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Summary on the ASM analyzer framework --------------------------------------
Summary on the ASM analyzer framework --------------------------------------
Value
- Abstract, needs to be implemented for each analysis.
- Represents the desired information about local variables and stack values, for example:
- Is this value known to be null / not null?
- What are the instructions that could potentially have produced this value?
Interpreter
- Abstract, needs to be implemented for each analysis. Sometimes one can subclass an existing interpreter, e.g., SourceInterpreter or BasicInterpreter.
- Multiple abstract methods that receive an instruction and the instruction's input values, and
return a value representing the result of that instruction.
- Note: due to control flow, the interpreter can be invoked multiple times for the same instruction, until reaching a fixed point.
- Abstract
mergefunction that computes the least upper bound of two values. Used by Frame.merge (see below).
Frame
- Can be used directly for many analyses, no subclass required.
- Every frame has an array of values: one for each local variable and for each stack slot.
- A
topindex stores the index of the current stack top - NOTE: for a size-2 local variable at index i, the local variable at i+1 is set to an empty value. However, for a size-2 value at index i on the stack, the value at i+1 holds the next stack value. IMPORTANT: this is only the case in ASM's analysis framework, not in bytecode. See comment below.
- A
- Defines the
execute(instruction)method.- executing mutates the state of the frame according to the effect of the instruction
- pop consumed values from the stack
- pass them to the interpreter together with the instruction
- if applicable, push the resulting value on the stack
- executing mutates the state of the frame according to the effect of the instruction
- Defines the
merge(otherFrame)method- called by the analyzer when multiple control flow paths lead to an instruction
- the frame at the branching instruction is merged into the current frame of the instruction (held by the analyzer)
- mutates the values of the current frame, merges all values using interpreter.merge.
- called by the analyzer when multiple control flow paths lead to an instruction
Analyzer
- Stores a frame for each instruction
mergefunction takes an instruction and a frame, merges the existing frame for that instr (from the frames array) with the new frame passed as argument. if the frame changed, puts the instruction on the work queue (fixpoint).- initial frame: initialized for first instr by calling interpreter.new[...]Value
for each slot (locals and params), stored in frames[firstInstr] by calling
merge - work queue of instructions (
queuearray,topindex for next instruction to analyze) - analyze(method): simulate control flow. while work queue non-empty:
- copy the state of
frames[instr]into a local framecurrent - call
current.execute(instr, interpreter), mutating thecurrentframe - if it's a branching instruction
- for all potential destination instructions
- merge the destination instruction frame with the
currentframe (this enqueues the destination instr if its frame changed)
- merge the destination instruction frame with the
- invoke
newControlFlowEdge(see below)
- for all potential destination instructions
- copy the state of
- the analyzer also tracks active exception handlers at each instruction
- the empty method
newControlFlowEdgecan be overridden to track control flow if required
MaxLocals and MaxStack ----------------------
At the JVM level, long and double values occupy two slots, both as local variables and on the stack, as specified in the JVM spec 2.6.2: "At any point in time, an operand stack has an associated depth, where a value of type long or double contributes two units to the depth and a value of any other type contributes one unit."
For example, a method class A { def f(a: Long, b: Long) = a + b } has MAXSTACK=4 in the classfile. This value is computed by the ClassWriter / MethodWriter when generating the classfile (we always pass COMPUTE_MAXS to the ClassWriter).
For running an ASM Analyzer, long and double values occupy two local variable slots, but only a single slot on the call stack, as shown by the following snippet:
import scala.tools.nsc.backend.jvm._ import scala.tools.nsc.backend.jvm.opt.BytecodeUtils._ import scala.collection.convert.decorateAsScala._ import scala.tools.asm.tree.analysis._
val cn = AsmUtils.readClass("/Users/luc/scala/scala/sandbox/A.class") val m = cn.methods.iterator.asScala.find(_.name == "f").head
// the value is read from the classfile, so it's 4 println(s"maxLocals: ${m.maxLocals}, maxStack: ${m.maxStack}") // maxLocals: 5, maxStack: 4
// we can safely set it to 2 for running the analyzer. m.maxStack = 2
val a = new Analyzer(new BasicInterpreter) a.analyze(cn.name, m) val addInsn = m.instructions.iterator.asScala.find(_.getOpcode == 97).get // LADD Opcode val addFrame = a.frameAt(addInsn, m)
addFrame.getStackSize // 2: the two long values only take one slot each addFrame.getLocals // 5: this takes one slot, the two long parameters take 2 slots each
While running the optimizer, we need to make sure that the
maxStackvalue of a method is large enough for running an ASM analyzer. We don't need to worry if the value is incorrect in the JVM perspective: the value will be re-computed and overwritten in the ClassWriter.Lessons learnt while benchmarking the alias tracking analysis -------------------------------------------------------------
Profiling
- Use YourKit for finding hotspots (cpu profiling). when it comes to drilling down into the details of a hotspot, don't pay too much attention to the percentages / time counts.
- Should also try other profilers.
- Use timers. When a method showed up as a hotspot, I added a timer around that method, and a second one within the method to measure specific parts. The timers slow things down, but the relative numbers show what parts of a method are slow.
