Ricerc@Sapienza

Modern advancements in computing architectures have been accompanied by new emergent paradigms to run Parallel Discrete Event Simulation (PDES) models efficiently. Indeed, many new paradigms to effectively use the available underlying hardware have been proposed in the literature. Among these, the Share-Everything paradigm, where the simulation state and the pending event queue are fully-shared among all the worker threads, tackles massively-parallel shared-memory machines, in order to support speculative simulation by taking into account the limits and benefits related to this family of architectures. Previous results have shown how this paradigm outperforms traditional speculative strategies (such as data-separated Time Warp systems) whenever the granularity of executed events is small. In particular, this paradigm has shown to benefit from the use of non-blocking algorithms. However, recent works have shown that as soon as the event¿s granularity becomes too large and/or the level of parallelism is increased, this paradigm is not more able to efficiently scale, a problem linked to the latency in memory accesses and the way in which the processed unsafe events are managed.

In this research project, we aim to design and implement a lightweight huge-scalable share-everything speculative PDES engine able to progress in the simulation independently by the amount of unsafe processed events. Among all, in order to purse scalability, particular attention will be placed facing NUMA architecture, then reducing latencies in memory management.

During this project, the non-blocking property will be seen as a vertical attribute of the engine. Moreover, a strong focus will be posed on reducing (both explicit and implicit) synchronization, in order to increase the efficient use of the underlying hardware.