Dynamic Partial Order Reduction Research Articles

Distributed dynamic partial order reduction

While stateless model checking avoids the memory blow-up problem by not recording the search history, runtime becomes a major limiting factor. In this paper, we present distributed dynamic partial order reduction (DDPOR) which can speed up stateless model checking using computer clusters, and get the benefit of dynamic partial order reduction (DPOR). The experiments show that DDPOR can give out nearly linear (with respect to the number of CPUs) speedup on realistic multithreaded programs, comparing with sequential stateless model checking that uses DPOR.

Full simulation coverage for SystemC transaction-level models of systems-on-a-chip

Transaction-Level Models (TLM) are used for the early validation of embedded software. A TL model is a virtual prototype of the hardware part of a System-on-a-Chip (SoC). When using SystemC for transaction level modeling, the main parallel entities of the hardware platform (processors, DMAs, bus arbiters, etc.) are modeled by asynchronous processes, which are scheduled at simulation time. The specification of this scheduling mechanism is non-deterministic; the set of all possible schedulings of the parallel activities represents the physical parallelism faithfully. Moreover TL models may contain loose timing annotations (intervals for instance), and the set of all possible values of time in these intervals is also meant to represent the hardware behaviors faithfully. However, any simulation engine is built on a deterministic scheduler, and at runtime will use specific values in the time intervals. This means that only a very small subset of all the possible schedulings and timings are exhibited during simulation. Some bugs may be missed if they are due to some behaviors of the hardware that are represented by other schedulings or timings. For a given finite test scenario, the set of valid schedulings and timings of a model is finite, but far too large to be explored fully. We present a solution to cover the set of schedulings and timings efficiently. Our solution is based on dynamic partial order reduction and constraint solving techniques. It gives a complete scheduling and timing set, which guarantees the detection of all local errors and deadlocks for a fixed test scenario.

Dynamic partial-order reduction for model checking software

We present a new approach to partial-order reduction for model checking software. This approach is based on initially exploring an arbitrary interleaving of the various concurrent processes/threads, and dynamically tracking interactions between these to identify backtracking points where alternative paths in the state space need to be explored. We present examples of multi-threaded programs where our new dynamic partial-order reduction technique significantly reduces the search space, even though traditional partial-order algorithms are helpless.