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Graph-based features for machine learning driven code optimization
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Maskinlärnings-driven programkodsoptimering med graf-baserad datautvinning (Swedish)
Abstract [en]

In this paper we present a method of using the Shortest-Path Graph Kernel, on graph-based features of computer programs, to train a Support Vector Regression model which predicts execution time speedup over baseline given an unseen program and a point in optimization space, based on a method proposed in Using Graph-Based Program Characterization for Predictive Modeling by Park et al.

The optimization space is represented by command-line parameters to the polyhedral C-to-C compiler PoCC, and PolyBench is used to generate the data set of speedups over baseline.

The model is found to produce results reasonable by some metrics, but due to the large error and the pseudo-random behaviour of the output the method, in its current form, must reluctantly be rejected.

Abstract [sv]

I den här raporten presenterar vi en metod att träna en Stöd-vektor-regressions-modell som givet ett osett program och en punkt i optimeringsrymden kan förutsäga hur mycket snabbare över baslinjen programmet kommer att exekvera förutsatt att man applicerar givna optimeringar. För att representera programmet använder vi en grafstruktur för vilken vi kan använda en grafkärna, Shortest-Path Graph Kernel, vilken kan avgöra hur lika två olika grafer är varandra. Metoden är baserad på en metod presenterad av Park et al. i Using Graph-Based Program Characterization for Predictive Modeling.

Optimeringsrymden erhålls genom olika kombinationer av kommandoradsparametrar till den polyhedriska C-till-C-kompilatorn PoCC. Testdatat erhölls genom att förberäkna hastighetsfaktorer för alla optimeringar och alla program i test-algoritms-biblioteket PolyBench.

Vi finner att modellen i vissa mått mätt producerar "bra" resultat, men p.g.a. av det stora felet och det slumpmässiga beteendet måste dessvärre metoden, i dess nuvarande form,förkastas.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Machine Learning, Graph Kernel, Compilation, Big Data Tuning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-211444OAI: oai:DiVA.org:kth-211444DiVA, id: diva2:1129224
External cooperation
Kyushu University, System Architecture Lab
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Presentation
2017-06-19, 4523, Lindstedtsvägen 5, Stockholm, 11:00 (English)
Supervisors
Examiners
Available from: 2017-10-16 Created: 2017-08-01 Last updated: 2018-01-13Bibliographically approved

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