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PDAWL: Profile-based Iterative Dynamic Adaptive WorkLoad Balance on Heterogeneous Architectures

Tongsheng Geng, Marcos Amaris, Stephane Zuckerman, Alfredo Goldman, Guang R. Gao, Jean-Luc Gaudiot

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX Titan, Performance, Sparse matrix, Task scheduling, Tesla K20, Tesla K40

May 24, 2020 by hgpu

Leveraging Data-Flow Information for Efficient Scheduling of Task-Parallel Programs on Heterogeneous Systems

Osman Seckin Simsek

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V, OpenCL, performance portability, Tesla K20, Thesis

May 3, 2020 by hgpu

Data Movement Optimization for High-Performance Computing

Tobias Gysi

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Tags: Computer science, CUDA, HPC, nVidia, Tesla K20, Thesis

March 15, 2020 by hgpu

Portable and Performant GPU/Heterogeneous Asynchronous Many-Task Runtime System

Bradley Peterson

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, MPI, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan X, OpenCL, SYCL, Tesla K20, Thesis

March 8, 2020 by hgpu

GPU-accelerated dynamic programming for join-order optimization

Andreas Meister, Gunter Saake

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Tags: Computer science, Databases, nVidia, OpenCL, Tesla K20

February 2, 2020 by hgpu

Static Analysis and Dynamic Adaptation of Parallelism

Pierre Huchant

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla K20, Tesla M2075, Thesis

January 12, 2020 by hgpu

Array Languages Make Neural Networks Fast

Artjoms Šinkarovs, Hans-Nikolai Vießmann, Sven-Bodo Scholz

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package, Python, PyTorch, TensorFlow, Tesla K20

December 15, 2019 by hgpu

GPU Computing with Python: Performance, Energy Efficiency and Usability

Håvard H. Holm, André R. Brodtkorb, Martin L. Sætra

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 840 M, OpenCL, Performance, Python, Tesla K20, Tesla M2090, Tesla P100, Tesla V100

December 8, 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

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

Hamid Reza Zohouri, Satoshi Matsuoka

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Tags: Benchmarking, Computer science, FPGA, HLS, Memory, nVidia, OpenCL, Package, Tesla K20, Tesla V100

October 20, 2019 by hgpu

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Beau Johnston

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Tags: Algorithms, AMD Radeon RX 480, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K40, Thesis

September 15, 2019 by hgpu

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Nico Reissmann

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Tags: Computer science, CUDA, nVidia, OpenMP, Package, Performance, performance portability, Tesla K20, Thesis

May 5, 2019 by hgpu

Ginkgo: a high-performance linear algebra library for manycore systems

Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

GROMACS with CUDA-aware MPI direct GPU communication support

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Rodinia_SYCL: The Rodinia Benchmark Suite in SYCL

The Rodinia Benchmark Suite in SYCL

Neural Architecture Search Without Training

Neural Architecture Search without Training

OpenABLext: An extension to OpenABL, a framework to generate high- performance code for agent-based simulations

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

SOFF: SNU OpenCL Framework for FPGAs

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

GCOOSpDM: Sparse-dense matrix-matrix multiplication on GPUs

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Programming Your GPU with OpenMP: A Hands-On Introduction

Evaluating the performance of HPC-style SYCL applications

sycl-bench: SYCL Benchmark Suite

SYCL-Bench: A Versatile Single-Source Benchmark Suite for Heterogeneous Computing

BlaBla: A Python package for clinical linguistic feature extraction in multiple languages

BlaBla: Linguistic Feature Extraction for Clinical Analysis in Multiple Languages

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2020

November

24-27

Naha, Okinawa, Japan

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2021

January

13-15

Shenzhen, China

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

2020

December

14-16

Bangkok, Thailand

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020

2020

December

14-16

Bangkok, Thailand

International Conference on Wireless Networks and Embedded Systems (ICWNES’20), 2020

2020

December

14-16

Bangkok, Thailand

International Joint Conference on Signals, Systems and Computers (CSSC’20), 2020

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