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Compactly Representing Parallel Program Executions

Compactly Representing Parallel Program Executions. Ankit Goel Abhik Roychoudhury Tulika Mitra National University of Singapore. Path profiles. Profiling a program’s execution Count based Path based Count based profiles are more aggregate

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Compactly Representing Parallel Program Executions

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  1. Compactly Representing Parallel Program Executions Ankit Goel Abhik Roychoudhury Tulika Mitra National University of Singapore

  2. Path profiles • Profiling a program’s execution • Count based • Path based • Count based profiles are more aggregate • # of execution of the program’s basic blocks • # of accesses of various memory locations • Path based profiles are more accurate • Sequence of basic blocks executed • Sequence of memory locations accessed • Use Online compression to generate compact path profiles.

  3. Organization • Compressed Path Profiles in Sequential Programs • Parallel Program Path Profiles • Compression Efficiency and Overheads • Data race detection over path profiles

  4. Compressed Path - Example Uncompressed Path 123123 1 Compressed Representation S  AA A  123 2 3 Control Flow Graph

  5. Online Path Compression • A program path is a string over a finite alphabet • Alphabet decided by what we instrument • Control flow (Basic Blocks executed) • Data flow (Memory Locations accessed) • A string s is represented by a Context Free Grammar Gs: Language of Gs is {s} • Construction of Gs is online and not post-mortem • Start with trivial grammar & modify it for each symbol • No recursive rules (DAG representation) • Compression scheme – Nevill-Manning & Witten 97 • Application to program paths – Larus 99

  6. Online Compression in action Path Executed Compressed Representation 1 S -> 1 12 S -> 12 123 S -> 123 1231 S -> 1231 12312 S -> 12312 S -> A3A A -> 12

  7. Online Compression in action Path Executed Compressed Representation S -> A3A3 A -> 12 123123 S -> BB B -> A3 A -> 12 S -> BB B -> 123

  8. Organization • Compressed Path Profiles in Sequential Programs • Parallel Program Path Profiles • Compression Efficiency and Overheads • Data race detection over path profiles

  9. What to represent ? • Control/data flow in each program thread • Communication among threads • Synchronization (locks, barriers) • Unsynchronized shared variable accesses • Too costly to observe/record order of all shared variable accesses • We will represent • Compressed flow in each thread (via Grammar) • Communication via synchronizations (How ?)

  10. Synchronization Pattern (Locks) lock Compute Pgm = P1 || P2 unlock lock unlock Memory P1 P2 Message Sequence Chart (MSC)

  11. Synchronization Pattern (Barrier) ready Pgm = P1 || P2 Blocked ready go go Compute Compute P1 P2 Memory

  12. Connection to MSCs Partial Order of MSC • Matches Observed Ordering • Total order in each thread • Ordering across threads visible via synchronization (msg. exchange) unlock lock Th. 1 Th. 2 Shared Mem. All synchronization ops. form a total order

  13. A first cut • Instrument each thread to observe local control/data flow and global synch. • Represent path profile of P1 || P2 • Each thread’s flow as a Grammar – (G1, G2) • Contains synch. ops. as well. • All synchronization ops. as a list. • Associate entries in this list to the occurrence of synch. ops. in (G1,G2) • How to navigate the path profile ? • Zoom in to a specific lock—unlock segment of P1

  14. Edge annotations a b (lock) c (unlock) x b (lock) c (unlock) y S 4 0 2 2 y a A x 0 1 b c Grammar for one thread

  15. Locating synch. operations S 4 X 0 2 2 y n a A x Y } 0 1 b c n synch ops. Locating the 3rd synchronization operation Can find synch. segments by looking up global list.

  16. So far • Control flow of each thread stored as a grammar • Synchronization ops. form a global list • Grammar of each thread annotated with counts • Easy searching of synchronization operations • What about shared data accesses ? • Sequence of memory locations accessed by a single LD/ST instruction can be compressed • Use a Grammar representation for this seq. as well

  17. Further compression • Locations accessed by a memory operation • 10,14,18,22,26,54,58,62,66,70,98 • Online Compression of the string as grammar • 10(1), 4(4), 28(1), 4(4), 28(1) • Difference representation + Run-length encoding • Useful for detecting regularity of array accesses • Sweep through an array: A run of constant diffs. • Accessing a sub-grid of a multidimensional array

  18. Organization • Compressed Path Profiles in Sequential Programs • Parallel Program Path Profiles • Compression Efficiency and Overheads • Data race detection over path profiles

  19. Any better than gzip ? Compression % (2 Processors)

  20. Scalability of Compression Compression % for our scheme

  21. Concerns about Timing Overheads • Our scheme does not add substantial time overhead over grammar based string compression • Our experiments conducted using RSIM • Tracing overheads can be higher in a real multiprocessor • Can tracing distort program behavior ? • Possible solution • Trace minimal number of operations in a parallel program execution (Netzer 1993) to ensure deterministic replay • Collect compressed path profile during replay.

  22. Organization • Compressed Path Profiles in Sequential Programs • Parallel Program Path Profiles • Compression Efficiency and Overheads • Data race detection over path profiles

  23. Apparent Data races lock • Last unlock in Th. 1 (first unlock) • Next lock in Th. 1 (second lock) • Locate root-to-leaf paths of these ops. • Tree rooted at the least common ancestor of these ops. unlock lock unlock lock unlock lock unlock Th. 1 Th.2 Th.3 Mem. No Decompression of the grammar of Th. 1

  24. Data race artifacts Sub := 1 A[1] := 0 X := Sub; Y := A[X] (artifact) X decides which addr. is accessed in Y := A[X] X is set by Sub:= 1 which is also in a data race. Detecting artifacts requires Data-flow Not captured by rd/wr sets in synch. segments Captured in our compact path profiles.

  25. Summary • Compressed representation of the execution profile of shared memory parallel programs • Control and shared data flow per thread • Synchronization patterns across threads • Overall compression efficiency 0.25% -- 9.81% • Compression efficiency scalable with increasing number of processors • Application: Post-mortem debugging such as detecting data races

  26. Other Applications • We do not capture actual order of unsynchronized shared memory accesses across processors • Can be useful in making architectural decisions such as choice of cache coherence protocol • Sufficient to maintain [Netzer 1993] • transitive reduction of program order on each proc. • shared variable conflict orders • Can we capture transitive reduction relation via annotations of WPP edges?

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