Efficient model transformations by combining pattern matching strategies
•
0 gefällt mir•371 views
Conference presentation at ICMT 2009.
Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie Efficient model transformations by combining pattern matching strategies
Ähnlich wie Efficient model transformations by combining pattern matching strategies (20)
Mehr von Istvan Rath
Mehr von Istvan Rath (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
Efficient model transformations by combining pattern matching strategies
- 1. Budapest University of Technology and Economics Efficient Model Transformations by Combining Pattern Matching Strategies Gábor Bergmann, Ákos Horváth, István Ráth, Dániel Varró Fault-tolerant Systems Research Group
- 2. Budapest University of Technology and Economics Talk Overview GT & PM Introduction overview Hybrid PM Measurements Fault-tolerant Systems Research Group
- 3. Budapest University of Technology and Economics Introduction Common problem to be solved by model transformation tools: − Efficient query and manipulation of complex graph- based patterns One possible solution: − Graph transformation Fault-tolerant Systems Research Group
- 4. Budapest University of Technology and Economics Benchmarking Aim: − systematic and reproducible measurements − on performance − under varying and precisely defined circumstances Overall goal: − help transformation engineers in selecting tools − serve as reference for future research Popular approach in different fields − AI − relational databases − rule-based expert systems Fault-tolerant Systems Research Group
- 5. Budapest University of Technology and Economics Talk overview GT & PM Introduction overview Hybtid PM Measurements Fault-tolerant Systems Research Group
- 6. Budapest University of Technology and Economics Metamodeling Package schemaRef Schema Class tableRef Table Association tableRef fKeyRef EndPoint ForeignKey columnRef Metamodel Attribute Column Instance model jarEntry: Class lang: Package jarFile: Class jar: Package java: Package Fault-tolerant Systems Research Group
- 7. Budapest University of Technology and Economics Graph Transformation schemaRule :schemaRef Con: Container java: Package Schema <<contains>> NEG {NEW} P: Package SN:Schema lang: Package :schemaRef {NEW} jar: Package Phases of GT matching – Pattern Matching phase – Updating phase: delete+ create Pattern Matching is the most critical issue from the performance viewpoint (in our experience) Fault-tolerant Systems Research Group
- 8. Budapest University of Technology and Economics Pattern matching techniques Execution strategies − Interpreted: AGG (Tiger), VIATRA, MOLA, Groove, ATL ● underlying PM engine − Compiled: Fujaba, GReAT, PROGRES, Tiger, VMTS, GrGEN.NET, ... ● directly executed as C(#) or Java code Algorithms − Constraint satisfaction: AGG (Tiger) ● variables + constraints − Local search (LS): Fujaba, GReAT, PROGRES, VIATRA, MOLA, Groove, Tiger (Compiled), GrGEN.NET, ... ● step-by-step extension of the matching − Incremental (INC): VIATRA, Tefkat ● Updated cache mechanism Fault-tolerant Systems Research Group
- 9. Budapest University of Technology and Economics Traditional Local Search-based pattern matching Method − usually defined at design/compile time − simple search plan − hard wired precedence for constraint checking (NAC, injectivity, attribute, etc.) ● Can be done adaptively Good performance expected when: − Small patterns, bound input parameters Fault-tolerant Systems Research Group
- 10. Budapest University of Technology and Economics Local Search based Pattern Matching Example frequently used & efficient solution schemaRule Con: Container Search plan <<contains>> NEG Con P: Package SN:Schema 1 :schemaRef <<contains>> 2 P 3 Constraint checking (NAC, Search sequence injectivity, etc.) order of traversal in the search plan Fault-tolerant Systems Research Group
- 11. Budapest University of Technology and Economics Incremental Pattern Matching Goal − Store matching sets − Incremental update − Fast response Good performance expected when: − frequent pattern matching − Small updates Possible application domain − E.g. synchronization, constraints, model simulation, etc. In VIATRA: an adapted RETE algorithm Fault-tolerant Systems Research Group
- 12. Budapest University of Technology and Economics Incremental Pattern Matching Example • RETE net – nodes: intermediate matchings INPUT p4 – edge: update propagation p1 p2 p3 c1 c2 s1 p4 • Example p4 p4 p4 – input: schemaRule pattern Package Class Schema – pattern: contained Package p1 p2 p3 p4 c1 c2 s1 – update: new package p4 p1: Package <<contains>> p1, p2 p1, p3 p3, p4 p2: Package p3: Package p4 p3, p4 NAC schemaRef p2, s1 s1:Schema c1: Class p4: Package out p1, p3 p3,p4 Fault-tolerant Systems Research Group
- 13. Budapest University of Technology and Economics Talk overview GT & PM Introduction overview Hybrid PM Measurements Fault-tolerant Systems Research Group
