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DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

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Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

High Performance Computing via High Level Synthesis

Mehdi Roozmeh

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Tags: Computer science, Design space exploration, FPGA, Heterogeneous systems, HLS, Nearest neighbour, nVidia, OpenCL, Package, Signal processing, Thesis

August 18, 2019 by hgpu

Combining Multiple Optimised FPGA-based Pulsar Search Modules Using OpenCL

Haomiao Wang, Prabu Thiagaraj, Oliver Sinnen

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Tags: Astrophysics, FPGA, OpenCL, Signal processing

June 17, 2018 by hgpu

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Xinying Wang

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Tags: Computer science, Data mining, Factorization, FPGA, Intel Xeon Phi, Matrix decomposition, Semantic indexing, Signal processing, Text mining, Thesis

December 28, 2017 by hgpu

A tutorial on the implementations of linear image filters in CPU and GPU

Alvaro Pardo

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 430, Signal processing, Tutorial

December 7, 2017 by hgpu

Practically efficient methods for performing bit-reversed permutation in C++11 on the x86-64 architecture

Christian Knauth, Boran Adas, Daniel Whitfield, Xuesong Wang, Lydia Ickler, Tim Conrad, Oliver Serang

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Tags: Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, Signal processing

August 8, 2017 by hgpu

A GPU Based Memory Optimized Parallel Method For FFT Implementation

Fan Zhang, Chen Hu, Qiang Yin, Wei Hu

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Tags: CUDA, FFT, Image processing, nVidia, Sensing, Signal processing, Tesla C2070

August 1, 2017 by hgpu

Synthesis of Embedded Software using Dataflow Schedule Graphs

Abhay Raina

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Tags: Computer science, DSP, nVidia, nVidia GeForce GTX 680, OpenCL, Signal processing, Thesis

July 2, 2017 by hgpu

On the Use of a GPU-Accelerated Mobile Device Processor for Sound Source Localization

Jose A. Belloch, Jose M. Badia, Francisco D. Igual, Maximo Cobos, Enrique S. Quintana-Orti

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Tags: Algorithms, ARM, nVidia, nVidia GeForce GTX 1080, OpenCL, Signal processing, Tesla K20

June 21, 2017 by hgpu

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim

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Tags: Computer science, CUDA, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Package, Python, Signal processing, Tesla K80, Tesla M60

June 21, 2017 by hgpu

Wireless Interference Identification with Convolutional Neural Networks

Malte Schmidt, Dimitri Block, Uwe Meier

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Tags: CNN, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 960, Signal processing

March 5, 2017 by hgpu

Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

Danny C. Price, Jonathon Kocz, Matthew Bailes, Lincoln J. Greenhill

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Tags: Astrophysics, CUDA, DSP, FPGA, nVidia, Overview, Signal processing

February 7, 2017 by hgpu

DBCSR: Distributed Block Compressed Sparse Row matrix library

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

FPGAMemBench: Memory Benchmark for OpenCL-supported Intel FPGAs

The Memory Controller Wall: Benchmarking the Intel FPGA SDK for OpenCL Memory Interface

Hierarchical Product Quantization for Approximate Nearest Neighbors Search

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

hlslib: A collection of extensions for Vivado HLS and Intel FPGA OpenCL to improve developer quality of life

hlslib: Software Engineering for Hardware Design

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

MCL kernel verification library

Verification of GPU Program Optimizations in Lean

waLBerla: A block-structured high-performance framework for multiphysics simulations

waLBerla: A block-structured high-performance framework for multiphysics simulations

MIOpen: AMD's Machine Intelligence Library

MIOpen: An Open Source Library For Deep Learning Primitives

horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet

Exascale Deep Learning for Scientific Inverse Problems

Futhark Vulkan Backend

Futhark Vulkan Backend

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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