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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Trace Sanitizer
T2 - IEEE/IFIP International Conference on Dependable Systems and Networks
AU - Saissi, Habib
AU - Winter, Stefan
AU - Schwahn, Oliver
AU - Pattabiraman, K.
AU - Suri, Neeraj
N1 - Conference code: 50
PY - 2020/6/23
Y1 - 2020/6/23
N2 - 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.
AB - 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.
U2 - 10.1109/DSN48063.2020.00025
DO - 10.1109/DSN48063.2020.00025
M3 - Conference contribution/Paper
SN - 9781728158105
BT - 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
PB - IEEE
Y2 - 29 June 2020 through 2 July 2020
ER -