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LLM-shark -- A Tool for Automatic Resource-boundness Analysis and Cache Partitioning Setup

Fulltext:


Authors:

Jakob Danielsson, Tiberiu Seceleanu, Marcus Jägemar, Moris Behnam, Mikael Sjödin

Research group:

Model-Based Engineering of Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

Intelligent and Resilient Computing for a Collaborative World

Abstract

We present LLM-shark, a tool for automatic hardware resource-boundness detection and cache-partitioning. Our tool has three primary objectives: First, it determines the hardware resource-boundness of a given application. Secondly, it estimates the initial cache partition size to ensure that the application performance is conserved and not affected by other processes competing for cache utilization. Thirdly, it continuously monitors that the application performance is maintained over time and, if necessary, change the cache partition size. We demonstrate LLM-shark's functionality through a series of tests using six different applications, including a set of feature detection algorithms and two synthetic applications. Our tests reveal that it is possible to determine an application's resource-boundness using a Pearson-correlation scheme implemented in LLM-shark. We propose a scheme to size cache partitions based on the correlation coefficient applications depending on their resource boundness.

Bibtex

@inproceedings{Danielsson6288,
author = {Jakob Danielsson and Tiberiu Seceleanu and Marcus J{\"a}gemar and Moris Behnam and Mikael Sj{\"o}din},
title = {LLM-shark -- A Tool for Automatic Resource-boundness Analysis and Cache Partitioning Setup},
month = {September},
year = {2021},
booktitle = {Intelligent and Resilient Computing for a Collaborative World},
url = {http://www.es.mdu.se/publications/6288-}
}