high performance computing on graphics processing units: hgpu.org

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Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

Woohyuk Choi, Sumin Hong, Won-Ki Jeong

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Tags: Clustering, Computer science, CUDA, Distributed computing, DSL, GPU cluster, Image processing, MapReduce, nVidia, nVidia GeForce GTX Titan, Python, Rendering, Spark

November 8, 2016 by hgpu

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Tarun Beri

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Tags: Computer science, CUDA, FFT, GPU cluster, Linear Algebra, Matrix multiplication, nVidia, Package, Prefetch, Tesla M2070, Thesis

November 5, 2016 by hgpu

Molecular Activity Prediction using Deep Learning Software Library

Yoshiki Kato, Shinji Hamada, Hitoshi Goto

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Tags: Bioinformatics, Chemistry, Computer science, CUDA, Deep learning, nVidia, Package, Python, Tesla K20

November 5, 2016 by hgpu

A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs

Elias Stehle, Hans-Arno Jacobsen

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, Sorting

November 5, 2016 by hgpu

Extensions and Limitations of the Neural GPU

Eric Price, Wojciech Zaremba, Ilya Sutskever

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Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, Package, Python

November 3, 2016 by hgpu

Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

Diego Fabregat-Traver, Davor Davidovic, Markus Hohnerbach, Edoardo Di Napoli

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20, Tesla K40, Tesla K80

November 3, 2016 by hgpu

Towards Automating Multi-dimensional Data Decomposition for Executing a Single-GPU Code on a Multi-GPU System

Ryotaro Sakai, Fumihiko Ino, Kenichi Hagihara

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Performance, Tesla K40

November 1, 2016 by hgpu

Programming Heterogeneous Systems from an Image Processing DSL

Jing Pu, Steven Bell, Xuan Yang, Jeff Setter, Stephen Richardson, Jonathan Ragan-Kelley, Mark Horowitz

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Tags: ARM, Computer science, Computer vision, CUDA, DSL, FPGA, Image processing, nVidia, nVidia Jetson TK1, OpenGL, Package

November 1, 2016 by hgpu

Performance Optimization of 3-D Lattice Boltzmann Flow Solver on a GPU

Nhat-Phuong Tran, Myungho Lee, Sugwon Hong

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Tags: Computer science, CUDA, Fluid dynamics, Lattice Boltzmann model, Numerical simulation, nVidia, Performance, Tesla K20

November 1, 2016 by hgpu

GPflow: A Gaussian process library using TensorFlow

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Leon-Villagra, Zoubin Ghahramani, James Hensman

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X, Package, Python, TensorFlow

October 29, 2016 by hgpu

GPU Performance Modeling and Optimization

Ang Li

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Tags: Computer science, CUDA, Hardware Architecture, nVidia, Performance, PTX, Thesis

October 29, 2016 by hgpu

GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

Yohei Miki, Masayuki Umemura

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce GTX Titan X, Tesla K20, Tesla M2090

October 29, 2016 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

Vispark: GPU-Accelerated Distributed Visual Computing Using Spark

UNICORN: A Bulk Synchronous Programming Model, Framework and Runtime for Hybrid CPU-GPU Clusters

Molecular Activity Prediction using Deep Learning Software Library

A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs

Extensions and Limitations of the Neural GPU

Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

Towards Automating Multi-dimensional Data Decomposition for Executing a Single-GPU Code on a Multi-GPU System

Programming Heterogeneous Systems from an Image Processing DSL

Performance Optimization of 3-D Lattice Boltzmann Flow Solver on a GPU

GPflow: A Gaussian process library using TensorFlow

GPU Performance Modeling and Optimization

GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

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)