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Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

Qadha'a AlEnezi, Hosam AboElFotoh, Bader AlBdaiwi, Mohammad Ali AlMulla

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 560 M, P systems

March 3, 2016 by hgpu

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

Hanjoo Kim, Jaehong Park, Jaehee Jang, Sungroh Yoon

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Tags: Caffe, Computer science, CUDA, Deep learning, Distributed computing, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, Package

March 1, 2016 by hgpu

Alpaka – An Abstraction Library for Parallel Kernel Acceleration

Erik Zenker, Benjamin Worpitz, Rene Widera, Axel Huebl, Guido Juckeland, Andreas Knupfer, Wolfgang E. Nagel, Michael Bussmann

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Tags: Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, performance portability, Portability, Tesla K20, Tesla K80

March 1, 2016 by hgpu

Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design

Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, Stephen W. Keckler

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package, Tesla K40

March 1, 2016 by hgpu

Optimization of Molecular Dynamics Simulation Code and Applications to Biomolecular Systems

David M. Bowman

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Tags: Biochemistry, Biomolecules, Chemistry, CUDA, Molecular dynamics, MPI, nVidia, OpenCL, OpenMP, Thesis

March 1, 2016 by hgpu

GPU performance analysis of a nodal discontinuous Galerkin method for acoustic and elastic models

Axel Modave, Amik St-Cyr, Tim Warburton

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Tags: BLAS, Computational Physics, CUBLAS, CUDA, FEM, Finite element method, Geoscience, Linear Algebra, nVidia, nVidia GeForce GTX 980, OCCA, Physics, Profiling, Seismic modeling, Seismology

March 1, 2016 by hgpu

GPU Robot Motion Planning using Semi-Infinite Nonlinear Programming

Benjamin Chretien, Adrien Escande, Abderrahmane Kheddar

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Tags: Computer science, CUDA, Nonlinear optimization, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX Titan Black, Robotics, Tesla C2070

February 25, 2016 by hgpu

Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

G.R. Mudalige, I.Z. Reguly, M.B. Giles

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Tags: cfd, Code generation, Computer science, CUDA, Fluid dynamics, Fortran, nVidia, Programming techniques, Tesla K80

February 25, 2016 by hgpu

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Steven Bohez, Sam Leroux, Pieter Simoens

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Java, Neural networks, nVidia, nVidia GeForce GTX 980, nVidia Jetson TK1, Package

February 25, 2016 by hgpu

ANTS2 package: simulation and experimental data processing for Anger camera type detectors

A. Morozov, V. Solovov, R. Martins, F. Neves, V. Domingos, V. Chepel

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Tags: CUDA, Instrumentation and Detectors, Neural networks, nVidia, nVidia GeForce GTX 770, Package, Physics, Visualization

February 25, 2016 by hgpu

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Hector Ortega Arranz

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, Path problems, Thesis

February 25, 2016 by hgpu

Deep Learning At Scale and At Ease

Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 970, Package

February 23, 2016 by hgpu

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Heuristics for the Variable Sized Bin Packing Problem Using a Hybrid P-System and CUDA Architecture

DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

Alpaka – An Abstraction Library for Parallel Kernel Acceleration

Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design

Optimization of Molecular Dynamics Simulation Code and Applications to Biomolecular Systems

GPU performance analysis of a nodal discontinuous Galerkin method for acoustic and elastic models

GPU Robot Motion Planning using Semi-Infinite Nonlinear Programming

Auto-Vectorizing a Large-scale Production Unstructured-mesh CFD Application

DIANNE: Distributed Artificial Neural Networks for the Internet of Things

ANTS2 package: simulation and experimental data processing for Anger camera type detectors

Parallel Approaches to Shortest-Path Problems for Multilevel Heterogeneous Computing

Deep Learning At Scale and At Ease

Recent source codes

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

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

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