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Hydra: a C++11 framework for data analysis in massively parallel platforms

A. A. Alves Junior, M. D. Sokoloff

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Tags: Computational Physics, Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, Tesla K40

November 16, 2017 by hgpu

Best Practice Guide – GPGPU

Momme Allalen, Vali Codreanu, Nevena Ilieva-Litova, Alan Gray, Anders Sjostrom, Volker Weinberg

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Tags: Computer science, CUDA, OpenACC, OpenCL, OpenMP, Review

November 12, 2017 by hgpu

Performance Evaluation of Deep Learning Tools in Docker Containers

Pengfei Xu, Shaohuai Shi, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Performance

November 12, 2017 by hgpu

Scalable Streaming Tools for Analyzing N-body Simulations: Finding Halos and Investigating Excursion Sets in One Pass

Nikita Ivkin, Zaoxing Liu, Lin F. Yang, Srinivas Suresh Kumar, Gerard Lemson, Mark Neyrinck, Alexander S. Szalay, Vladimir Braverman, Tamas Budavari

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Tags: Algorithms, Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce GTX 1080, Tesla C2050, Tesla C2070

November 7, 2017 by hgpu

Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs

G. D. Balogh, I. Z. Reguly, G. R. Mudalige

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, OpenACC, OpenMP, Performance, Tesla K40, Tesla P100

November 7, 2017 by hgpu

Lattice QCD on new chips: a community summary

Antonio Rago

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Tags: CUDA, HEP, High Energy Physics - Lattice, Intel Xeon Phi, MPI, nVidia, OpenMP, Physics, QCD, Review, Tesla V100

November 7, 2017 by hgpu

Acceleration of tensor-product operations for high-order finite element methods

Kasia Swirydowicz, Noel Chalmers, Ali Karakus, Timothy Warburton

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Tags: Computer science, CUDA, FEM, Finite element method, Mathematical Software, Numerical Analysis, nVidia, Overview, Performance, Tesla P100

November 7, 2017 by hgpu

Dynamic Load Balancing Strategies for Graph Applications on GPUs

Ananya Raval, Rupesh Nasre, Vivek Kumar, Vasudevan R, Sathish Vadhiyar, Keshav Pingali

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Tags: Computer science, CUDA, load balancing, nVidia, Tesla K20

November 5, 2017 by hgpu

A Dynamic Hash Table for the GPU

Saman Ashkiani, Martin Farach-Colton, John D. Owens

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Tags: Computer science, CUDA, Graph theory, Hashing, nVidia, Tesla K40

November 5, 2017 by hgpu

ChainerMN: Scalable Distributed Deep Learning Framework

Takuya Akiba, Keisuke Fukuda, Shuji Suzuki

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

November 5, 2017 by hgpu

Automatic Scan Parallelization in OpenMP

Maicol Zegarra, Marcio Pereira, Xavier Martorell, Guido Araujo

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Tags: Algorithms, ARM, Computer science, CUDA, nVidia, OpenCL, OpenMP, Sorting, Tesla K40

October 31, 2017 by hgpu

An efficient GPU algorithm for tetrahedron-based Brillouin-zone integration

Daniel Guterding, Harald O. Jeschke

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 970, Physics

October 31, 2017 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

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high performance computing on graphics processing units: hgpu.org

Hydra: a C++11 framework for data analysis in massively parallel platforms

Best Practice Guide – GPGPU

Performance Evaluation of Deep Learning Tools in Docker Containers

Scalable Streaming Tools for Analyzing N-body Simulations: Finding Halos and Investigating Excursion Sets in One Pass

Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs

Lattice QCD on new chips: a community summary

Acceleration of tensor-product operations for high-order finite element methods

Dynamic Load Balancing Strategies for Graph Applications on GPUs

A Dynamic Hash Table for the GPU

ChainerMN: Scalable Distributed Deep Learning Framework

Automatic Scan Parallelization in OpenMP

An efficient GPU algorithm for tetrahedron-based Brillouin-zone integration

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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