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By 林泽燕

By 林泽燕. Program trace A program trace lists the addresses of instructions executed and data referenced during a program‘s execution by injecting code.

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By 林泽燕

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  1. By 林泽燕

  2. Program trace • A program tracelists the addresses of instructions executed and data referenced during a program‘s execution by injecting code. • Application areas: program comprehension, debugging, bug reproduction, test case generation, fault localization, verification, and fault repair. • Problem: run-time overhead during the execution

  3. Solution • A framework for distributed trace collection • a witness set->a profile->execute once->a partial path • The first approach for parallel trace collection

  4. Realizable distributed trace collection

  5. Realizable distributed trace collection （a diamond has more than two distinct edges and contains exactly one fork and one join.）

  6. Effective parallel distribution • The time for parallel trace collection depends on the maximum time taken by any machine. • the cost of a profile->the cost to cover the diamonds->the number of witness hits by the actual run->the total weight of the witnesses

  7. Important findings • For effective time for collection, each machine contains only a single profile if the size of profile is unbounded. • Combining multiple diamonds in the same profile can reduce the total witness weights to cover the diamonds. • Fine grained weight distribution facilitates even distribution of total weights across machines. {{b3,b4},{b6,b7}} VS {{b3,b4,b6}} 6 units with weights 1,2,3,4,5,6 {{1},{2},{3},{4},{5},{6}} VS {{1}, {2, 3}, {4, 5, 6}}

  8. 3 distribution strategies • Fine-grained distribution strategy: forms each job as a single diamond or cycle-without-diamond • Function-wise grouping strategy: forms each job by combining all diamonds and directed-cycles-without-diamond of a single function • Optimized-grouping strategy: uses heuristics to find potentially witness-optimizablediamonds and forms jobs by combining only such diamonds.

  9. The overall algorithm for profile distribution

  10. The overall algorithm for profile distribution • A spanning tree of a connected graph G can be defined as a maximal set of edges of G that contains no cycle, or as a minimal set of edges that connect all vertices.

  11. Path reconstruction from partial paths

  12. Experimental results

  13. Experimental results 1.Cost of collection vs. Machine • using 6 machines the trace collection time reduces to less than 1/3.

  14. Experimental results 2.Comparison of distribution strategies • Optimized strategy is always better than the function-wise grouping strategy • For benchmarks with less number of functions show better results with fine grained distribution strategy • Grouping strategies are more effective in reducing weights of profiles when multiple diamonds are in cycles. Absence of the cycles makes fine-grained strategy more effective than the two grouping strategies

  15. Experimental results 3.Effect of bounded profile size • with increase in limit, in most cases the cost decrease, with an exception in few cases. • Exception: In these cases the diamonds in each CFG are not witness-optimizable, thus combining them increases the profile size and cost.

  16. ~~The END

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