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Study of OpenCL Processing Models for FPGA Devices

Piotr Szkotak, Pawel Russek, Kazimierz Wiatr

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Tags: Computer science, FPGA, OpenCL, Package, Security

November 10, 2019 by hgpu

CL-VIS: Visualization Platform for Understanding and Checking the OpenCL Programs

SeongKi Kim, HyukSoo Han

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Tags: Computer science, nVidia, nVidia GeForce GTX 960 M, OpenCL, Visualization

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

Accelerating Stochastic Simulations on GPUs Using OpenCL

Pilsung Kang

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Tags: Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Physics, Stochastic simulation

November 10, 2019 by hgpu

Code Optimization on GPUs

Changwan Hong

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Tags: Chemistry, Code generation, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla P100, Thesis

November 3, 2019 by hgpu

In-memory database acceleration on FPGAs: a survey

Jian Fang, Yvo T. B. Mulder, Jan Hidders, Jinho Lee, H. Peter Hofstee

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Tags: Computer science, Databases, FPGA, HLS, OpenCL, survey

November 3, 2019 by hgpu

Implementing and evaluating an heterogeneous, scalable, tridiagonal linear system solver with OpenCL to target FPGAs, GPUs, and CPUs

Hamish J. Macintosh, Jasmine E. Banks, Neil A. Kelson

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Tags: Computer science, FPGA, Linear Algebra, nVidia, nVidia Quadro M 4000, OpenCL

November 3, 2019 by hgpu

Research on OpenCL optimization for FPGA deep learning application

Shuo Zhang, Yanxia Wu, Chaoguang Men, Hongtao He, Kai Liang

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Tags: Computer science, Deep learning, FPGA, OpenCL, Package

November 3, 2019 by hgpu

Performance Evaluation of Advanced Features in CUDA Unified Memory

Steven W. D. Chien, Ivy B. Peng, Stefano Markidis

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 1050, Package, Prefetch, Tesla V100

October 27, 2019 by hgpu

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

Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, Package, Sequence alignment

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

AI Benchmark: All About Deep Learning on Smartphones in 2019

Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool

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Tags: Android, Benchmarking, Computer science, CUDA, Deep learning, nVidia, OpenCL, Overview

October 20, 2019 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Programming

Study of OpenCL Processing Models for FPGA Devices

CL-VIS: Visualization Platform for Understanding and Checking the OpenCL Programs

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

Accelerating Stochastic Simulations on GPUs Using OpenCL

Code Optimization on GPUs

In-memory database acceleration on FPGAs: a survey

Implementing and evaluating an heterogeneous, scalable, tridiagonal linear system solver with OpenCL to target FPGAs, GPUs, and CPUs

Research on OpenCL optimization for FPGA deep learning application

Performance Evaluation of Advanced Features in CUDA Unified Memory

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

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

AI Benchmark: All About Deep Learning on Smartphones in 2019

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