Implementation of several scheduling methods to improve the performance of static analyses #263
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…se can run without exception
This reverts commit 0d3ba57.
…erTest.scala eingepflegt, erste Anfänge Purity anzupassen
# Conflicts: # DEVELOPING_OPAL/tools/src/main/scala/org/opalj/support/info/Immutability.scala # OPAL/si/src/main/scala/org/opalj/fpcf/seq/PKESequentialPropertyStore.scala
…debugging purposes ONLY!
…es at once. Old immutability with manual batching is also included in the folder
…asurement # Conflicts: # OPAL/tac/src/main/scala/org/opalj/tac/fpcf/analyses/cg/DoPrivilegedCGAnalysis.scala
…r the handleNewCallers method in the FieldLocalityAnalysis.scala
…ss to be able to set the Config in the Runner/Analysis. Implementation of new schedulers becomes easier.
errt
requested changes
May 6, 2025
| @@ -4,7 +4,7 @@ org.opalj { | |||
| // to support the debugging of analyses developed using the property store. | |||
| // I.e., debug performs a wide range of additionaly checks to identify errors as | |||
| // early as possible. | |||
| fpcf.PropertyStore.Debug = false | |||
| fpcf.PropertyStore.Debug = true | |||
| if (cleanUpSubPhase.isEmpty) { | ||
| finalizationOrder.toArray | ||
| (propertyKindsComputedInThisPhase -- finalizationOrder.flatten.toSet) | ||
| this.subPhaseFinalizationOrder = (if (cleanUpSubPhase.isEmpty) { |
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This is not pretty. Can it be cleaned up? At least it should be documented what it's doing.
| val scenario = AnalysisScenario(Set(eager1, eager3, eager4, eager5, eager6, eager2, lazy1, lazy2, lazy3, transformer1, triggered1, triggered2, triggered3), ps) | ||
| val schedule = scenario.computeSchedule(ps) | ||
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| print(schedule.batches) |
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This isn't a test if it just prints stuff
| @@ -44,6 +43,114 @@ class PropertyComputationsSchedulerTest extends AnyFunSpec with Matchers with Be | |||
| derivesEagerly = Set(PropertyBounds.lub(pks(1))) | |||
| ) | |||
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| def analysis1IsScheduledBeforeOrAtTheSameBatchAsAnalysis2( | |||
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What are these methods for? They don't seem to be used
| /** | ||
| * This ImmutabilityRunner has been adapted for the scheduling strategies. This means that all analyses are transferred to one scheduler, | ||
| * whereby the call graph is transferred to the scheduler with the other analyses in “dependencies”. | ||
| * The original immutability analysis is the "Immutability.scala". |
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Please merge into the original runner or remove, we can't maintain two almost identical ones.
| counter = counter + 1 | ||
| } | ||
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| val transformedGraph = preparedGraph.map { case (node, deps) => |
| sortedNodes = sortedNodes :+ node | ||
| } | ||
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| } |
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Insert blank line after method definition
| } | ||
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| sortedNodes | ||
| } |
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Insert blank line after method definition
| def mergeIndependentBatches( | ||
| tfMap: Map[Int, List[Int]], | ||
| ct: Int, | ||
| graph: Map[Int, List[Int]] |
| graph: Map[Int, List[Int]] | ||
| ): Map[Int, List[Int]] = { | ||
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| var allUses: Set[Int] = Set.empty |
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This mutable variable seems like a bad design choice
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Formatting also needs to be applied: |
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Static subanalyses are divided into different batches and summarized by the strategies in order to shorten the runtime.
This approach offers 4 strategies:
Single Phase Scheduling (SPS) - All sub-analyses are put into one batch and executed (may lead to wrong results in some cases. Further research needed).
Maximum Phase Scheduling (MP)- This strategy tries to create the most phases from the given analyses.
Independent Phase Merge Scheduling (IPMS) - An extension of the MPS strategy that executes independent phases together.
Smallest Phase Merge Scheduling (SPMS) - Also an extension of the MPS strategy, which merges independent phases into one phase based on the number of partial analyses.
There is also a Lazy Transformer Multiple Phase Flag, which allows the analyses to schedule lazy and transformer partial analyses in multiple phases.