high performance computing on graphics processing units: hgpu.org

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Sorting with GPUs: A Survey

Dmitri I. Arkhipov, Di Wu, Keqin Li, Amelia C. Regan

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Tags: Algorithms, Computer science, CUDA, nVidia, Sorting, survey

September 12, 2017 by hgpu

A Comparative Study of 2D Numerical Methods with GPU Computing

Ben J. Zimmerman, Jonathan D. Regele, Bong Wie

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Tags: Computer science, CUDA, Finite volume method, Numerical simulation, nVidia, Tesla K20

September 12, 2017 by hgpu

Report: Performance comparison between C2075 and P100 GPU cards using cosmological correlation functions

Miguel Cardenas-Montes, Ivan Mendez-Jimenez, Juan Jose Rodriguez-Vazquez, Jose Maria Hernandez Calama

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C2075, Tesla P100

September 12, 2017 by hgpu

Beyond 16GB: Out-of-Core Stencil Computations

Istvan Z. Reguly, Gihan R. Mudalige, Michael B. Giles

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Tags: Computer science, CUDA, Intel Xeon Phi, MPI, nVidia, Stencil computation, Tesla P100

September 9, 2017 by hgpu

Progressive Clustering of Big Data with GPU Acceleration and Visualization

Jun Wang, Eric Papenhausen, Bing Wang, Sungsoo Ha, Alla Zelenyuk, Klaus Mueller

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Tags: Algorithms, big data, Clustering, Computer science, CUDA, nVidia, Tesla K20, Visualization

September 9, 2017 by hgpu

Data Layout Oriented Compilation Techniques in Vectorization for Multi-/Many-cores

Shixiong Xu

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 645, nVidia Jetson TK1

September 9, 2017 by hgpu

From MPI to MPI+OpenACC: Conversion of a legacy FORTRAN PCG solver for the spherical Laplace equation

Ronald M. Caplan, Zoran Mikic, Jon A. Linker

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Tags: Computer science, CUDA, Fortran, Laplace and Poisson equation, Mathematical Software, MPI, nVidia, OpenACC, performance portability, Tesla P100

September 7, 2017 by hgpu

Integer sorting on multicores: some (experiments and) observations

Alexandros V. Gerbessiotis

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Performance, Sorting, Tesla K40

September 3, 2017 by hgpu

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

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Tags: Computer science, CUDA, Deep learning, Image recognition, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Performance, TensorFlow, Tesla K40

September 3, 2017 by hgpu

Large Integer Arithmetic in GPU for Cryptography

Lee Wen Dick

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, Security, Thesis

August 26, 2017 by hgpu

Dynamic Parallelism in GPU Optimized Barnes Hut Trees for Molecular Dynamics Simulations

Melisa Carranza Zuniga

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Tags: Algorithms, Biology, Biomolecules, Biophysics, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Physics, Thesis

August 26, 2017 by hgpu

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

Xu Liu, Langshi Chen, Jesun S. Firoz, Judy Qiu, Lei Jiang

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Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1070

August 17, 2017 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

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

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

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Sorting with GPUs: A Survey

A Comparative Study of 2D Numerical Methods with GPU Computing

Report: Performance comparison between C2075 and P100 GPU cards using cosmological correlation functions

Beyond 16GB: Out-of-Core Stencil Computations

Progressive Clustering of Big Data with GPU Acceleration and Visualization

Data Layout Oriented Compilation Techniques in Vectorization for Multi-/Many-cores

From MPI to MPI+OpenACC: Conversion of a legacy FORTRAN PCG solver for the spherical Laplace equation

Integer sorting on multicores: some (experiments and) observations

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Large Integer Arithmetic in GPU for Cryptography

Dynamic Parallelism in GPU Optimized Barnes Hut Trees for Molecular Dynamics Simulations

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

Most viewed papers (last 30 days)