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Modelling Application Cache Behavior using Regression Models
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Mälardalen University. (Model-Based Engineering of Embedded Systems)ORCID iD: 0000-0002-3755-562X
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-2612-4135
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1996-1234
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2021 (English)In: / [ed] IEEE, Västerås, 2021Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we describe the creation of resource usage forecasts for applications with unknown execution characteristics, by evaluating different regression processes, including autoregressive, multivariate adaptive regression splines, exponential smoothing, etc. We utilize Performance Monitor Units (PMU) and generate hardware resource usage models for the L-2-cache and the L-3-cache using nine different regression processes. The measurement strategy and regression process methodology are general and applicable to any given hardware resource when performance counters are available. We use three benchmark applications: the SIFT feature detection algorithm, a standard matrix multiplication, and a version of Bubblesort. Our evaluation shows that Multi Adaptive Regressive Spline (MARS) models generate the best resource usage forecasts among the considered models, followed by Single Exponential Splines (SES) and Triple Exponential Splines (TES).

Place, publisher, year, edition, pages
Västerås, 2021.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-56073DOI: 10.1109/COMPSAC51774.2021.00284ISI: 000706529000273Scopus ID: 2-s2.0-85115859046ISBN: 978-1-6654-2463-9 (print)OAI: oai:DiVA.org:mdh-56073DiVA, id: diva2:1599694
Conference
The 11th IEEE International Workshop on Industrial Experience in Embedded Systems Design (IEESD 2021)
Available from: 2021-10-01 Created: 2021-10-01 Last updated: 2021-11-11Bibliographically approved
In thesis
1. Automatic Characterization and Mitigation of Shared-resource Contention in Multi-core Systems
Open this publication in new window or tab >>Automatic Characterization and Mitigation of Shared-resource Contention in Multi-core Systems
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Multi-core computers are infamous for being hard to use in time-critical systems due to execution-time variations as an effect of shared resource contention. In this thesis, we study the problem of shared resource contention, which occurs when multiple applications executing on different cores do not have exclusive access to of a shared hardware resource. We investigate performance variations of parallel tasks in multi-core systems and present a method to pinpoint the source of the resource contention using hardware performance counters. We investigate mitigation methods for performance variations due to resource contention, including the Jailhouse hypervisor and the cache-partitioning tool PALLOC. We propose a benchmark strategy that quantifies the isolation gained from a specific isolation technique and exemplify this strategy using the Jailhouse hypervisor. We furthermore present and implement solutions for cache-partition allocation during application runtime. Our implementation aims to avoid over-provisioning of cache through pre-runtime estimations of an application's dependency towards the cache and continuous re-partitioning of the cache memory during application runtime.

The primary goal of this thesis is to contribute to a process that automates some of the tedious manual testing needed to detect resource contention bottlenecks. The methods we present in this provide a holistic solution for automatic mitigating resource-contention in a multi-core system. First, we evaluate the risk for shared resource contention when several applications execute simultaneously. We then allocate partitions to mitigate resource contention for applications that risk severe performance degradations. We finally present methods that dynamically re-allocate partition space to meet the performance requirements of the running applications. 

Abstract [sv]

Flerkärniga datorer är ökända för att vara svåra att använda i tidskritiska system på grund av prestandavariationer som sker på grund av samtidigt delande av hårdvaruresurser. I denna avhandling studerar vi problemet med delade resurser som uppstår när flera applikationer som körs på olika kärnor inte har exklusivt ägande av en delad resurs. Vi undersöker prestationsvariationer för parallella uppgifter i flerkärniga system och presenterar en metod för att identifiera källan till resurskonflikten med hjälp av befintliga hårdvaruprestationsräknare. Vi undersöker begränsningsmetoder för prestationsvariationer på grund av resurstvister, inklusive Jailhouse-hypervisor och cachepartitionsverktyget PALLOC. Vi föreslår en riktmärkesstrategi som kvantifierar isoleringen från en specifik isoleringsteknik och exemplifierar denna strategi med hjälp av Jailhouse -hypervisor. Vi presenterar och implementerar dessutom lösningar för tilldelningskontroll för cachepartitioner under applikationstiden. Vår implementering syftar till att undvika onödiga cacheallokeringar genom att uppskattninga programmets beroende av cacheminnet och kontinuerlig omallokering av cacheminnet medans applikationen kör.

Huvudmålet med denna avhandling är att underlätta den manuella testningen av resurskonflits-flaskhalsar och istället föreslå en automatiska metoder. De metoder vi presenterar ger en helhetslösning för automatisk lindring av resurskonflikter i ett flerkärnigt system. Först utvärderar vi risken för negativ påverkan genom delade resurser när flera applikationer körs samtidigt. Vi tilldelar sedan partitioner för att mildra resurskonflikter för applikationer som riskerar allvarliga prestandaförsämringar. Vi presenterar slutligen metoder som dynamiskt omallokerar cacheminne för att uppfylla prestandakraven för de applikationer som körs.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2021. p. 254
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 348
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-56075 (URN)978-91-7485-527-2 (ISBN)
Public defence
2021-11-19, Paros, Mälardalens högskola, Västerås, 13:00 (English)
Opponent
Supervisors
Available from: 2021-10-07 Created: 2021-10-01 Last updated: 2022-11-08Bibliographically approved

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Danielsson, JakobMarcus, JägemarSeceleanu, TiberiuBehnam, MorisSjödin, Mikael

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