Hierarchical Adaptive State Space Caching Based on Level Sampling

Abstract

Abstract. In the past, several attempts have been made to deal with the state space explosion problem by equipping a depth-first search (DFS) algorithm with a state cache, or by avoiding collision detection, thereby keeping the state hash table at a fixed size. Most of these attempts are tailored specifically for DFS, and are often not guaranteed to terminate and/or to exhaustively visit all the states. In this paper, we propose a general framework of hierarchical caches which can also be used by breadth-first searches (BFS). Our method, based on an adequate sampling of BFS levels during the traversal, guarantees that the BFS terminates and traverses all transitions of the state space. We define several (static or adaptive) configurations of hierarchical caches and we study experimentally their effectiveness on benchmark examples of state spaces and on several communication protocols, using a generic implementation of the cache framework that we developed within the CADP toolbox.

TESTOR: A Modular Tool for On-the-Fly Conformance Test Case Generation

“…We plan to experiment with state space caching techniques [33] and with other on-the-fly reductions to accelerate CTG generation in TESTOR. We also plan to investigate how to facilitate the description of test purposes, by deriving them from the action-based, branching-time temporal properties of the model (following the results of [13] in the state-based, linear-time setting) or by synthesizing them according to behavioral coverage criteria.…”

Many-core on-the-fly model checking of safety properties using GPUs

“…Research has shown that in state space exploration, due to the characteristics of most networks, there is a strong sense of locality, i.e. in each search iteration, the set of new state vectors is relatively small, and most of the already visited vectors have been visited about two iterations earlier [28,36]. This allows effective use of block local state caches in shared memory.…”

GPUexplore: Many-Core On-the-Fly State Space Exploration Using GPUs

“…Research has shown that in state space exploration, due to the characteristics of most networks, there is a strong sense of locality, i.e. in each search iteration, the set of new state vectors is relatively small, and most of the already visited vectors have been visited about two iterations earlier [18,19]. This allows effective use of block local state caches in shared memory.…”

“…If the vector is not old, then it is attempted to insert it in the bucket (lines [15][16][17][18][19][20][21][22][23]. This is done by the warp leader (WarpTId = 0, line 18), by performing a CAS.…”