Runtime Techniques for Efficient and Reliable Program Execution Harry Xu CS 295 Winter 2012

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

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

Program Analysis Dynamic analysis v.s. static analysis Static analysisDynamic analysis Analyze static codeAnalyze running program Find problems in any executionFind problems in some real execution Sound: no false negatives*Unsound: false negatives Imprecise: false positivesPrecise: often no false positives Doesnt slow running programSlows executing program

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

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

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

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

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

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

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

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

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

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

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 – It is undecidable to determine object liveness – Use dynamic analysis with various heuristics

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

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)

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

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

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

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

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 – Lets publish them!!!

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?