high performance computing on graphics processing units: hgpu.org

Federico Simmross-Wattenberg, Manuel Rodriguez-Cayetano, Javier Royuela-del-Val, Elena Martin-Gonzalez, Elisa Moya-Saez, Marcos Martin-Fernandez, Carlos Alberola-Lopez

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Source codes

Tags: Heterogeneous systems, Image processing, Medicine, OpenCL, Package

August 5, 2018 by hgpu

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Rohan Garg, Apoore Mohan, Michael Sullivan, Gene Cooperman

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla P100

August 5, 2018 by hgpu

Optimization of OpenCL applications on FPGA

Albert Navarro Torrento

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Tags: Computer science, FPGA, OpenCL, Optimization, Thesis

July 28, 2018 by hgpu

Elementary functions: towards automatically generated, efficient, and vectorizable implementations

Hugues De Lassus Saint-Genies

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Tags: Algorithms, Code generation, Computer science, Elementary functions, Performance, Thesis

July 28, 2018 by hgpu

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

Sergey Khrapov, Alexander Khoperskov

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Tags: Astrophysics, CUDA, hydrodynamics, nVidia, OpenMP, Tesla K20, Tesla K40, Tesla K80

July 28, 2018 by hgpu

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Xiaowang Zhang, Mingyue Zhang, Peng Peng, Jiaming Song, Zhiyong Feng, Lei Zou

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Tags: Computer science, CUDA, Databases, nVidia, Sparse matrix, Tesla M40

July 28, 2018 by hgpu

Block-Size Independence for GPU Programs

Rajeev Alur, Joseph Devietti, Nimit Singhania

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Performance

July 28, 2018 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

GPU parallelization of a hybrid pseudospectral fluid turbulence framework using CUDA

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

GPU schedulers: how fair is fair enough?

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Optimization of OpenCL applications on FPGA

Elementary functions: towards automatically generated, efficient, and vectorizable implementations

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Block-Size Independence for GPU Programs

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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