Relaxing causal constraints in PDES

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Discrete Event Simulation (DES) is a widely used technique in which the state of the simulator is updated by events happening at discrete points in time (hence the name). DES is used to model and analyze many kinds of systems, including computer architectures, communication networks, street traffic, and others. Parallel and Distributed Simulation (PADS) aims at improving the efficiency of DES by partitioning the simulation model across multiple processing elements, in order to enabling larger and/or more detailed studies to be carried out. The interest on PADS is increasing since the widespread availability of multicore processors and affordable high performance computing clusters. However, designing parallel simulation models requires considerable expertise, the result being that PADS techniques are not as widespread as they could be. In this paper we describe ErlangTW, a parallel simulation middleware based on the Time Warp synchronization protocol. ErlangTW is entirely written in Erlang, a concurrent, functional programming language specifically targeted at building distributed systems. We argue that writing parallel simulation models in Erlang is considerably easier than using conventional programming languages. Moreover, ErlangTW allows simulation models to be executed either on single-core, multicore and distributed computing architectures. We describe the design and prototype implementation of ErlangTW, and report some preliminary performance results on multicore and distributed architectures using the well known PHOLD benchmark.

Parallel Discrete Event simulation (PDEs) is an important technology for studying large-scale complex systems[1]. And there are some problems in the present PDEs modeling, such as poor intuitiveness, low efficiency, difficulty in expansion and reuse, insufficient support of modeling environment for model checking, difficulty in model checking and so on. For these problems, a parallel discrete event simulation modeling method, named Object Class Interaction Graph(OCIG), based on basic event graph and object-oriented thinking is proposed. It has the natural and simple advantages of event graph modeling for discrete event simulation, and it can well meet the requirements of scalable, easy assembly, and layered parallelism for parallel discrete event simulation applications. The article also uses the set theory to formally define it, laying the foundation for the subsequent formal checking of the model [2].

Parallel simulation has the potential to accelerate the execution of simulation applications. However developing a parallel discrete-event simulation from scratch requires an in-depth knowledge of the mapping process from the physical model to the simulation model, and a substantial effort in optimising performance. This paper presents an overview of the SPaDES (Structured Parallel Discrete-Event Simulation) parallel simulation framework. We focus on the performance analysis of SPaDES/C++, an implementation of SPaDES on a distributed-memory Fujitsu AP3000 parallel computer. SPaDES/C++ hides the underlying complex parallel simulation synchronization and parallel programming details from the simulationist. Our empirical results show that the SPaDES framework can deliver good speedup if the process granularity is properly optimised.

As network simulations scale to hundreds of thousands of nodes, parallel discrete event simulation (PDES) has become indispensable for sustaining performance—yet its efficacy hinges on frequent time synchronization steps. Existing synchronization algorithms suffer from the inter-machine communication overhead in distributed environments, eroding the benefits of parallelism. We observe that time synchronization in PDES often involves frequent allreduce operations, and that in-network computing has the potential to substantially accelerate such collectives. In this work, we present SINA (Synchronization using In-Network Allreduce)—the first integration of in-network computing into time synchronization to address the demands of large-scale simulations. We implemented SINA in a testbed with Mellanox SHArP-enabled switches and ConnectX-5 network cards, offloading allreduce to the network hardware while preserving software‑level correctness. Our evaluation shows that SINA achieves up to 88.6% acceleration compared to state‑of‑the‑art methods and achieves up to 67.5% optimization in topologies of tens of thousands of nodes, demonstrating its suitability for high‑performance, large‑scale parallel simulations.

Parallel discrete-event simulation (PDES) is an important tool in the codesign of extreme-scale systems because PDES provides a cost-effective way to evaluate designs of high-performance computing systems. Optimistic synchronization algorithms for PDES, such as Time Warp, allow events to be processed without global synchronization among the processing elements. A rollback mechanism is provided when events are processed out of timestamp order. Although optimistic synchronization protocols enable the scalability of large-scale PDES, the performance of the simulations must be tuned to reduce the number of rollbacks and provide an improved simulation runtime. To enable efficient large-scale optimistic simulations, one has to gain insight into the factors that affect the rollback behavior and simulation performance. We developed a tool for ROSS model developers that gives them detailed metrics on the performance of their large-scale optimistic simulations at varying levels of simulation granularity. Model developers can use this information for parameter tuning of optimistic simulations in order to achieve better runtime and fewer rollbacks. In this work, we instrument the ROSS optimistic PDES framework to gather detailed statistics about the simulation engine. We have also developed an interactive visualization interface that uses the data collected by the ROSS instrumentation to understand the underlying behavior of the simulation engine. The interface connects real time to virtual time in the simulation and provides the ability to view simulation data at different granularities. We demonstrate the usefulness of our framework by performing a visual analysis of the dragonfly network topology model provided by the CODES simulation framework built on top of ROSS. The instrumentation needs to minimize overhead in order to accurately collect data about the simulation performance. To ensure that the instrumentation does not introduce unnecessary overhead, we perform a scaling study that compares instrumented ROSS simulations with their noninstrumented counterparts in order to determine the amount of perturbation when running at different simulation scales.

