identifying logically related regions of the heap
DESCRIPTION
Identifying Logically Related Regions of the Heap. Mark Marron 1 , Deepak Kapur 2 , Manuel Hermenegildo 1 1 Imdea-Software (Spain) 2 University of New Mexico. Overview. Want to identify regions (sets of objects) that are conceptually related Conceptually related - PowerPoint PPT PresentationTRANSCRIPT
Mark Marron1, Deepak Kapur2,Manuel Hermenegildo1
1Imdea-Software (Spain) 2University of New Mexico
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Want to identify regions (sets of objects) that are conceptually related
Conceptually related• Same recursive data structure• Stored in equivalent locations (e.g., same array)
Extract information via static analysis Apply memory optimizations on regions
instead of over entire heap• Region Allocation/Collection• Region/Parallel GC• Optimized Layout
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Must be Dynamic• Variable based partitions too coarse, do not
represent composition well.• Allocation site based too imprecise, can cause
spurious grouping of objects. Must be Repartitionable• Want to track program splitting and merging
regions: list append, subset operations.
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Base on storage shape graph• Nodes represent sets of objects (or recursive data
structures), edges represent sets of pointers• Has natural representation heap regions and
relations between them• Efficient
Annotate nodes and edges with additional instrumentation properties• For region identification only need type
information
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Recursive Structures• Group objects representing same recursive
structure, keep distinct from other recursive structures
References• Group objects stored in similar sets of locations
together (objects in A, in B, both A and B) Composite Structures• Group objects in each subcomponent, group
similar components hierarchically
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The general approach taken to Identifying Recursive Data Structures is well known• Look at type information to determine which
objects may be part of a recursive structure• Based on connectivity group these recursive
objects together Two subtle distinctions made in this work• Only group objects in complete recursive
structure• Ignore back pointers in computing complete
recursive structures
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class Enode{ Enode[] fromN; …}
The grouping of objects that are in the same container or related composite structures is more difficult
Given regions R, R’ when do they represent conceptually equivalent sets of objects• Stored in the same types of locations (variables,
collections, referred to by same object fields)• Have same type of recursive signature (can split
leaf contents of recursive structures from internal recursive component)
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N-Body Simulation in 3-dimensions Uses Fast Multi-Pole method with space
decomposition tree• For nearby bodies use naive n2 algorithm• For distant bodies compute center of mass of
many bodies and treat as single point mass
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for(…) { root = null; makeTree();
Iterator<Body> bm = this.bodyTabRev.iterator(); while(bm.hasNext()) bm.next().hackGravity(root);
Iterator<Body> bp = this.bodyTabRev.iterator(); while(bm.hasNext()) bm.next().propUpdatedAccel();}
Statically collect, space decomposition tree and all MathVector/double[] objects (11% of GC work).
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GC objects reachable from the acc/vel fields in parallel with the hackGravity method (no overhead).
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Inline Double[] into MathVector objects, 23% serial speedup 37% memory use reduction.
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Benchmark
LOC Analysis Time
Analysis Memory
Region Ok
tsp 910 0.03s <30 MB Y
em3d 1103 0.09s <30 MB Y
voronoi 1324 0.50s <30 MB Y
bh 2304 0.72s <30 MB Y
db 1985 0.68s <30 MB Y
raytrace 5809 15.5s 38 MB Y
exp 3567 152.3s 48 MB Y
debug 15293 114.8s 122 MB Y
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Simple interpreter and debug environment for large subset of Java language
14,000+ Loc (in normalized form), 90 Classes• Additional 1500 Loc for specialized standard library
handling stubs. Large recursive call structures, large
inheritance trees with numerous virtual method implementations
Wide range of data structure types, extensive use of java.util collections, heap contains both shared and unshared structures.
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Region Information provides excellent basis for driving many memory optimizations and supporting other analysis work
A simple set of heuristics (when taking into account a few subtleties) is sufficient for grouping memory objects
Recent work shows excellent scalability on non-trivial programs
Further work on developing robust infrastructure for further evaluation and applications
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