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A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms

William R. Saunders, James Grant, Eike H. Muller

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Tags: Algorithms, Biology, Chemistry, Code generation, Computer science, CUDA, Differential equations, DSL, Molecular dynamics, nVidia, Package, Partial differential equations, PDEs, Physics, Python, Tesla K20

April 15, 2017 by hgpu

Acceleration of Linear Finite-Difference Poisson-Boltzmann Methods on Graphics Processing Units

Ruxi Qi, Wesley M. Botello-Smith, Ray Luo

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Tags: Biomolecules, Biophysics, Boltzmann equation, Computational Physics, CUBLAS, CUDA, Electrostatics, Molecular simulation, nVidia, nVidia GeForce GTX 980 Ti, Physics, Poisson Boltzmann

April 11, 2017 by hgpu

Performance and energy optimization of the iterative solution of sparse linear systems on multicore processors

Maria Barreda Vaya

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OmpSs, Tesla C2050, Thesis

April 11, 2017 by hgpu

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

Siyang Qin, Roberto Manduchi

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Tags: Algorithms, Caffe, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan X

April 7, 2017 by hgpu

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

Julian Romera

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenMPI, Performance, Tesla K80, Tesla M2090, Thesis

April 7, 2017 by hgpu

Evaluation of Libraries for Parallel Computing in Haskell – A Case Study with a Super-resolution Application

Takuya Matsumoto, Kiminori Matsuzaki

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Tags: Computer science, CUDA, Haskell, Image processing, MRI, nVidia, nVidia GeForce GTX 750 Ti

April 3, 2017 by hgpu

Sparse Matrix-Vector Multiplication on GPGPUs

Salvatore Filippone, Valeria Cardellini, Davide Barbieri, Alessandro Fanfarillo

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Tags: Computer science, CUDA, Fortran, nVidia, nVidia GeForce GTX 660, Package, Review, Sparse matrix, Tesla K20, Tesla K40, Tesla M2070

April 3, 2017 by hgpu

Chai: Collaborative Heterogeneous Applications for Integrated-architectures

Juan Gomez-Luna, Izzat El Hajj, Li-Wen Chang, Victor Garcia-Flores, Simon Garcia de Gonzalo, Thomas B. Jablin, Antonio J. Pena, Wen-mei Hwu

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

April 3, 2017 by hgpu

Energy conservation techniques for GPU computing

Xinxin Mei

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 980, Task scheduling, Thesis

March 28, 2017 by hgpu

Comparing Programmer Productivity in OpenACC and CUDA: an Empirical Investigation

Xuechao Li, Po-Chou Shih, Jeffrey Overbey, Cheryl Seals, Alvin Lim

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Tags: Computer science, CUDA, nVidia, OpenACC, Package

March 20, 2017 by hgpu

Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow

Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matlab, Neural networks, nVidia, nVidia GeForce GTX 960, Package, TensorFlow, Torch

March 20, 2017 by hgpu

GPU accelerated population annealing algorithm

Lev Yu. Barash, Martin Weigel, Michal Borovsky, Wolfhard Janke, Lev N. Shchur

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Tags: Algorithms, CUDA, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Tesla K80

March 14, 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

Programming

A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms

Acceleration of Linear Finite-Difference Poisson-Boltzmann Methods on Graphics Processing Units

Performance and energy optimization of the iterative solution of sparse linear systems on multicore processors

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

Evaluation of Libraries for Parallel Computing in Haskell – A Case Study with a Super-resolution Application

Sparse Matrix-Vector Multiplication on GPGPUs

Chai: Collaborative Heterogeneous Applications for Integrated-architectures

Energy conservation techniques for GPU computing

Comparing Programmer Productivity in OpenACC and CUDA: an Empirical Investigation

Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow

GPU accelerated population annealing algorithm

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)