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Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

Dan Lin

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Tags: Algorithms, Computer science, Data parallelism, LLVM, nVidia, nVidia GeForce GTX 950, Package, PTX, Thesis

April 28, 2018 by hgpu

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Zhe Jia, Marco Maggioni, Benjamin Staiger, Daniele P. Scarpazza

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Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, PTX, Tesla V100

April 22, 2018 by hgpu

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

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Tags: Compilers, Computer science, DSL, FPGA, Matrix multiplication, nVidia, OpenMPI, Performance, Programming Languages, PTX, Tesla K40

March 3, 2018 by hgpu

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Philippe Tillet, David Cox

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980 Ti, OpenCL, Package, Performance, PTX, Tesla P100

February 17, 2018 by hgpu

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

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Tags: Computer science, Deep learning, FPGA, Neural networks, nVidia, Package, PTX

February 3, 2018 by hgpu

Comprehensive Optimization of Parametric Kernels for Graphics Processing Units

Xiaohui Chen, Marc Moreno-Maza, Jeeva Paudel, Ning Xie

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Tags: Code generation, Computer science, CUDA, LLVM, nVidia, Package, Performance, PTX, Symbolic Computation, Tesla M2050

January 20, 2018 by hgpu

Implementing implicit OpenMP data sharing on GPUs

Gheorghe-Teodor Bercea, Carlo Bertolli, Arpith C. Jacob, Alexandre Eichenberger, Alexey Bataev, Georgios Rokos, Hyojin Sung, Tong Chen, Kevin O'Brien

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Tags: Computer science, CUDA, LLVM, Memory model, nVidia, OpenMP, Programming Languages, PTX, Tesla K40, Tesla P100

November 30, 2017 by hgpu

Efficient Target and Application Specific Selection and Ordering of Compiler Passes

Ricardo Jorge Ferreira Nobre

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Tags: Code generation, Compilers, Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX 460, nVidia GeForce GTX 480, OpenCL, PTX, Thesis

November 26, 2017 by hgpu

Autotuning GPU Kernels via Static and Predictive Analysis

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Code generation, Computer science, CUDA, nVidia, Performance, PTX, Tesla K20, Tesla M2050, Tesla M40

February 2, 2017 by hgpu

GPU Performance Modeling and Optimization

Ang Li

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Tags: Computer science, CUDA, Hardware Architecture, nVidia, Performance, PTX, Thesis

October 29, 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

Automatic Loop Partitioning for Heterogeneous Systems

Alexander Baier

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan Black, Performance, PTX, Thesis

August 1, 2016 by hgpu

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OCLADock - OpenCL Accelerated Molecular Docking

A Case Study in Using OpenCL on FPGAs: Creating an Open-Source Accelerator of the AutoDock Molecular Docking Software

sentiment-discovery: Unsupervised Language Modeling at scale for robust sentiment classification

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Parallax: A Tool for Automatic Parallelization of Deep Learning Training in Distributed Multi-GPU Environments

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image processing and Reconstruction on heterogeneous Devices

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices

Spatial: Specify Parameterized Accelerators Through Inordinately Abstract Language

Spatial: A Language and Compiler for Application Accelerators

cuPentBatch: A batched pentadiagonal solver for NVIDIA GPUs

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

CloudCL: Single-Paradigm Distributed Heterogeneous Computing for Cloud Infrastructures

CloudCL: Single-Paradigm Distributed Heterogeneous Computing for Cloud Infrastructures

Calamari: OCR Engine based on OCRopy and Kraken

Calamari – A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition

FluidFFT: efficient and easy Fast Fourier Transform for Python

FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

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2018

August

20-21

Guangzhou, China

The 3rd International Conference on Computer Systems and Communication Technology (ICCSCT), 2018

2018

November

02-04

Academic Exchange Center in Ocean University of China, Qingdao, China

The 4th International Conference on Communication and Information Processing (ICCIP), 2018

2018

November

27-30

Hida Takayama, Japan

The Third International Workshop on GPU Computing and AI (GCA), 2018

2018

September

19-21

Nagoya University, Japan

The 5th International Conference on Power and Energy Systems Engineering (CPESE), 2018

2018

September

22-24

MediaCityUK, Salford Quays, Greater Manchester, England

The 10th International Conference on Information Management and Engineering (ICIME), 2018

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