high performance computing on graphics processing units: hgpu.org

Clemens Grelck, Kevin Hammond, Heinz Hertlein, Philip Holzenspies, Chris Jesshope, Raimund Kirner, Bernd Scheuermann, Alex Shafarenko, Iraneus Te Boekhorst, Volkmar Wieser

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 425 M, Performance, Visualization

December 29, 2011 by hgpu

Parallel computing system for the efficient calculation of molecular similarity based on negative electrostatic potential

Raul Ernesto Torres Carvajal

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Tags: Algorithms, Chemistry, Cluster analysis, CUDA, Electrostatic potential, Electrostatics, nVidia, nVidia GeForce GT 240, Pattern recognition, Thesis

December 29, 2011 by hgpu

Parallel Quadtree Coding of Large-Scale Raster Geospatial Data on Multicore CPUs and GPGPUs

Jianting Zhang, Simin You, Le Gruenwald

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Tags: CUDA, Data parallelism, Earth and Space Sciences, Geoscience, nVidia, Optimization, Sensing, Tesla C2050

December 29, 2011 by hgpu

Accelerating NBODY6 with Graphics Processing Units

Keigo Nitadori, Sverre J. Aarseth

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Source codes

Tags: Astrophysics, CUDA, N-body simulation, nVidia, nVidia GeForce GTX 470, nVidia GeForce GTX 560 Ti, Package

December 29, 2011 by hgpu

Improving the speed of neural networks on CPUs

Vincent Vanhoucke, Andrew Senior, Mark Z. Mao

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Tags: Computer science, CUDA, Neural networks, nVidia, Speech recognition, Tesla C2070, Tutorial

December 28, 2011 by hgpu

Multilevel Tile Load Map on Massive Terrain Visualization

Junlan Nie, Dongliang Guo, Yanfen Wang, Lingfu Kong, Yong Tang

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Tags: Algorithms, Computer science, GLSL, Image processing, nVidia, nVidia GeForce GT 240 M, OpenGL, Optimization, Rendering, Visualization

December 28, 2011 by hgpu

Speeding Up Particle Trajectory Simulations under Moving Force Fields using GPUs

Robert Patro, John P. Dickerson, Sujal Bista, Satyandra K. Gupta, Amitabh Varshney

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Tags: CUDA, nVidia, Optical trapping, Physics, Tesla S1070

December 28, 2011 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

Acceleration of PIC Simulation with GPU

Scalable Fast Multipole Methods on Distributed Heterogeneous Architectures

Parallelism, Patterns, and Performance in Iterative MRI Reconstruction

Visualization assisted by parallel processing

Engineering Concurrent Software Guided by Statistical Performance Analysis

Parallel computing system for the efficient calculation of molecular similarity based on negative electrostatic potential

Parallel Quadtree Coding of Large-Scale Raster Geospatial Data on Multicore CPUs and GPGPUs

Accelerating NBODY6 with Graphics Processing Units

Improving the speed of neural networks on CPUs

Multilevel Tile Load Map on Massive Terrain Visualization

Speeding Up Particle Trajectory Simulations under Moving Force Fields using GPUs

Recent source codes

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

INT v.s. FP: A framework to compare low-bit integer and float-point formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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