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Accelerating Spark RDD Operations with Local and Remote GPU Devices

Yasuhiro Ohno, Shin Morishima, Hiroki Matsutani

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 980 Ti, Spark

December 20, 2016 by hgpu

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Daniel Winkler, Michael Meister, Massoud Rezavand, Wolfgang Rauch

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Physics

December 20, 2016 by hgpu

GPU-Based Nonlocal Filtering for Large Scale SAR Processing

Gerald Baier, Xiao Xiang Zhu

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Tags: Filtering, Image processing, nVidia, OpenCL, SAR, Signal denoising, Signal processing, Tesla K40

December 17, 2016 by hgpu

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Farhad Merchant, Tarun Vatwani, Anupam Chattopadhyay, Soumyendu Raha, S K Nandy, Ranjani Narayan

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Tags: Algorithms, Computer science, CUDA, Factorization, FPGA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Performance, Tesla C2050

December 17, 2016 by hgpu

Toward Automatic Translation: From OpenACC to OpenMP 4

Nawrin Sultana

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Tags: Algorithms, Code generation, Compilers, Computer science, nVidia, OpenACC, OpenMP, Tesla K40, Thesis

December 17, 2016 by hgpu

Speedup for quantum optimal control from GPU-based automatic differentiation

Nelson Leung, Mohamed Abdelhafez, Jens Koch, David I. Schuster

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Tags: Algorithms, CUDA, Deep learning, Mesoscale and Nanoscale Physics, Numerical simulation, nVidia, Package, Physics, Python, Quantum Physics, TensorFlow, Tesla K40

December 17, 2016 by hgpu

Parallel Level set algorithm with MPI and accelerated on GPU

Zhenlin Wang

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Tags: Computational Physics, CUDA, Finite difference, MPI, nVidia, Package, PDEs, Physics

December 17, 2016 by hgpu

Automating the Last-Mile for High Performance Dense Linear Algebra

Richard Michael Veras, Tze Meng Low, Tyler Michael Smith, Robert van de Geijn, Franz Franchetti

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Tags: Code generation, Computer science, Intel Xeon Phi, Linear Algebra, Performance

December 14, 2016 by hgpu

Translating OpenMP Device Constructs to OpenCL using Unnecessary Data Transfer Elimination

Junghyun Kim, Yong-Jun Lee, Jungho Park, Jaejin Lee

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Tags: AMD Radeon R9 390X, ATI, Benchmarking, Code generation, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenACC, OpenCL, OpenMP

December 14, 2016 by hgpu

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

Ning Xie

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Tags: Code generation, Computer science, CUDA, nVidia, OpenMP, Tesla M2050, Thesis

December 14, 2016 by hgpu

nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

Sven Koitka, Christoph M. Friedrich

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Tags: Benchmarking, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package, Tesla K80

December 14, 2016 by hgpu

GaDei: On Scale-up Training As A Service For Deep Learning

Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bowen Zhou

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Tags: Computer science, CUDA, Deep learning, nVidia, Tesla K20, Tesla K80

December 14, 2016 by hgpu

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

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high performance computing on graphics processing units: hgpu.org

Applications

Accelerating Spark RDD Operations with Local and Remote GPU Devices

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

GPU-Based Nonlocal Filtering for Large Scale SAR Processing

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Toward Automatic Translation: From OpenACC to OpenMP 4

Speedup for quantum optimal control from GPU-based automatic differentiation

Parallel Level set algorithm with MPI and accelerated on GPU

Automating the Last-Mile for High Performance Dense Linear Algebra

Translating OpenMP Device Constructs to OpenCL using Unnecessary Data Transfer Elimination

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

GaDei: On Scale-up Training As A Service For Deep Learning

Recent source codes

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

HPX: a C++ Standard Library for Concurrency and Parallelism

TorchQC: Quantum Dynamics and Machine Learning

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

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