Applications

hgpu.org » Applications

Democratizing General Purpose GPU Programming through OpenCL and Scala

Raider Beck, Helge Willum Larsen, Tommy Jensen

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Tags: Benchmarking, Code generation, Compilers, Computer science, nVidia, OpenCL, Package, Tesla C870

October 24, 2011 by hgpu

The MOSIX Virtual OpenCL (VCL) Cluster Platform

Amnon Barak, Amnon Shiloh

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Tags: APU, Computer science, GPU cluster, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 480, OpenCL, Operating systems, Package

October 24, 2011 by hgpu

Power and Performance Studies of the Explicit Multi-Threading (XMT) Architecture

Fuat Keceli

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Tags: Algorithms, ASIC, Benchmarking, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Package, Performance, Thesis

October 23, 2011 by hgpu

DOPA: GPU-based protein alignment using database and memory access optimizations

Laiq Hasan, Marijn Kentie, Zaid Al-Ars

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Tags: Algorithms, Bioinformatics, Biology, Computational Complexity, CUDA, Databases, nVidia, nVidia GeForce GTX 275, Optimization, Package, Sequence alignment, Smith-Waterman algorithm

October 21, 2011 by hgpu

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

Sergei Morozov, Sudhanshu Mathur

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Tags: CUDA, Finance, Fortran, Machine learning, nVidia, nVidia GeForce GTX 280, Package

October 21, 2011 by hgpu

Designing the Language Liszt for Building Portable Mesh-based PDE Solvers

Zachary DeVito, Pat Hanrahan

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, Partial differential equations, PDEs, Performance, Programming Languages, Tesla C2050

October 19, 2011 by hgpu

AeminiumGPU: A CPU-GPU Hybrid Runtime for the Aeminium Language

Alcides Fonseca

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Tags: Compilers, Computer science, Java, nVidia, nVidia GeForce GTX 285, OpenCL, Optimization, Package, Thesis

October 19, 2011 by hgpu

GPU Parallel Collections For Scala

Kishen Das Kondabagilu Rajanna

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Tags: Compilers, Computer science, CUDA, Java, nVidia, nVidia GeForce 9800 GT, OpenCL, Package, Performance, Thesis

October 19, 2011 by hgpu

Design and Performance of the OP2 Library for Unstructured Mesh Applications

Carlo Bertolli, Adam Betts, Gihan Mudalige, Mike Giles, Paul Kelly

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, OpenCL, OpenMP, Package, Tesla C2050

October 18, 2011 by hgpu

Odeint – Solving ordinary differential equations in C++

Karsten Ahnert, Mario Mulansky

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Tags: Chaotic Dynamics, Computational Physics, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, ODEs, Ordinary differential equations, Package

October 18, 2011 by hgpu

The Anatomy of High-Performance 2D Similarity Calculations

Imran S. Haque, Vijay S. Pande, W. Patrick Walters

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Tags: Algorithms, Biochemistry, Chemistry, Clustering, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, Package

October 16, 2011 by hgpu

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Naoya Maruyama, Tatsuo Nomura, Kento Sato, Satoshi Matsuoka

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Tags: Computer science, CUDA, MPI, nVidia, Optimization, Package, Performance, Programming Languages, Programming techniques, Tesla M2050

October 15, 2011 by hgpu

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

RepoLaunch: Automating Build & Test Pipeline of Code Repositories on ANY Language and ANY Platform

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high performance computing on graphics processing units: hgpu.org

Applications

Democratizing General Purpose GPU Programming through OpenCL and Scala

The MOSIX Virtual OpenCL (VCL) Cluster Platform

Power and Performance Studies of the Explicit Multi-Threading (XMT) Architecture

DOPA: GPU-based protein alignment using database and memory access optimizations

Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

Designing the Language Liszt for Building Portable Mesh-based PDE Solvers

AeminiumGPU: A CPU-GPU Hybrid Runtime for the Aeminium Language

GPU Parallel Collections For Scala

Design and Performance of the OP2 Library for Unstructured Mesh Applications

Odeint – Solving ordinary differential equations in C++

The Anatomy of High-Performance 2D Similarity Calculations

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers

Recent source codes

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

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

MSKernelBench & CUDAMaster

EvoScientist: Harness Vibe Research with Self-evolving AI Scientists

RepoLaunch: Automating Build and Test Pipeline of Code Repositories on ANY Language and ANY Platform

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