high performance computing on graphics processing units: hgpu.org

Mauricio Araya-Polo, Javier Cabezas, Mauricio Hanzich, Miquel Pericas, Felix Rubio, Isaac Gelado, Muhammad Shafiq, Enric Morancho, Nacho Navarro, Eduard Ayguade, Jose M. Cela, Mateo Valero

Tags: Algorithms, Earth and Space Sciences, nVidia, Seismology

January 26, 2011 by hgpu

Architectural Support for the Stream Execution Model on General-Purpose Processors

Jayanth Gummaraju, Mattan Erez, Joel Coburn, Mendel Rosenblum, William J. Dally

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Tags: Algorithms, Architecture, Computer science, Performance

January 26, 2011 by hgpu

On the Visualization of Social and other Scale-Free Networks

Yuntao Jia, Jared Hoberock, Michael Garland, John C. Hart

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Tags: Algorithms, Computer science, Visualization

January 24, 2011 by hgpu

MLS-based scalar fields over triangle meshes and their application in mesh processing

Jingyi Jin, Michael Garland, Edgar A. Ramos

Tags: 3D Graphics and Realism, Algorithms, Computer science, Triangular meshes

January 24, 2011 by hgpu

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Xiao-Long Wu, Nady Obeid, Wen-Mei Hwu

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization

January 24, 2011 by hgpu

Efficient Parallel Scan Algorithms for GPUs

Shubhabrata Sengupta, Mark Harris, Michael Garland

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Package

January 24, 2011 by hgpu

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

Visualization with stylized line primitives

An hardware architecture for 3D object tracking and motion estimation

Real-Time Shadow Volume Algorithm for Subdivision Surface Based Models

Global Depth from Epipolar Volumes – A General Framework for Reconstructing Non-Lambertian Surfaces

Image-Space Collision Detection Through Alternate Surface Peeling

Application-independent accurate mouse placements on surfaces of arbitrary geometry

Assessing Accelerator-Based HPC Reverse Time Migration

Architectural Support for the Stream Execution Model on General-Purpose Processors

On the Visualization of Social and other Scale-Free Networks

MLS-based scalar fields over triangle meshes and their application in mesh processing

Exploiting More Parallelism from Applications Having Generalized Reductions on GPU Architectures

Efficient Parallel Scan Algorithms for GPUs

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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