1. Static Data Race detection for Concurrent Programs with Asynchronous Calls Presenter: M. Amin Alipour Software Design Laboratory http://asd.cs.mtu.edu malipour@mtu.edu

2. Outline Introduction Problem Statement Main Contribution Solution Experimental Results

3. Introduction Data Race: – When there is multiple threads access a shared data and at least one of them write.

4. Static Data Race Detection Given source code of a program determine the possible places that race may occur.

5. Detection Criteria – Soundness Preserving every real data race – Accuracy Keeping the bogus warning low – Scalability Being efficient in large programs

6. Subtleties with Static Detection Asynchronous Function Call Recursion Accuracy int h1 (int x) { return x * x; } int h2 (int y) { return y + y;} struct funcType { int (*func) (int); } void f (int x, funcType *g, int *z) { l2: if ( x > 0) { *z = *(g->func) (x); } l3: else { *z = *(g->func) (-x); } } int main () { struct funcType ft; int a, b, p1, p2, z; struct thread t1, t2;.... if (p1) { ft.func = &h1; l0: fork (t1, f, a, ft, &z); join (t1); }... if (p2) { ft.func = &h2; l1: fork (t2, f, b, ft, &z); join (t2);

7. Main Contribution An improvement to – Emami, M., Ghiya, R., and Hendren, L. J. Context-sensitive interprocedural points-to analysis in the presence of function pointers. In Proceedings of the ACM SIGPLAN 1994 Conference on Programming Language Design and Implementation.

8. Classical Approach 1- Identify shared variables 2- Enumerate control location 3- Determine lockset ( set of locks held on all accesses to data at the location ) 4- Determine possible race locations, by lockset analysis ( Empty lockset indicates possible data races ) 5- Prune results in Step 4

9. Concurrent Control Flow Graph

10. Example Emami and et al’s CCFG Unrolled CCFG

11. Strategy to explore CCFG Unroll CCFG until no new data flow tuple D=(A,L) visited. – A is set of aliases – L is Lockset Would CCFG be finite? – Yes, the worst case is O(|F||P|2 |P| 2 |L| ) |F| number of functions |P| number of functions cardinality of Steensgaard’s pointers set |L| set of locks

12. Assumptions The algorithm uses Steensgard’s algorithm for points-to analysis, which results possible pointers that may point to same objects (variable or function) The algorithm assumes that number of statements affecting locks,functions and threads are small.

14. Pruning the result It uses a thread order analysis to remove any warning wherein the program location pairs that do not occur.

15. Pruning algorithm

16. Concurrent call graph int h1 (int x) { return x * x; } int h2 (int y) { return y + y;} struct funcType { int (*func) (int); } void f (int x, funcType *g, int *z) { l2: if ( x > 0) { *z = *(g->func) (x); } l3: else { *z = *(g->func) (-x); } } int main () { struct funcType ft; int a, b, p1, p2, z; struct thread t1, t2;.... if (p1) { ft.func = &h1; l0: fork (t1, f, a, ft, &z); join (t1); }... if (p2) { ft.func = &h2; l1: fork (t2, f, b, ft, &z); join (t2);

17. Experimental Results

18. References . V. Kahlon, N, Sinha, Y. Zhang and E. Kruus. “Static Data Race Detection for Concurrent Programs Asynchronous Calls” The 7th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE), Amsterdam, The Netherlands. August 2009.