL: Web-Based Volunteer Computing with WebCL

Tommy MacWilliam, Cris Cecka

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Tags: Computer science, Distributed computing, Java, nVidia, nVidia GeForce GT 320 M, OpenCL, Package, Tesla K20

July 7, 2013 by hgpu

vSMC: Parallel Sequential Monte Carlo in C++

Yan Zhou

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Tags: Algorithms, Bayesian, Mathematical Software, Mathematics, nVidia, nVidia Quadro FX 2000, OpenCL, Package, Physics, Signal processing, Statistics

July 1, 2013 by hgpu

Efficient computation of constrained parameterizations on parallel platforms

Theodoros Athanasiadis, Georgios Zioupos, Ioannis Fudos

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Tags: 3D Graphics and Realism, Computer science, nVidia, OpenCL, Package, Tesla C2070

June 29, 2013 by hgpu

GPU Programming in Rust: Implementing High Level Abstractions in a Systems Level Language

Eric Holk, Milinda Pathirage, Arun Chauhan, Andrew Lumsdaine, Nicholas D. Matsakis

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 460, OpenCL, Package, PTX

June 17, 2013 by hgpu

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Xavier Andrade, Alan Aspuru-Guzik

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Tags: ATI, ATI Radeon HD 7970, Computational Physics, Condensed matter, Data parallelism, nVidia, OpenCL, Package, Physics, Tesla K20

June 14, 2013 by hgpu

Effects of Concurrency Techniques and Algorithm Performance: A Comparative Analysis of Single-Threaded, Multi-Threaded, and GPGPU Programming Techniques

Joshua Penton

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Tags: ATI, ATI Radeon HD 6750 M, Computer science, FFT, Java, OpenCL, Package, Performance, Programming techniques

May 28, 2013 by hgpu

An Investigation of the Performance Portability of OpenCL

S.J. Pennycook, S.D. Hammond, S.A. Wright, J.A. Herdman, I. Miller, S.A. Jarvis

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Tags: ATI, ATI FirePro V7800, Benchmarking, Computer science, CUDA, Fortran, MPI, nVidia, OpenCL, Package, Performance, Tesla C1060, Tesla C2050

May 21, 2013 by hgpu

CLgrep: A Parallel String Matching Tool

Peng Wu

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Package, String matching, Thesis

May 20, 2013 by hgpu

Point Spread Function Estimation of Solar Surface Images with a Cooperative Particle Swarm Optimization on GPUs

Peter Frank Perroni

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Tags: Algorithms, Astrophysics, Image processing, nVidia, OpenCL, Package, Particle swarm optimization, Tesla C2050, Thesis

May 16, 2013 by hgpu

Accelerating Financial Applications on the GPU

Scott Grauer-Gray, William Killian, Robert Searles, John Cavazos

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Tags: Computational finance, CUDA, Finance, nVidia, OpenACC, OpenCL, Package, Review, Tesla C2050, Tesla K20

May 6, 2013 by hgpu

Directive-based Approach to Heterogeneous Computing

Ruyman Reyes Castro

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenACC, OpenCL, Package, Python, Tesla C1060, Thesis

April 25, 2013 by hgpu

Solving Wave Equations on Unstructured Geometries

Andreas Klockner, Timothy Warburton, Jan S. Hesthaven

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Tags: Computer science, Mathematical Software, Numerical Analysis, nVidia, nVidia GeForce GTX 295, OpenCL, Package, Python, Wave equation

April 23, 2013 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

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

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Programming

CrowdCL: Web-Based Volunteer Computing with WebCL

vSMC: Parallel Sequential Monte Carlo in C++

Efficient computation of constrained parameterizations on parallel platforms

GPU Programming in Rust: Implementing High Level Abstractions in a Systems Level Language

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Effects of Concurrency Techniques and Algorithm Performance: A Comparative Analysis of Single-Threaded, Multi-Threaded, and GPGPU Programming Techniques

An Investigation of the Performance Portability of OpenCL

CLgrep: A Parallel String Matching Tool

Point Spread Function Estimation of Solar Surface Images with a Cooperative Particle Swarm Optimization on GPUs

Accelerating Financial Applications on the GPU

Directive-based Approach to Heterogeneous Computing

Solving Wave Equations on Unstructured Geometries

Recent source codes

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

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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