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Auflistung nach Schlagwort "multi-core software"

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  • Konferenzbeitrag
    Model-Driven Performance Optimization Tool Platform for Multi-core Systems with Open Sources Technologies
    (Softwaretechnik-Trends Band 39, Heft 2, 2019) Raza, Syed Aoun
    With the advent of multi-core ECUs and hardware fusion in the automotive domain, the tooling environment to support multi-core software development has gained significance. Especially, tools that can provide an early indication about the architectural behavior before the existence of the code. Such domain specific tool platforms, which enable analyses (e.g., data-consistency) and optimizations (memory management, task-to-core mapping, timing simulations and distribution) are not easily available. The commercial solutions available on the market cannot be applied generally with off the shelf optimization options. Therefore, at Bosch we have developed a tool platform that bridges the open source and commercial solutions to support analysis and optimizations of multi-core system development. This paper provides an overview of the different optimization use cases and developed tool platform.
  • Konferenzbeitrag
    Optimization of Automotive Software Distribution on Multi-core Systems using Machine Learning Approaches
    (Softwaretechnik-Trends Band 40, Heft 2, 2020) Raza, Syed Aoun; Vallavanthara, Amal Jose; Nidavan, Rakesh
    Multi-core software should be partitioned under different constraints e.g., balanced execution load on cores, timing behavior and optimized level of communication/ synchronization among different system components. The objective is to efficiently distribute the processes onto multi-core hardware such that the system has reduced communication/ synchronization complexity. Moreover, a bad distribution strategy during migration from single- to multi-core and from multi- to many-core hardware does not always return the expected performance gain. This paper presents two novel AI-based approaches for optimal distribution (minimal inter-core communication inspite of no deadline misses) of software system on multi-core hardware architecture. We discuss the comparisons of our machine learning solutions based on unsupervised and reinforcement learning. We share the benefits and limitations of using unsupervised learning and reinforcement learning based on our experience.