Building custom memory profilers. In# Gonzalez- Herrera Diverse Team Advisers: Johann Bourcier and Olivier Barais

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1 Building custom memory profilers In# Gonzalez- Herrera Diverse Team Advisers: Johann Bourcier and Olivier Barais

2 What are and why we need custom memory profilers? Developers think in terms of high- level abstrac#ons High- level abstrac#ons everywhere!!! Development frameworks Tooling- support is geing bejer DSLs Built atop existent technologies => using subop*mal tools when no op*on is available 2

3 Aspects in Kermeta 3 K3 defini#on User s mental model Real, low- level, representa#on 3

4 State- of- the- art profilers view? Object Class Name Instance Size A sizeof(a) + sizeof(a.a) AAspectProperty@fdsfdf3343 AAspectProperty sizeof(aaspectproperty) + sizeof(aaspectproperty.b) AAspect1Property@dfds\j566 AAspect1Property sizeof(aaspect1property). 4

5 Again why custom memory profilers? For execu#ves: Memory profilers for Java mostly offer support for standard Java abstrac#ons. As well as we None need at all custom or just editors, restricted we support need for custom maintenance tools addi#onal abstrac#ons But when there is support, solu#ons perform poorly This forbid their usage at run#me 5

6 What problem we face? Compute values on each subset entry A0 entry value key key value value key key value C A B C A B A.a B.b A.a B.b 6

7 Problem in detail The heap is a DAG with thousands of nodes. Such a DAG changes a lot over #me A memory analysis is: 1. Solve a subgraph matching problem (NP- complete) 2. Compute values on the subgraph Most queries are tractable OQL (Eclipse MAT, VisualVM) Cypher (internal experiments) Tractable doesn t mean ready for produc#on environments Preprocessing is a bojleneck Duplica#ng the DAG is a poor choice 7

8 Brief DSL s descripfon Subgraph specifica#on User- defined data Collec#ons Anonymous lambda expressions to operate on collec#ons 8

9 DSL for custom memory profiling Trade- off between expressiveness and performance The computa#onal model is simple and explicit Linear #me execu#on on the number of objects Easy to see the cost of profiling Execute the DSL by traversing the DAG of live objects Support many queries, par#cularly resource consump#on queries 9

10 Language implementafon Java XText JVMTI agent Our DSL JVMTI plugin in C JVM Applica#on Op*miza*on is the main issue in the implementa*on Avoid precompu#ng unneeded data by using variability Avoid traversing leaf nodes Reordering expressions Minimizing number of graph traversals 10

11 EvaluaFon Performance gain 1. Impact of Analysis on the Total Execu#on Time 2. Comparing Analysis Time for Simple Asser#on 3. Analysis Time in Real Scenarios Discussion on expressiveness 11

12 Impact of Analysis on the Total ExecuFon Time Very simple analysis Execute the analysis many #mes Use different analysis techniques 12

13 Comparing Analysis Time for Simple AsserFon 13

14 Analysis Time in Real Scenarios OSGi- based applica#ons Compute the memory consump#on of each component 14

15 Discussion on Language Expressiveness Our approach Cypher Easy to reason about its performance and expressiveness OQL enough 15

16 Conclusions and Future Works Tooling support for high- level abstrac#ons is incomplete. We lack tools to deal with maintenance tasks. We provide a DSL to define custom memory profilers which show low overhead Performance has been evaluated Expressiveness was discussed For a new DSL, is it possible to automate the genera#on of a profiler? 16

17 ? 17

OS/Run'me and Execu'on Time Produc'vity

OS/Run'me and Execu'on Time Produc'vity Ron Brightwell, Technical Manager Scalable System SoAware Department Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation,