high performance computing on graphics processing units: hgpu.org

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cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Antti-Pekka Hynninen, Dmitry I. Lyakh

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Tesla K20, Tesla K40, Tesla M40, Tesla P100

May 6, 2017 by hgpu

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

Tatyana Dyakonova, Alexander Khoperskov, Sergey Khrapov

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Tags: cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, Tesla C2070, Tesla K20, Tesla K40, Tesla K80

May 6, 2017 by hgpu

GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

Zheyong Fan, Ville Vierimaa, Ari Harju

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Tags: Computational Physics, CUDA, nVidia, Package, Physics, Sparse matrix, Tesla K40

May 6, 2017 by hgpu

Speeding up a few orders of magnitude the Jacobi method: high order Chebyshev-Jacobi over GPUs

J.E. Adsuara, I. Cordero-Carrion, P. Cerda-Duran, M.A. Aloy

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Tags: CUDA, Differential equations, Finite difference, Mathematics, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, Partial differential equations, PDEs, Tesla K40

May 2, 2017 by hgpu

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Hannes Mohr

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Tags: Algorithms, ASIC, CUDA, FPGA, HEP, nVidia, Physics, Tesla K40, Tesla K80, Thesis

April 20, 2017 by hgpu

Portable, high-performance containers for HPC

Lucas Benedicic, Felipe A. Cruz, Alberto Madonna, Kean Mariotti

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Tags: Computer science, CUDA, nVidia, nVidia Quadro K 110 M, OpenCL, performance portability, Tesla K40, Tesla K80, Tesla P100, Virtualization

April 15, 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

Massive Exploration of Neural Machine Translation Architectures

Denny Britz, Anna Goldie, Thang Luong, Quoc Le

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Tags: Computer science, Deep learning, Neural networks, NLP, nVidia, Package, Python, TensorFlow, Tesla K40

March 14, 2017 by hgpu

Model-independent partial wave analysis using a massively-parallel fitting framework

Liang Sun, Rafael Aoude, Alberto Correa Dos Reis, Michael David Sokoloff

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Tags: Algorithms, CUDA, High Energy Physics - Experiment, nVidia, OpenMP, Package, Physics, Tesla K40

March 14, 2017 by hgpu

Improving the Neural GPU Architecture for Algorithm Learning

Karlis Freivalds, Renars Liepins

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, Memory model, nVidia, Package, Python, TensorFlow, Tesla K40

March 5, 2017 by hgpu

An Efficient Multiway Mergesort for GPU Architectures

Henri Casanova, John Iacono, Ben Karsin, Nodari Sitchinava, Volker Weichert

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 770, nVidia Quadro M 4000, Sorting, Tesla K40

February 28, 2017 by hgpu

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

A. A. Alves Jr, M. D. Sokoloff

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Intel Xeon Phi, Monte Carlo simulation, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan Z, nVidia Quadro K 2200, OpenMP, Package, Physics, Tesla K40

February 22, 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

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high performance computing on graphics processing units: hgpu.org

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

Speeding up a few orders of magnitude the Jacobi method: high order Chebyshev-Jacobi over GPUs

Evaluation of GPU-based track-triggering for the CMS detector at CERN’s HL-LHC

Portable, high-performance containers for HPC

Sparse Matrix-Vector Multiplication on GPGPUs

Massive Exploration of Neural Machine Translation Architectures

Model-independent partial wave analysis using a massively-parallel fitting framework

Improving the Neural GPU Architecture for Algorithm Learning

An Efficient Multiway Mergesort for GPU Architectures

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel 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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