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Framework for Parallel Kernels Auto-tuning

Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce GTX 970, OpenCL, Package, Performance, Thesis

November 10, 2019 by hgpu

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič

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Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20

October 27, 2019 by hgpu

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning

Newsha Ardalani, Urmish Thakker, Aws Albarghouthi, Karu Sankaralingam

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX 660 Ti, nVidia GeForce GTX 750, Performance

June 23, 2019 by hgpu

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Huihui Zou, Shanjiang Tang, Ce Yu, Hao Fu, Yusen Li, Wenjie Tang

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Tags: Algorithms, APU, Bioinformatics, Biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, OpenCL, Sequence alignment, Smith-Waterman algorithm

December 9, 2018 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

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

September 27, 2016 by hgpu

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

Zhe Zhou, Wenrui Diao, Xiangyu Liu, Zhou Li, Kehuan Zhang, Rui Liu

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Tags: AMD Radeon R7 250, ATI, Cloud, Computer science, nVidia, nVidia GeForce GTX 750, OpenCL, Operating systems, Security

May 26, 2016 by hgpu

Architecture-Adaptive Code Variant Tuning

Saurav Muralidharan, Amit Roy, Mary Hall, Michael Garland, Piyush Rai

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 750, nVidia GeForce GTX 770, nVidia GeForce GTX 980, Package, Performance, Tesla C2075, Tesla K20

May 21, 2016 by hgpu

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

High Performance Histograms on SIMT and SIMD Architectures

M.E.R. Berger

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Tags: AMD Radeon R9 290X, Computer science, CUDA, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 660, nVidia GeForce GTX 750, OpenCL, Performance, Thesis

December 10, 2015 by hgpu

Orchestrating Multiple Data-Parallel Kernels on Multiple Devices

Janghaeng Lee, Mehrzad Samadi, Scott Mahlke

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 760, OpenCL

November 29, 2015 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Masivo: Parallel public transport routes simulation

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

FBLAS: BLAS implementation for Intel FPGA

FBLAS: Streaming Linear Algebra Kernels on FPGA

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Titan: A CUDA-based physics simulation sandbox for high-performance robotics, physics, and reinforcement learning applications

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

onYourMarks: A deep learning benchmark implementation for Nvidia's Jetson TX2

Benchmarking Deep Learning Models on Jetson TX2

Hacking Neural Networks: A small course on exploiting and defending neural networks

Hacking Neural Networks: A Short Introduction

A Computing Kernel for Network Binarization on PyTorch

A Computing Kernel for Network Binarization on PyTorch

Deep Learning Based FPGA-CPU Acceleration

Deep Learning Based FPGA-CPU Acceleration

KTT: Kernel Tuning Toolkit

Framework for Parallel Kernels Auto-tuning

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

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

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