English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multi-core architectures can be leveraged to allow independent processes torun in parallel. However, due to resources shared across cores, such as caches,distinct processes may interfere with one another, e.g. affecting executiontime. Analysing the extent of this interference is difficult due to: (1) thediversity of modern architectures, which may contain different implementationsof shared resources, and (2) the complex nature of modern processors, in whichinterference might arise due to subtle interactions. To address this, wepropose a black-box auto-tuning approach that searches for processes that areeffective at causing slowdowns for a program when executed in parallel. Suchslowdowns provide lower bounds on worst-case execution time; an importantmetric in systems with real-time constraints.  Our approach considers a set of parameterised "enemy" processes and "victim"programs, each targeting a shared resource. The autotuner searches for enemyprocess parameters that are effective at causing slowdowns in the victimprograms. The idea is that victim programs behave as a proxy for sharedresource usage of arbitrary programs. We evaluate our approach on: 5 differentchips; 3 resources (cache, memory bus, and main memory); and consider severalsearch strategies and slowdown metrics. Using enemy processes tuned per chip,we evaluate the slowdowns on the autobench and coremark benchmark suites andshow that our method is able to achieve slowdowns in 98% of benchmark/chipcombinations and provide similar results to manually written enemy processes.

Do Your Cores Play Nicely? A Portable Framework for Multi-core Interference Tuning and Analysis