Genetic Improvement of Last Level Cache

Langdon, William B.; Clark, David

doi:10.1007/978-3-031-56957-9_13

Lecture Notes in Computer Science

2024

DOI: 10.1007/978-3-031-56957-9_13

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Genetic Improvement of Last Level Cache

William B. Langdon,

David Clark

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2024

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Cited by 2 publications

References 49 publications

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Deep imperative mutations have less impact

Langdon,

Clark

2024

Autom Softw Eng

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Information theory and entropy loss predict deeper more hierarchical software will be more robust. Suggesting silent errors and equivalent mutations will be more common in deeper code, highly structured code will be hard to test, so explaining best practise preference for unit testing of small methods rather than system wide analysis. Using the genetic improvement (GI) tool MAGPIE, we measure the impact of source code mutations and how this varies with execution depth in two diverse multi-level nested software. gem5 is a million line single threaded state-of-the-art C++ discrete time VLSI circuit simulator, whilst PARSEC VIPS is a non-deterministic parallel computing multi-threaded image processing benchmark written in C. More than 28–53% of mutants compile and generate identical results to the original program. We observe 12% and 16% Failed Disruption Propagation (FDP). Excluding internal errors, exceptions and asserts, here most faults below about 30 nested function levels which are Executed and Infect data or divert control flow are not Propagated to the output, i.e. these deep PIE changes have no visible external effect. Suggesting automatic software engineering on highly structured code will be hard.

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Deep imperative mutations have less impact

Langdon,

Clark

2024

Autom Softw Eng

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Sustaining Evolution for Shallow Embodied Intelligence

Langdon,

Hulme

2024

IOP Conf. Ser.: Mater. Sci. Eng.

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Lenski’s experiments with E. Coli show Biology can sustain continual evolutionary improvement. However long term evolutionary experiments (LTEE) with evolutionary computing find that information theory’s failed disruption propagation (FDP) in deeply nested genetic programming (GP) hierarchies can greatly slow adaptation. We propose that researchers aiming at embodied artificial intelligence should control software robustness by using porous high surface area geometrical architectures, perhaps composed of many shallow mangrove like tree structures intimately linked to their data rich fitness environment.

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Genetic Improvement of Last Level Cache

Cited by 2 publications

References 49 publications

Deep imperative mutations have less impact

Deep imperative mutations have less impact

Sustaining Evolution for Shallow Embodied Intelligence

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