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DeepPy: Pythonic deep learning

Anders Boesen Lindbo Larsen

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python

September 3, 2016 by hgpu

Deep Learning on FPGAs

Griffin James Lacey

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Tags: Computer science, Deep learning, FPGA, nVidia, nVidia Quadro K600, OpenCL, Thesis

August 31, 2016 by hgpu

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package

August 31, 2016 by hgpu

Optimization of RAID Erasure Coding Algorithms for Intel Xeon Phi

Aleksei Marov, Andrey Fedorov

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Tags: Algorithms, Computer science, Error recovery, Intel Xeon Phi

August 31, 2016 by hgpu

Flux tubes at Finite Temperature

Pedro Bicudo, Nuno Cardoso, Marco Cardoso

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Tags: CUDA, High Energy Physics - Lattice, nVidia, Phase transition, Physics, QCD

August 31, 2016 by hgpu

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 2016 by hgpu

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Suyang Zhu, Sunita Chandrasekaran, Peng Sun, Barbara Chapman, Marcus Winter, Tobias Schuele

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson TK1

August 28, 2016 by hgpu

DawnCC: a Source-to-Source Automatic Parallelizer of C and C++ Programs

Breno Campos Ferreira Guimaraes, Gleison Souza Diniz Mendonca, Fernando Magno Quintao Pereira

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Tags: Code generation, Compilers, Computer science, OpenACC, OpenMP

August 28, 2016 by hgpu

Benchmarking State-of-the-Art Deep Learning Software Tools

Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu

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Tags: Benchmarking, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, RNN, Tesla K80

August 28, 2016 by hgpu

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Christopher P. Stone, Kyle E. Niemeyer

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Tags: Computational Physics, Computer science, Differential equations, Intel Xeon Phi, nVidia, ODEs, OpenCL, Ordinary differential equations, Physics, Tesla K20

August 28, 2016 by hgpu

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Dennis Bazow, Ulrich W. Heinz, Michael Strickland

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, High Energy Physics - Phenomenology, Intel Xeon Phi, Nuclear Theory, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan Z, Physics

August 28, 2016 by hgpu

Fast Multidimensional Image Processing with OpenCL

Daniel Oliveira Dantas, Helton Danilo Passos Leal, Davy Oliveira Barros Sousa

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Tags: AMD Radeon R9 270X, ATI, CUDA, GLSL, Image processing, OpenCL, Package

August 23, 2016 by hgpu

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

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

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high performance computing on graphics processing units: hgpu.org

Applications

DeepPy: Pythonic deep learning

Deep Learning on FPGAs

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Optimization of RAID Erasure Coding Algorithms for Intel Xeon Phi

Flux tubes at Finite Temperature

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

DawnCC: a Source-to-Source Automatic Parallelizer of C and C++ Programs

Benchmarking State-of-the-Art Deep Learning Software Tools

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Fast Multidimensional Image Processing with OpenCL

Recent source codes

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

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

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