Runtime Techniques for Efficient and Reliable Program Execution

1. Runtime Techniques for Efficientand ReliableProgram Execution Harry Xu CS 295 Winter 2012

2. Who Am I • Recently got my Ph.D. (in 08/11) • Interested in (static and dynamic) program analysis • Theoretical foundations (mathematical models) • Applications (e.g., compiler, performance tuning, verification, security, distributed computing, etc.) • Most recent interest--- software bloat analysis

3. Who Are You • Your name • Advisor • Research interests • What do you expect from the class

4. Program Analysis • Dynamic analysis v.s. static analysis

5. Analysis Dimensions • Analysis scope • Intraprocedural– focusing on each individual function • Interprocedural– considering calling structures • Context sensitivity • Context-sensitive– distinguishing different callers when analyzing each function • Context-insensitive– get a unified solution

6. Concerns • Precision • Requires higher context-sensitivity, finer-grained abstractions, etc. • Scalability • The opposite • Find the right balance • Combine static and dynamic analyses

7. Application Domains • Static analysis • Static compiler (e.g., type system and optimizations) • Verification tools – prove a program is “bug-free” • Dynamic analysis • (Dynamic) optimizing compiler (e.g., providing feedback) • Testing – find bugs in specific program runs • Performance tuning

8. This Class • Focus on dynamic analysis • Foundations • Various profiling techniques • Dynamic slicing • Calling context encoding • Applications • Memory leak detection • Software bloat detection • Finding bugs • Providing feedback in a dynamic compiler

9. This Class • A research seminar • Emphasize both • Fundamental technology (i.e., science) • Practical problems (i.e., engineering) • CS Research • Solving engineering problemswith scientific solutions

10. Foundations I – Profiling void main(String[] s){ A a = new A(…); for(…){ B b = new B(…); } } long totalObjs = 0; • A run-time technique that gathers execution information • E.g., total # objects created during the execution • How--- via program instrumentation totalObjs ++; totalObjs ++; print(totalObjs);

11. Foundations I – Profiling • Kinds • Path profiling (Week 1) • Calling context profiling (Week 4) • Dependence profiling (Week 5)

12. Foundations II – Dynamic Slicing void main(String[] s){ a.f = …; … c = b.f; } • Record all memory accesses and their dependence relationships • Applications • Automated debugging • Performance analysis 0x23456789

13. Foundations III – Context Profiling void m(…){ bar(new A()); //call 1 } void n(…){ bar (new B()); //call 2 } void bar(A a){ a.f = …; } • Record calling contexts for method invocations m n call 2 call 1 bar

14. Foundations III – Context Profiling void m(…){ bar(new A()); //call 1 } void n(…){ bar (new B());//call 2 } void bar(A a){ a.f = …; } • Record calling contexts for method invocations • Applications • Context-sensitive dynamic analysis • Interprocedural compiler optimization m n call 1 call 2 bar bar

15. Application I – Memory Leak Detection • C/C++ memory leaks • a = malloc (…), but no free(a) • Can be detected by both static and dynamic analyses • Memory leaks in managed programs(week 3 and 7) • Caused by unnecessary references • Itisundecidabletodetermineobjectliveness • Usedynamicanalysiswithvariousheuristics

16. Application II – Software Bloat Analysis • Inefficient run-time work and resource usage to achieve simple tasks • Surprisingly common • Supporting thousands of users (millions are expected) • Consequences for scalability, power usage, and performance • Week 3 and 8

17. Application III – Bug Detection • Use dynamic analysis to find functional bugs (Week 7 and 8) • We are not going to cover • Concurrency bug detection • Systematic testing • One security paper (DieHarder)

18. Application IV – Optimizing Compiler • Use dynamic analysis to provide feedback to direct optimizations (Week 10) • Dynamic analyses exist in almost all modern dynamic compilers

19. Grading Policy I • Paper critiques (15%) • Problem definition • Key insights/contributions • Weakness/flaws • Opportunities for future work • Your problems in understanding the paper • Due 6pm the day before the class • Presenters are exempt from writing critiques for papers they are presenting

20. Grading Policy II • Paper presentations (30%) • Two presentations on similar topics in one class • 30 mins for each • Important presentation skills • Focus on high-level ideas • Illustrate basic ideas using concrete examples and pictures • Do not copy algorithms/formulae/complex examples from the paper • Include interesting/non-trivial questions you want discuss in the class • Register for papers before Friday Jan 13

21. Grading Policy III • In-class discussion (15%) • Assume your audience has read the paper • Try to say non-obvious, interesting things • Depth is more important than breadth

22. Grading Policy IV • Projects (30%) • Either by yourself or with another student • Try to work on an ambitious project that may not work eventually, rather than a simple project that is guaranteed to work • It is a good idea to make it your own research project, rather than think of it as something to fulfill the class requirement • Let’s publish them!!!

23. Important Notice • Paper critiques for the first two papers are due 6pm Tuesday (1/10) • Paper selection due on Friday (1/13) • Any problem with the current class schedule?