Introduction

Motivation

With the transition from homogeneous to heterogeneous multi-core platforms, there is an increasing demand for software mapping that minimize the latency of a task chain to facilitate high computing performance.

While achieving the minimum task response time is a key to solving the challenge, satisfying the optimized response time for every task is hard to realize since the limited number of processing units and task deadlines are concerned. Therefore, it is reasonable to analyze mapping models based on the sum of all tasks’ response time and extract the model with the minimum sum value. Finding the optimized solution is taken care of by a meta-heuristic algorithm such as GA or SA. This leads to the need for establishing an application that has a built-in response-time calculation algorithm and works with the meta-heuristic which would make the entire developing circle quicker.

Furthermore, the response time results obtained through the meta-heuristic are used to measure E2E task chain latency to help determine how long it would take to execute a certain set of software instructions. E2E latency measurement depends on the measurement target, the communication model and the simulation scenario. The ultimate goal of this project is to devise E2E latency measurement methodologies in an analytical way, and analyze the results of the methodologies to determine how a specific set of constraints configurations affects E2E latency.

AMALTHEA

AMALTHEA is a project as well as its results with a purpose to provide AUTOSAR compatible environment for efficient multi-core system development which is publicly available open-source. The project results cover a comprehensive system model, trace model, and framework which provides support tools when needed. The [11] WATERS Industrial Challenge (-, 2019) model used in this project is also created with the AMALTHEA framework and can be utilized and manipulated on [3] APP4MC (-, 2020) version 0.9.7 platform.