high performance computing on graphics processing units: hgpu.org

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Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation

Moritz Kreutzer, Georg Hager, Gerhard Wellein, Holger Fehske, Achim Basermann, Alan R. Bishop

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Tags: Computer science, GPU cluster, nVidia, Performance, Sparse matrix, Tesla C2050, Tesla C2070

December 26, 2011 by hgpu

Designing Fast Architecture Sensitive Tree Search on Modern Multi-Core/Many-Core Processors

Changkyu Kim, Jatin Chhugani, Nadathur Satish, Eric Sedlar, Anthony D. Nguyen, Tim Kaldewey, Victor W. Lee, Scott A. Brandt, Pradeep Dubey

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Tags: Compression, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 280, Search, Tesla C2050

December 25, 2011 by hgpu

Turbo Bayesian Compressed Sensing

Depeng Yang

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Tags: Algorithms, Bayesian, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 480, Sensing, Signal processing

December 25, 2011 by hgpu

Modeling Parallel Programs for Heterogeneous Computing

Ferosh Jacob

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance

December 25, 2011 by hgpu

Methodology of control and supervision of web connected mobile robots with CUDA technology application

Janusz Bedkowski, Andrzej Maslowski

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, nVidia Quadro FX 1600 M, nVidia Quadro FX 3700

December 25, 2011 by hgpu

Accelerating linear system solutions using randomization techniques

Marc Baboulin, Jack Dongarra, Julien Herrmann, Stanimire Tomov

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla C2050

December 25, 2011 by hgpu

G-CP: Providing Fault Tolerance on the GPU through Software Checkpointing

Felix Loh, Matt Sinclair

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Tags: Algorithms, Computer science, CUDA, Fault tolerance, nVidia, Tesla C1060

December 25, 2011 by hgpu

Design and Development of Optical Flow Based Obstacle Avoidance Using CUDA

S. Jeevith , N.D. Gangadhar, S. Santhosh Kumar

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Optical flow

December 25, 2011 by hgpu

Toolchain for programming, simulating and studying the XMT many-core architecture

Fuat Keceli, Alexandros Tzannes, George C. Caragea, Rajeev Barua, Uzi Vishkin

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Tags: Algorithms, Compilers, Computer science, Hardware Architecture, nVidia, nVidia GeForce GTX 280, Package

December 24, 2011 by hgpu

High Performance and Scalable Radix Sorting: A case study of implementing dynamic parallelism for GPU computing

Duane Merrill, Andrew Grimshaw

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Package, Sorting

December 24, 2011 by hgpu

Efficient Use of In-Game Ray-Tracing Techniques

Thales Luis Rodrigues Sabino, Paulo Andrade, Lucas Grassano Lattari, Esteban Clua, Anselmo Montenegro, Paulo Aristarco Pagliosa

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Tags: 3D Graphics and Realism, Computer science, CUDA, Games, GLSL, Image generation, nVidia, nVidia GeForce 9800 GTX, OpenGL, Raytracing, Rendering

December 24, 2011 by hgpu

Sorting on GPUs for large scale datasets: A thorough comparison

Gabriele Capannini, Fabrizio Silvestri, Ranieri Baraglia

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Sorting

December 24, 2011 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

See all packages

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high performance computing on graphics processing units: hgpu.org

Sparse matrix-vector multiplication on GPGPU clusters: A new storage format and a scalable implementation

Designing Fast Architecture Sensitive Tree Search on Modern Multi-Core/Many-Core Processors

Turbo Bayesian Compressed Sensing

Modeling Parallel Programs for Heterogeneous Computing

Methodology of control and supervision of web connected mobile robots with CUDA technology application

Accelerating linear system solutions using randomization techniques

G-CP: Providing Fault Tolerance on the GPU through Software Checkpointing

Design and Development of Optical Flow Based Obstacle Avoidance Using CUDA

Toolchain for programming, simulating and studying the XMT many-core architecture

High Performance and Scalable Radix Sorting: A case study of implementing dynamic parallelism for GPU computing

Efficient Use of In-Game Ray-Tracing Techniques

Sorting on GPUs for large scale datasets: A thorough comparison

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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