high performance computing on graphics processing units: hgpu.org

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NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen

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Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Python

June 2, 2018 by hgpu

Learning to Optimize Tensor Programs

Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: ARM, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X

May 26, 2018 by hgpu

G-NET: Effective GPU Sharing in NFV Systems

Kai Zhang, Bingsheng He, Jiayu Hu, Zeke Wang, Bei Hua, Jiayi Meng, Lishan Yang

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

April 15, 2018 by hgpu

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li, Wei Tan

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Tags: Algorithms, Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Package, Tesla V100

March 17, 2018 by hgpu

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

Alfonso Rodriguez, Cesar Castanares, Teresa Riesgo, Eduardo de la Torre

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Tags: ARM, Computer science, FPGA, nVidia, nVidia GeForce GTX Titan X, OpenCL

February 17, 2018 by hgpu

Interactive GPU active contours for segmenting inhomogeneous objects

Chris G. Willcocks, Philip T. G. Jackson, Carl J. Nelson, Amar V. Nasrulloh, Boguslaw Obara

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Tags: Biology, Image processing, Medicine, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

February 9, 2018 by hgpu

Fast and Flexible GPU Accelerated Binding Free Energy Calculations within the AMBER Molecular Dynamics Package

Daniel J. Mermelstein, Charles Lin, Gard Nelson, Rachael Kretsch, J. Andrew McCammon, Ross C. Walker

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X

January 20, 2018 by hgpu

Deep In-GPU Experience Replay

Ben Parr

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Tags: Artificial intelligence, Computer science, nVidia, nVidia GeForce GTX Titan X

January 13, 2018 by hgpu

A Generic Inverted Index Framework for Similarity Search on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Lubos Krcal, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Hashing, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, OpenMPI, Package

December 28, 2017 by hgpu

Deep learning for galaxy surface brightness profile fitting

D. Tuccillo, M. Huertas-Company, E. Decenciere, S. Velasco-Forero, H. Dominguez Sanchez, P. Dimauro

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Tags: Astrophysics, Deep learning, Keras, nVidia, nVidia GeForce GTX Titan X

November 16, 2017 by hgpu

Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation

Wim Vanderbauwhede, Gavin Davidson

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Tags: Compilers, Computer science, Fortran, Heterogeneous systems, Mathematical Software, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenACC, OpenCL, OpenMP, Package

November 16, 2017 by hgpu

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

Leiming Yu, Fanny Nina-Paravecino, David Kaeli, Qianqian Fang

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Tags: Algorithms, AMD R9 Nano, AMD Radeon RX 480, ATI, Computational Physics, Heterogeneous systems, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 590, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan X, OpenCL, Package, Physics

November 12, 2017 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

NengoDL: Combining deep learning and neuromorphic modelling methods

Learning to Optimize Tensor Programs

G-NET: Effective GPU Sharing in NFV Systems

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

Interactive GPU active contours for segmenting inhomogeneous objects

Fast and Flexible GPU Accelerated Binding Free Energy Calculations within the AMBER Molecular Dynamics Package

Deep In-GPU Experience Replay

A Generic Inverted Index Framework for Similarity Search on the GPU

Deep learning for galaxy surface brightness profile fitting

Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

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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