- 14. Budapest University of Technology and Economics Hybrid pattern matching Idea: combine local search-based and incremental pattern matching Motivation − Incremental PM is better for most cases, but… ● Has memory overhead! ● Has update overhead − LS might be better in certain cases Based on experience with a ”real world” transformation application1 1Kovacs, M., Lollini, P., Majzik, I., Bondavalli, A.: An Integrated Framework for the Dependability Evaluation of Distributed Mobile Applications (SERENE’08) Fault-tolerant Systems Research Group
- 15. Budapest University of Technology and Economics Where LS might be better… Memory consumption − RETE sizes grow roughly linearly with the model space − Constrained memory trashing Cache construction time penalty − RETE networks take time to construct − „navigation patterns” can be matched quicker by LS Expensive updates − Certain patterns’ matching set is HUGE − Depends largely on the transformation Fault-tolerant Systems Research Group
- 16. Budapest University of Technology and Economics Case study: ORM Synchronization :schemaRef : Package : Schema :tableRef : Class : Table :columnRef : Attribute : Attribute : Column : Column :columnRef Transformation workflow Check Modify Perform source source Synchronize mapping model model Fault-tolerant Systems Research Group
- 17. Budapest University of Technology and Economics Phases Check Phase Initial Transformation − Well-formedness checking − Match reusability − Static graph structure − Unidirectional − No model manipulation − Complex rules − Batch like execution Refactoring Synchronization − Single rule executed: − Match reusability move package in the − Unidirectional hierarchy − Simple rules − Manual execution − Live execution Fault-tolerant Systems Research Group
- 18. Budapest University of Technology and Economics Phases Check Phase Initial Transformation − Simple patterns, looking − Processes the entire only for the first match model (full traversal) − INC: cache construction − Match set may not fit into penalty high memory − LS may be a better choice − Solution: decompose, use LS for certain patterns Refactoring Synchronization − Move in containment − INC significantly better (as hierarchy: very expensive demonstrated at ICGT08) cache update − LS can be significantly better Fault-tolerant Systems Research Group
- 19. Budapest University of Technology and Economics Hybrid PM in the source code Assign a PM implementation on a per-pattern (per-rule) basis ability to fine tune performance on a very fine grained level. Fault-tolerant Systems Research Group
- 20. Budapest University of Technology and Economics Talk overview GT & PM Introduction overview Hybrid PM Measurements Fault-tolerant Systems Research Group
- 21. Budapest University of Technology and Economics Environment Hardware and OS − 1.8 GHz Intel Core2 Duo − 2048 MB RAM − Windows XP SP3 − Sun JVM 1.6.0_02 for VIATRA Tool related − VIATRA2 R3 Build 2009.02.03 − Standard services of the default distribution Fault-tolerant Systems Research Group
- 22. Budapest University of Technology and Economics Composite ORM Synchronization benchmark Hybrid scales be-er with increasingly large models! Fault-tolerant Systems Research Group
- 23. Budapest University of Technology and Economics Considerations for selecting PM strategy Graph pattern static attributes − Number of patterns − Pattern size − Containment constraints Control structures − Parameter passing − Usage frequencies − Model update cost Model-dependent pattern characteristics − Model statistics (instance count) Fault-tolerant Systems Research Group
- 24. Budapest University of Technology and Economics Adaptive hybrid pattern matching Goal: provide (semi-) automatic aid for strategy selection Idea: monitor memory usage − JVM telemetry Prevent heap exhaustion − Destroy match set cache structures − Switch to LS PM Strategy Used heap [MB] Transform phase execution time [s] LS 201 77.1 INC 353 13.6 Static hybrid 220 10.9 Adaptive hybrid 235 35.7 Fault-tolerant Systems Research Group
- 25. Budapest University of Technology and Economics Further benchmarking Paper for a GRaBaTS 2008 special issue in STTT’09: Experimental assessment of combining pattern matching strategies with VIATRA2 In-detail investigation − hybrid approach − Transformation language-level optimizations Optimization − Based on experience with ICMT’09 paper Fault-tolerant Systems Research Group
- 26. Budapest University of Technology and Economics AntWorld Results Linear characteristic retained, slower by only a constant multiplier Fault-tolerant Systems Research Group
- 27. Budapest University of Technology and Economics Memory footprint Linear reduction in memory overhead Fault-tolerant Systems Research Group
- 28. Budapest University of Technology and Economics Summary Optimization strategy Performance Memory footprint LS High order polynomial Constant Switch to INC Polynomial order reduction Linear increase with model size Switch to Hybrid Linear (~50%) reduction Linear (~50%) reduction In short: you may get a linear decrease in memory for a linear increase in execution time retains complexity class characteristics Fault-tolerant Systems Research Group
- 29. Budapest University of Technology and Economics ? Fault-tolerant Systems Research Group