Time-predictable GPU execution
Časově deterministické vykonávání kódu na GPU
Authors
Supervisors
Reviewers
Editors
Other contributors
Journal Title
Journal ISSN
Volume Title
Publisher
České vysoké učení technické v Praze
Czech Technical University in Prague
Czech Technical University in Prague
Date of defense
Abstract
In this thesis, we evaluate the interference between multiple GPU (Graphics processing unit) kernels running in parallel based on artificial random and sequential walks, the 2D Convolution benchmark provided by polybench and the KCF (Kernelized Correlation Filter) tracker implemented by Vít Karafiát and Michal Sojka. To achieve a reduction of the interference between the running kernels and to reduce the resulting execution jitter, we used a time-triggered execution on the GPU. To enable the synchronization, we assessed two synchronization mechanisms available on Tegra X2 platform: one based on zero-copy memory and one based on the globaltimer. We found that the NVIDIA profiler (nvprof) reconfigures the resolution of globaltimer from 1 s to 160 ns. With this resolution, we were able to reduce the execution time jitter of a tiled 2D convolution kernel from 6.47% to 0.15% while maintaining the same average execution time by use of a time-triggered GPU execution.
In this thesis, we evaluate the interference between multiple GPU (Graphics processing unit) kernels running in parallel based on artificial random and sequential walks, the 2D Convolution benchmark provided by polybench and the KCF (Kernelized Correlation Filter) tracker implemented by Vít Karafiát and Michal Sojka. To achieve a reduction of the interference between the running kernels and to reduce the resulting execution jitter, we used a time-triggered execution on the GPU. To enable the synchronization, we assessed two synchronization mechanisms available on Tegra X2 platform: one based on zero-copy memory and one based on the globaltimer. We found that the NVIDIA profiler (nvprof) reconfigures the resolution of globaltimer from 1 s to 160 ns. With this resolution, we were able to reduce the execution time jitter of a tiled 2D convolution kernel from 6.47% to 0.15% while maintaining the same average execution time by use of a time-triggered GPU execution.
In this thesis, we evaluate the interference between multiple GPU (Graphics processing unit) kernels running in parallel based on artificial random and sequential walks, the 2D Convolution benchmark provided by polybench and the KCF (Kernelized Correlation Filter) tracker implemented by Vít Karafiát and Michal Sojka. To achieve a reduction of the interference between the running kernels and to reduce the resulting execution jitter, we used a time-triggered execution on the GPU. To enable the synchronization, we assessed two synchronization mechanisms available on Tegra X2 platform: one based on zero-copy memory and one based on the globaltimer. We found that the NVIDIA profiler (nvprof) reconfigures the resolution of globaltimer from 1 s to 160 ns. With this resolution, we were able to reduce the execution time jitter of a tiled 2D convolution kernel from 6.47% to 0.15% while maintaining the same average execution time by use of a time-triggered GPU execution.
Description
Keywords
Citation
Underlying research data set URL
Permanent link
Rights/License
A university thesis is a work protected by the Copyright Act of the Czech Republic. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one`s own expense. The use of thesis should be in compliance with the Copyright Act.
Vysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem v platném znění.
Vysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem v platném znění.