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Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

Robert Hochberg

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce 9600 M GT, nVidia GeForce GT 330 M, Tesla C1060

April 27, 2012 by hgpu

High-performance sparse matrix-vector multiplication on GPUs for structured grid computations

Jeswin Godwin, Justin Holewinski, P. Sadayappan

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Tags: Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla C2050

April 25, 2012 by hgpu

Radio Astronomy Beam Forming on Many-Core Architectures

Alessio Sclocco, Ana Lucia Varbanescu, Jan David Mol, Rob V. van Nieuwpoort

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Tags: Astrophysics, ATI, ATI Radeon HD 6970, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Signal processing

April 25, 2012 by hgpu

An Efficient Work-Distribution Strategy for Gridding Radio-Telescope Data on GPUs

John W. Romein

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Tags: Astrophysics, ATI, ATI Radeon HD 7970, CUDA, nVidia, nVidia GeForce GTX 680, OpenCL, Performance

April 25, 2012 by hgpu

Lynx: A Dynamic Instrumentation System for Data-Parallel Applications on GPGPU Architectures

Naila Farooqui, Andrew Kerr, Greg Eisenhauer, Karsten Schwan, Sudhakar Yalamanchili

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Optimization, Package, Tesla C2050

April 25, 2012 by hgpu

Polymer Field-Theory Simulations on Graphics Processing Units

Kris T. Delaney, Glenn H. Fredrickson

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Tags: Computational Physics, CUDA, Field equations, MPI, nVidia, Physics, Polymer Systems, Soft Condensed Matter, Statistical Mechanics, Tesla C2050

April 25, 2012 by hgpu

The Bones Source-to-Source Compiler Manual

Cedric Nugteren

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Tags: Algorithms, Code generation, Computer science, CUDA, MPI, nVidia, OpenCL, Package

April 25, 2012 by hgpu

Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing

John A. Stratton, Christopher Rodrigues, I-Jui Sung, Nady Obeid, Li-Wen Chang, Nasser Anssari, Geng Daniel Liu, Wen-mei W. Hwu

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Molecular simulation, MPI, nVidia, nVidia GeForce GTX 480, Optimization, Package

April 25, 2012 by hgpu

WebCL for Hardware-Accelerated Web Applications

Won Jeon, Tasneem Brutch, Simon Gibbs

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Tags: Algorithms, Computer science, Java, nVidia, nVidia GeForce GT 330 M, OpenCL, Package

April 25, 2012 by hgpu

Comparison of Different Parallel Implementaions of the 2+1-Dimensional KPZ Model and the 3-Dimensional KMC Model

Jeffrey Kelling, Odor Geza, Mate Nagy Ferenc, Henrik Schulz, Karl-Heinz Heinig

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 6970, Computer science, CUDA, Materials Science, MPI, nVidia, OpenCL, Physics, Tesla C1060, Tesla C2050, Tesla C2070

April 25, 2012 by hgpu

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

Takayuki Muranushi

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Tags: Algorithms, Astrophysics, CUDA, Differential equations, Instrumentation and Methods for Astrophysics, nVidia, Package, Partial differential equations, PDEs, Tesla M2050

April 25, 2012 by hgpu

Real-time video breakup detection for multiple HD video streams on a single GPU

Jakub Rosner, Hannes Fassold, Martin Winter, Peter Schallauer

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Tags: CUDA, Fermi architecture, GPU, nVidia, nVidia GeForce GTX 480, Real-time video breakup, video distortions, video quality

April 24, 2012 by HannesF99

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

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

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high performance computing on graphics processing units: hgpu.org

Matrix Multiplication with CUDA – A basic introduction to the CUDA programming model

High-performance sparse matrix-vector multiplication on GPUs for structured grid computations

Radio Astronomy Beam Forming on Many-Core Architectures

An Efficient Work-Distribution Strategy for Gridding Radio-Telescope Data on GPUs

Lynx: A Dynamic Instrumentation System for Data-Parallel Applications on GPGPU Architectures

Polymer Field-Theory Simulations on Graphics Processing Units

The Bones Source-to-Source Compiler Manual

Parboil: A Revised Benchmark Suite for Scientific and Commercial Throughput Computing

WebCL for Hardware-Accelerated Web Applications

Comparison of Different Parallel Implementaions of the 2+1-Dimensional KPZ Model and the 3-Dimensional KMC Model

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

Real-time video breakup detection for multiple HD video streams on a single GPU

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

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

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