Optimizing simulation on shared-memory platforms: The smart cities case

Modern advancements in computing architectures have been accompanied by new emergent paradigms to run Parallel Discrete Event Simulation 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 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 this paper, we show performance implications of this simulation-engine organization when the simulation models have a variable granularity. To this end, we have selected a traffic model, tailored for smart cities-oriented simulation. Our assessment illustrates the effects of the various tuning parameters related to the approach, opening to a higher understanding of this innovative paradigm

Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing

Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal

A Non-Blocking Priority Queue for the Pending Event Set

The large diffusion of shared-memory multi-core machines has impacted the way Parallel Discrete Event Simulation (PDES) engines are built. While they were originally conceived as data-partitioned platforms, where each thread is in charge of managing a subset of simulation objects, nowadays the trend is to shift towards share-everything settings. In this scenario, any thread can (in principle) take care of CPU-dispatching pending events bound to whichever simulation object, which helps to fully share the load across the available CPU-cores. Hence, a fundamental aspect to be tackled is to provide an efficient globally-shared pending events’ set from which multiple worker threads can concurrently extract events to be processed, and into which they can concurrently insert new produced events to be processed in the future. To cope with this aspect, we present the design and implementation of a concurrent non-blocking pending events’ set data structure, which can be seen as a variant of a classical calendar queue. Early experimental data collected with a synthetic stress test are reported, showing excellent scalability of our proposal on a machine equipped with 32 CPU-cores

A Conflict-Resilient Lock-Free Calendar Queue for Scalable Share-Everything PDES Platforms

Emerging share-everything Parallel Discrete Event Simulation (PDES) platforms rely on worker threads fully sharing the workload of events to be processed. These platforms require efficient event pool data structures enabling high concurrency of extraction/insertion operations. Non-blocking event pool algorithms are raising as promising solutions for this problem. However, the classical non-blocking paradigm leads concurrent conflicting operations, acting on a same portion of the event pool data structure, to abort and then retry. In this article we present a conflict-resilient non-blocking calendar queue that enables conflicting dequeue operations, concurrently attempting to extract the minimum element, to survive, thus improving the level of scalability of accesses to the hot portion of the data structure---namely the bucket to which the current locality of the events to be processed is bound. We have integrated our solution within an open source share-everything PDES platform and report the results of an experimental analysis of the proposed concurrent data structure compared to some literature solutions