ASM analyzer insights
- The time for running an analysis depends on the number of locals and the number of instructions. Reducing the number of locals helps speeding up the analysis: there are less values to merge when merging to frames. See also https://github.com/scala/scala-dev/issues/47
- The common hot spot of an ASM analysis is Frame.merge, for example in producers / consumers.
- For nullness analysis the time is spent as follows
- 20% merging nullness values. this is as expected: for example, the same absolute amount of time is spent in merging BasicValues when running a BasicInterpreter.
- 50% merging alias sets. i tried to optimize what i could out of this.
- 20% is spent creating new frames from existing ones, see comment on AliasingFrame.init.
- The implementation of Frame.merge (the main hot spot) contains a megamorphic callsite to
interpreter.merge. This can be observed easily by running a test program that either runs a BasicValue analysis only, versus a program that first runs a nullness analysis and then a BasicValue. In an example, the time for the BasicValue analysis goes from 519ms to 1963ms, a 3.8x slowdown. - I added counters to the Frame.merge methods for nullness and BasicValue analysis. In the examples I benchmarked, the number of merge invocations was always exactly the same. It would probably be possible to come up with an example where alias set merging forces additional analysis rounds until reaching the fixpoint, but I did not observe such cases.
To benchmark an analysis, instead of benchmarking analysis while it runs in the compiler backend, one can easily run it from a separate program (or the repl). The bytecode to analyze can simply be parsed from a classfile. See example at the end of this comment.
Nullness Analysis in Miguel's Optimizer ---------------------------------------
Miguel implemented alias tracking for nullness analysis differently [1]. Remember that every frame has an array of values. Miguel's idea was to represent aliasing using reference equality in the values array: if two entries in the array point to the same value object, the two entries are aliases in the frame of the given instruction.
While this idea seems elegant at first sight, Miguel's implementation does not merge frames correctly when it comes to aliasing. Assume in frame 1, values (a, b, c) are aliases, while in frame 2 (a, b) are aliases. When merging the second into the first, we have to make sure that c is removed as an alias of (a, b).
It would be possible to implement correct alias set merging in Miguel's approach. However, frame merging is the main hot spot of analysis. The computational complexity of implementing alias set merging by traversing the values array and comparing references is too high. The concrete alias set representation that is used in the current implementation (see class AliasingFrame) makes alias set merging more efficient.
[1] https://github.com/scala-opt/scala/blob/opt/rebase/src/compiler/scala/tools/nsc/backend/bcode/NullnessPropagator.java
Complexity and scaling of analysis ----------------------------------
The time complexity of a data flow analysis depends on:
- The size of the method. The complexity factor is linear (assuming the number of locals and branching instructions remains constant). The main analysis loop runs through all instructions of a method once. Instructions are only re-enqueued if a control flow merge changes the frame at some instruction.
- The branching instructions. When a second (third, ..) control flow edge arrives at an instruction, the existing frame at the instruction is merged with the one computed on the new branch. If the merge function changes the existing frame, the instruction is enqueued for another analysis. This results in a merge operation for the successors of the instruction.
- The number of local variables. The hot spot of analysis is frame merging. The merge function iterates through the values in the frame (locals and stack values) and merges them.
I measured the running time of an analysis for two examples:
- Keep the number of locals and branching instructions constant, increase the number of instructions. The running time grows linearly with the method size.
- Increase the size and number of locals in a method. The method size and number of locals grow in the same pace. Here, the running time increase is polynomial. It looks like the complexity is be #instructions * #locals^2 (see below).
I measured nullness analysis (which tracks aliases) and a SimpleValue analysis. Nullness runs roughly 5x slower (because of alias tracking) at every problem size - this factor doesn't change.
The numbers below are for nullness. Note that the last column is constant, i.e., the running time is proportional to #ins * #loc^2. Therefore we use this factor when limiting the maximal method size for running an analysis.
#insns #locals time (ms) time / #ins * #loc2 * 106 1305 156 34 1.07 2610 311 165 0.65 3915 466 490 0.57 5220 621 1200 0.59 6525 776 2220 0.56 7830 931 3830 0.56 9135 1086 6570 0.60 10440 1241 9700 0.60 11745 1396 13800 0.60
As a second experiment, nullness analysis was run with varying #insns but constant #locals. The last column shows linear complexity with respect to the method size (linearOffset = 2279):
#insns #locals time (ms) (time + linearOffset) / #insns 5220 621 1090 0.645 6224 621 1690 0.637 7226 621 2280 0.630 8228 621 2870 0.625 9230 621 3530 0.629 10232 621 4130 0.626 11234 621 4770 0.627 12236 621 5520 0.637 13238 621 6170 0.638
When running a BasicValue analysis, the complexity observation is the same (time is proportional to #ins * #loc^2).
Measuring analysis execution time ---------------------------------
See code below.
