high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce RTX 2080 Ti

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

View

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

Data-Parallel Flattening by Expansion

Martin Elsman, Troels Henriksen, Niels Gustav Westphal Serup

View

Tags: Code generation, Computer science, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package

June 23, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

View

Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

Karan Aggarwal, Uday Bondhugula, Varsha Sreenivasan, Devarajan Sridharan

View

Tags: Algorithms, Computer science, CUDA, Neuroscience, nVidia, nVidia GeForce RTX 2080 Ti, Sparse matrix

May 19, 2019 by hgpu

A Study on the Acceleration of Arrival Curve Construction and Regular Specification Mining using GPUs

Nirmal Joshi Benann Rajendra

View

Tags: AMD Radeon R9 390X, ATI, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080 Ti, OpenCL, Thesis

December 30, 2018 by hgpu

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

KTT: Kernel Tuning Toolkit

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

DBCSR: Distributed Block Compressed Sparse Row matrix library

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

See all packages

* * *

* * *

* * *

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

+

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: