TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis

Habib Saissi, Stefan Winter, Oliver Schwan, Karthik Pattabiraman, and Neeraj Suri, To appear in the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2020. (Acceptance Rate: 16.5%). [PDF | Talk] (Code)

Abstract: Modern computing systems typically relax execution determinism, for instance by allowing the CPU scheduler to inter- leave the execution of several threads. While beneficial for performance, execution non-determinism affects programs’ execution traces and hampers the comparability of repeated executions. We present TraceSanitizer, a novel approach for execution trace comparison in Error Propagation Analyses (EPA) of multi-threaded programs. TraceSanitizer can identify and compensate for non- determinisms caused either by dynamic memory allocation or by non-deterministic scheduling. We formulate a condition under which TraceSanitizer is guaranteed to achieve a 0% false positive rate and automate its verification using Satisfiability Modulo Theory (SMT) solving techniques. TraceSanitizer is comprehensively evaluated using execution traces from the PARSEC and Phoenix benchmarks. In contrast with other approaches, Trace- Sanitizer eliminates false positives without increasing the false negative rate (for a specific class of programs), with reasonable performance overheads.

Comments are closed.