- package opt
Type Members
- abstract class BCodeBodyBuilder extends BCodeSkelBuilder
- abstract class BCodeHelpers extends BCodeIdiomatic
- abstract class BCodeIdiomatic extends AnyRef
- abstract class BCodeSkelBuilder extends BCodeHelpers
- abstract class BCodeSyncAndTry extends BCodeBodyBuilder
- abstract class BTypes extends AnyRef
The BTypes component defines The BType class hierarchy.
The BTypes component defines The BType class hierarchy. A BType stores all type information that is required after building the ASM nodes. This includes optimizations, generation of InnerClass attributes and generation of stack map frames.
The representation is immutable and independent of the compiler data structures, hence it can be queried by concurrent threads.
- abstract class BTypesFromClassfile extends AnyRef
- abstract class BTypesFromSymbols[G <: Global] extends BTypes
This class mainly contains the method classBTypeFromSymbol, which extracts the necessary information from a symbol and its type to create the corresponding ClassBType.
This class mainly contains the method classBTypeFromSymbol, which extracts the necessary information from a symbol and its type to create the corresponding ClassBType. It requires access to the compiler (global parameter).
- trait BackendStats extends AnyRef
- class ClassNode1 extends ClassNode
A subclass of
ClassNodeto customize the representation of label nodes withLabelNode1. - abstract class ClassfileWriters extends AnyRef
- final class ClearableJConcurrentHashMap[K, V] extends Clearable
- abstract class CodeGen[G <: Global] extends PerRunInit
- final class CompilationUnitInPostProcess extends AnyRef
State for a compilation unit being post-processed.
State for a compilation unit being post-processed.
- Holds the classes to post-process (released for GC when no longer used)
- Keeps a reference to the future that runs the post-processor
- Buffers messages reported during post-processing
- final case class CompilationUnitPaths(sourceFile: AbstractFile, outputDir: AbstractFile) extends Product with Serializable
Paths for a compilation unit, used during classfile writing
- abstract class CoreBTypes extends PerRunInit
- abstract class CoreBTypesFromSymbols[G <: Global] extends CoreBTypes
- abstract class GenBCode extends SubComponent
Some notes about the backend's state and its initialization and release.
Some notes about the backend's state and its initialization and release.
State that is used in a single run is allocated through
recordPerRunCache, for exampleByteCodeRepository.compilingClassesorCallGraph.callsites. This state is cleared at the end of each run.Some state needs to be re-initialized per run, for example
CoreBTypes(computed from Symbols / Types) or theGeneratedClassHandler(depends on the compiler settings). This state is (re-) initialized in theGenBCode.initializemethod. There two categories:- State that is stored in a
varfield and (re-) assigned in theinitializemethod, for example theGeneratedClassHandler2. State that uses thePerRunInit/bTypes.perRunLazy/LazyVarinfrastructure, for example the types inCoreBTypes
The reason to use the
LazyVarinfrastructure is to prevent eagerly computing all the state even if it's never used in a run. It can also be used to work around initialization ordering issues, just like ordinary lazy vals. For state that is known to be accessed, avarfield is just fine.Typical
LazyVaruse:lazy val state: LazyVar[T] = perRunLazy(component)(initializer)- The
initializerexpression is executed lazily - When the initializer actually runs, it synchronizes on the
PostProcessorFrontendAccess.frontendLock - The
component.initializemethod causes theLazyVarto be re-initialized on the nextget - The
stateis itself alazy valto make sure thecomponent.initializemethod only clears thoseLazyVars that were ever accessed
TODO: convert some uses of
LazyVarto ordinaryvar. - State that is stored in a
- case class GeneratedClass(classNode: ClassNode, sourceClassName: String, position: Position, isArtifact: Boolean) extends Product with Serializable
The result of code generation.
The result of code generation. isArtifact is
truefor mirror and bean-info classes. - case class GeneratedCompilationUnit(sourceFile: AbstractFile, classes: List[GeneratedClass]) extends Product with Serializable
- class LabelNode1 extends LabelNode
A subclass of
LabelNodeto add user-definable flags. - class MethodNode1 extends MethodNode
A subclass of
MethodNodeto customize the representation of label nodes withLabelNode1. - trait PerRunInit extends AnyRef
Utility for backend components that have state that needs to be re-initialized at every compiler run, for example state that depends on compiler settings of frontend types (Symbols, Types).
Utility for backend components that have state that needs to be re-initialized at every compiler run, for example state that depends on compiler settings of frontend types (Symbols, Types).
The trait provides an
initializemethod that runs all initializers added throughperRunLazy. - abstract class PostProcessor extends PerRunInit
Implements late stages of the backend that don't depend on a Global instance, i.e., optimizations, post-processing and classfile serialization and writing.
- sealed abstract class PostProcessorFrontendAccess extends AnyRef
Functionality needed in the post-processor whose implementation depends on the compiler frontend.
Functionality needed in the post-processor whose implementation depends on the compiler frontend. All methods are synchronized.
Value Members
- object AsmUtils
- object BCodeHelpers
- object BTypes
- object BackendReporting
Utilities for error reporting.
Utilities for error reporting.
Defines some utility methods to make error reporting with Either easier.
- object GenBCode
- object PostProcessorFrontendAccess
The Scala compiler and reflection APIs.