The large size and complexity of the modern digital hardware impose great challenges to design and validation. Hardware Description Languages (HDLs) and System-Level Description Languages (SLDLs) rely on sequential discrete event semantics. Parallel Discrete Event Simulation (PDES) has recently gained extensive attention for parallelizing these languages due to the ever-increasing complexity of embedded and cyber physical systems. However, PDES application has not yet reached acceptable maturity and pervasiveness for accelerating computer architecture problems. This is due to inherent complexity of hardware components that require using different advanced PDES techniques. In this paper, we look at the main problem from a radically different angle. First, we suppose there is only a single universal discrete event model of computation for simulation purely defined by distributed optimistic PDES, i.e., logical-process-based event scheduling worldview. Second, we construct a new parallel system-level simulation language called OSML for ESL. Third, we propose a unified Cloud-based CAD tool called Troodon to automatically parallelize existing hardware languages atop OSML. To the best of our knowledge, OSML is the first work on optimistic synchronization applied to hardware-specific SLDLs and hardware models at different levels of abstraction in ESL, which contain complex data structures by proposing a hybrid checkpointing scheme.

Parallel discrete event simulation (PDES) is an important application in use in many DoD projects; for example, PDES is used in large-scale war-gaming, and in complex system design, analysis and verification. Improving PDES performance and capacity allows faster simulation times and more extensive analysis of more detailed models. These benefits are not application-specific: they should reflect to any application that uses the improved simulation kernel. In this work, we overview our efforts for optimizing PDES in a heterogeneous high performance computing (HHPC) environment. We profile the SPEEDES simulator and identify several opportunities. We report on our experiences on two fronts: (1) optimizing the communication subsystem - a critical system for PDES since it is a fine-grained application and (2) exploring the use of augmented FPGA boards to accelerate simulation. While such approaches have been attempted for sequential and data path intensive applications, we believe that their use in clustered environments is novel. Both efforts are works in progress; we present our designs and some preliminary analysis results. For example, removing the centralized communication server from event message exchange path with a number of other small improvements to the simulation cycle, improved performance by an average of 20% performance improvement for one of our large benchmarks.

Parallel discrete event simulation (PDES) decreases a simulation's runtime by splitting the simulation's work between multiple processors. Many users avoid PDES because it is difficult to specify a large and complicated model using existing PDES tools. In this paper we describe how the PARASOL PDES system uses migrating user level threads to support the process interaction world view. The process interaction world view is popular in sequential simulation languages and is a major departure from the logical process view supported by most PDES systems.

A number of algorithms have been developed to support parallel execution of discrete-event simulation models. In general, these algorithms are complex and implementing them directly in a simulation model is a difficult and resource-intensive programming task. Parallel simulation languages and environments can be of considerable help in hiding the complexity of the underlying synchronization algorithm and in providing a simpler virtual machine to the model designer. This paper is a survey of currently available software tools that facilitate the design of parallel discrete-event simulations.

A number of algorithms have been developed to support parallel execution of discrete-event simulation models. In general, these algorithms are complex and implementing them directly in a simulation model is a difficult and resource-intensive programming task. Parallel simulation languages and environments can be of considerable help in hiding the complexity of the underlying synchronization algorithm and in providing a simpler virtual machine to the model designer. This paper is a survey of currently available software tools that facilitate the design of parallel discrete-event simulations.

In contrast to traditional Parallel Discrete Event Simulation (PDES) systems using dedicated machines, an alternative master/worker approach for high throughput execution on shared computing platforms is proposed. Master/worker systems offer potential advantages over traditional PDES systems such as better support for heterogeneous machines, client-driven load balancing, fault tolerance, and execution on non-dedicated machines. We examine feasibility of executing PDES codes on master/worker systems. We characterise application properties that are important for efficient execution and define suitable performance metrics. The Aurora Parallel and Distributed Simulation System utilising the master/worker approach is described along with performance measurements in executing conservative PDES codes.

Research in parallel simulation has been around for more than two decades. However, the number of papers reporting on its application to real world problems is limited. At the 2002 PADS conference, researchers discussed the need to go beyond synchronization and performance issues, and, in particular, to demonstrate that parallel simulation could be used in real world applications outside military and network simulations. Since then, we have seen an increase in the number of papers on the parallel simulation applications in areas such as operations management and the physical sciences. This paper advocates the application of parallel simulation in population dynamics which is often used as the basis for policy planning and analysis. In this paper, we show how the simulation model is implemented using the μsik parallel simulation library. We conduct some experiments to measure the simulation performance. The result shows that good event parallelism can be achieved. 1

The optimal level of performance from parallel discrete-event simulation depends on the circuit being simulated, the vectors being simulated, and the machine on which the simulation is being performed. Empirical studies based on very simple models suggest that the amount of parallelism available in typical circuits is very small. A model of optimal performance for a machine with an infinite number of processors having uniform memory accesses is presented. It demonstrates that some circuits have significantly more parallelism than previously believed. The model is refined to define the optimal load partitioning for a machine with a finite number of processors with uniform access and extended to define the optimal static data partitioning. A metric is obtained which can be used to benchmark different models of parallel simulation. The effectiveness of these models in detecting performance problems of the version of RSIM running on the BBN Butterfly is shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The master/worker (MW) paradigm can be used to implement parallel discrete event simulations (PDES) on metacomputing systems. MW PDES applications incur overheads not found in conventional PDES executions executing on tightly coupled machines. We introduce four techniques for reducing these overheads on public resource and desktop grid infrastructures Work unit caching, pipelined state updates, expedited message delivery, and adaptive work unit scheduling mechanisms are described that provide significant reduction in overall overhead when used in tandem. We present performance results showing that an optimized MW PDES system can exhibit performance comparable to a traditional PDES system for a queueing network and a particle physics simulation.