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Compiler-Driven Performance on Heterogeneous Computing Platforms

Artem Chikin

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, Performance, Tesla P100, Tesla V100, Thesis

November 17, 2019 by hgpu

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

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Alexander Brandt, Davood Mohajerani, Marc Moreno Maza, Jeeva Paudel, Linxiao Wang

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Tags: Computer science, CUDA, LLVM, nVidia, Package, Performance

November 10, 2019 by hgpu

Performance Debugging Frameworks for FPGA High-Level Synthesis

Young-kyu Choi

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Tags: Benchmarking, Computer science, Design space exploration, FPGA, HLS, Performance, Thesis

October 27, 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

Performance Evaluation of Blocking and NonBlocking Concurrent Queues on GPUs

Hossein Pourmeidani

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Tags: AMD Radeon R7, ATI, Benchmarking, Computer science, OpenCL, Performance, Thesis

October 13, 2019 by hgpu

Performance Impact of Memory Channels on Sparse and Irregular Algorithms

Oded Green, James Fox, Jeffrey Young, Jun Shirako, David Bader

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Performance, Sparse, Tesla P100, Tesla V100

October 13, 2019 by hgpu

Code optimization based on source to source transformations using profile guided metrics

Youenn Lebras

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Tags: Code generation, Computer science, Performance, Thesis

September 15, 2019 by hgpu

Visual Performance Analysis of Memory Behavior in a Task-Based Runtime on Hybrid Platforms

Lucas Leandro Nesi, Samuel Thibault, Luka Stanisic, Lucas Mello Schnorr

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, Performance, StarPU

September 1, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

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Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

Performance Comparison for Neuroscience Application Benchmarks

Andreas Herten, Thorsten Hater, Wouter Klijn, Dirk Pleiter

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Tags: Benchmarking, Computer science, CUDA, Neuroscience, nVidia, Performance, Tesla P100, Tesla V100

August 11, 2019 by hgpu

Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Yu (Emma)Wang, Gu-Yeon Wei, David Brooks

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Tags: Benchmarking, Cloud, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100, TPU

July 28, 2019 by hgpu

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

SHA512AOCLStudy

Study of OpenCL Processing Models for FPGA Devices

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

JSDoop

JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training

FPGA CNN Acceleration

Research on OpenCL optimization for FPGA deep learning application

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Performance Evaluation of Advanced Features in CUDA Unified Memory

Performance Evaluation of Advanced Features in CUDA Unified Memory

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

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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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