%0 Journal Article %1 Aumayr:2021:AgnosticRR %A Aumayr, Dominik %A Marr, Stefan %A Kaleba, Sophie %A Gonzalez Boix, Elisa %A Mössenböck, Hanspeter %B Programming %D 2021 %I AOSA Inc. %J The Art, Science, and Engineering of Programming %K Actors CSP Concurrency MeMyPublication MultiParadigm MultiThreading Nondeterministic RecordReplay SOMns STM myown %N 3 %P 39 %R 10.22152/programming-journal.org/2021/5/14 %T Capturing High-level Nondeterminism in Concurrent Programs for Practical Concurrency Model Agnostic Record & Replay %V 5 %X With concurrency being integral to most software systems, developers combine high-level concurrency models in the same application to tackle each problem with appropriate abstractions. While languages and libraries offer a wide range of concurrency models, debugging support for applications that combine them has not yet gained much attention. Record & replay aids debugging by deterministically reproducing recorded bugs, but is typically designed for a single concurrency model only. This paper proposes a practical concurrency-model-agnostic record & replay approach for multi-paradigm concurrent programs, i.e. applications that combine concurrency models. Our approach traces high-level non- deterministic events by using a uniform model-agnostic trace format and infrastructure. This enables ordering- based record & replay support for a wide range of concurrency models, and thereby enables debugging of applications that combine them. In addition, it allows language implementors to add new concurrency mod- els and reuse the model-agnostic record & replay support. We argue that a concurrency-model-agnostic record & replay is practical and enables advanced debugging support for a wide range of concurrency models. The evaluation shows that our approach is expressive and flexible enough to support record & replay of applications using threads & locks, communicating event loops, communicating sequential processes, software transactional memory and combinations of those concurrency models. For the actor model, we reach recording performance competitive with an optimized special-purpose record & replay solution. The average recording overhead on the Savina actor benchmark suite is 10% (min. 0%, max. 23%). The performance for other concurrency models and combinations thereof is at a similar level. We believe our concurrency-model-agnostic approach helps developers of applications that mix and match concurrency models. We hope that this substrate inspires new tools and languages making building and maintaining of multi-paradigm concurrent applications simpler and safer.

@article{Aumayr:2021:AgnosticRR, abstract = {With concurrency being integral to most software systems, developers combine high-level concurrency models in the same application to tackle each problem with appropriate abstractions. While languages and libraries offer a wide range of concurrency models, debugging support for applications that combine them has not yet gained much attention. Record & replay aids debugging by deterministically reproducing recorded bugs, but is typically designed for a single concurrency model only. This paper proposes a practical concurrency-model-agnostic record & replay approach for multi-paradigm concurrent programs, i.e. applications that combine concurrency models. Our approach traces high-level non- deterministic events by using a uniform model-agnostic trace format and infrastructure. This enables ordering- based record & replay support for a wide range of concurrency models, and thereby enables debugging of applications that combine them. In addition, it allows language implementors to add new concurrency mod- els and reuse the model-agnostic record & replay support. We argue that a concurrency-model-agnostic record & replay is practical and enables advanced debugging support for a wide range of concurrency models. The evaluation shows that our approach is expressive and flexible enough to support record & replay of applications using threads & locks, communicating event loops, communicating sequential processes, software transactional memory and combinations of those concurrency models. For the actor model, we reach recording performance competitive with an optimized special-purpose record & replay solution. The average recording overhead on the Savina actor benchmark suite is 10% (min. 0%, max. 23%). The performance for other concurrency models and combinations thereof is at a similar level. We believe our concurrency-model-agnostic approach helps developers of applications that mix and match concurrency models. We hope that this substrate inspires new tools and languages making building and maintaining of multi-paradigm concurrent applications simpler and safer.}, added-at = {2021-02-22T15:36:29.000+0100}, author = {Aumayr, Dominik and Marr, Stefan and Kaleba, Sophie and Gonzalez Boix, Elisa and Mössenböck, Hanspeter}, biburl = {https://www.bibsonomy.org/bibtex/27f5fea5fbd29e83e9aa50ee5e937b6ac/gron}, day = 28, doi = {10.22152/programming-journal.org/2021/5/14}, interhash = {dd132fe351941768d8e358b78400c776}, intrahash = {7f5fea5fbd29e83e9aa50ee5e937b6ac}, issn = {2473-7321}, journal = {The Art, Science, and Engineering of Programming}, keywords = {Actors CSP Concurrency MeMyPublication MultiParadigm MultiThreading Nondeterministic RecordReplay SOMns STM myown}, month = {February}, number = 3, pages = 39, pdf = {https://stefan-marr.de/downloads/prog21-aumayr-et-al-capturing-high-level-nondeterminism-in-concurrent-programs-for-practical-concurrency-model-agnostic-record-replay.pdf}, publisher = {AOSA Inc.}, series = {Programming}, timestamp = {2021-02-22T15:47:05.000+0100}, title = {{Capturing High-level Nondeterminism in Concurrent Programs for Practical Concurrency Model Agnostic Record & Replay}}, volume = 5, year = 2021 }