scholarly journals Optimal Dynamic Partial Order Reduction with Observers

2018 ◽  
pp. 229-248 ◽  
Author(s):  
Stavros Aronis ◽  
Bengt Jonsson ◽  
Magnus Lång ◽  
Konstantinos Sagonas
Keyword(s):  
Partial Order ◽  
Order Reduction ◽  
Partial Order Reduction ◽  
Optimal Dynamic

scholarly journals Optimal dynamic partial order reduction

2014 ◽  
Vol 49 (1) ◽  
pp. 373-384 ◽  
Author(s):  
Parosh Abdulla ◽  
Stavros Aronis ◽  
Bengt Jonsson ◽  
Konstantinos Sagonas
Keyword(s):  
Partial Order ◽  
Order Reduction ◽  
Partial Order Reduction ◽  
Optimal Dynamic

scholarly journals Truly stateless, optimal dynamic partial order reduction

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-28
Author(s):  
Michalis Kokologiannakis ◽  
Iason Marmanis ◽  
Vladimir Gladstein ◽  
Viktor Vafeiadis
Keyword(s):  
Partial Order ◽  
Equivalence Class ◽  
Order Reduction ◽  
Concurrent Programs ◽  
Partial Order Reduction ◽  
Sequential Consistency ◽  
Memory Consumption ◽  
Optimal Dynamic ◽  
Shared State ◽  
Trace Equivalence

Dynamic partial order reduction (DPOR) verifies concurrent programs by exploring all their interleavings up to some equivalence relation, such as the Mazurkiewicz trace equivalence. Doing so involves a complex trade-off between space and time. Existing DPOR algorithms are either exploration-optimal (i.e., explore exactly only interleaving per equivalence class) but may use exponential memory in the size of the program, or maintain polynomial memory consumption but potentially explore exponentially many redundant interleavings. In this paper, we show that it is possible to have the best of both worlds: exploring exactly one interleaving per equivalence class with linear memory consumption. Our algorithm, TruSt, formalized in Coq, is applicable not only to sequential consistency, but also to any weak memory model that satisfies a few basic assumptions, including TSO, PSO, and RC11. In addition, TruSt is embarrassingly parallelizable: its different exploration options have no shared state, and can therefore be explored completely in parallel. Consequently, TruSt outperforms the state-of-the-art in terms of memory and/or time.

Dynamic partial order reduction for relaxed memory models

2015 ◽  
Vol 50 (6) ◽  
pp. 250-259 ◽  
Author(s):  
Naling Zhang ◽  
Markus Kusano ◽  
Chao Wang
Keyword(s):  
Partial Order ◽  
Order Reduction ◽  
Memory Models ◽  
Partial Order Reduction ◽  
Relaxed Memory

Partial-Order Reduction for Supervisory Controller Synthesis

2021 ◽  
pp. 1-1
Author(s):  
Bram Vandersanden ◽  
Marc Geilen ◽  
Michel Reniers ◽  
Twan Basten
Keyword(s):  
Partial Order ◽  
Order Reduction ◽  
Controller Synthesis ◽  
Partial Order Reduction ◽  
Supervisory Controller
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