No abstract

The design of the Georgia Tech Time Warp (GTW, version 2.0) executive for cache-coherent sharedmemory multiprocessors is described. The programmer's interface is presented. Several optimizations used to efficiently realize key functions such as event list manipulation, memory and buffer management, and message passing are discussed. An efficient algorithm for computing GVT on shared-memory multiprocessors is described. Measurements of a wireless personal communication services (PCS) network simulation indicate the GTW simulator is able to sustain performance as high as 335,000 committed events per second for this application on a 42-processor KSR-2 machine.18(4) ~423-434, August.

We present a cloning mechanism that enables the evaluation of multiple simulated futures. Performance of the mechanism is analyzed and evaluated experimentally on a shared memory multiprocessor. A running parallel discrete event simulation is dynamically cloned at decision points to explore different execution paths concurrently. In this way what-if and alternative scenario analysis can be performed in applications such as gaming or tactical and strategic battle management. A construct called virtual logical processes avoids repeating common computations among clones and improves efficiency. The advantages of cloning are preserved regardless of the number of clones (or execution paths). Our performance results with a commercial air traffic control simulation demonstrate that cloning can significantly reduce the time required to compute multiple alternate futures.

Global virtual time (GVT) is used in the Time Warp synchronization mechanism to perform irrevocable operations such as I/O and to reclaim storage. Most existing algorithms for computing GVT assume a message-passing programming model. Here, GVT computation is examined in the context of a shared-memory model. We observe that computation of GVT is much simpler in shared-memory multiprocessors because these machines normally guarantee that no two processors will observe a set of memory operations as occurring in different orders. Exploiting this fact, we propose an efficient, asynchronous, shared-memory GVT algorithm and prove its correctness. This algorithm does not require message acknowledgments, special GVT messages, or FIFO delivery of messages, and requires only a minimal number of shared variables and data structures. The algorithm only requires one round of interprocessor communication to compute GVT, in contrast to many message-based algorithms that require two. An efficient implementation is described that eliminates the need for a processor to explicitly compute a local minimum for time warp systems using a lowest-timestamp-first scheduling policy in each processor.In addition, we propose a new mechanism called on-the-fly fossil collection that enables efficient storage reclamation for simulations containing large numbers, e.g., hundreds of thousand or even millions of simulator objects. On-the-fly fossil collection can be used in time warp systems executing on either shared-memory or message-based machines. Performance measurements of the GVT algorithm and the on-the-fly fossil collection mechanism on a Kendall Square Research KSR-2 machine demonstrate that these techniques enable frequent GVT and fossil collections, e.g., every millisecond, without incurring a significant performance penalty.

Several fields have created ontologies for their subdomains. For example, the biological sciences have developed extensive ontologies such as the Gene Ontology, which is considered a great success. Ontologies could provide similar advantages to the Modeling and Simulation community. They provide a way to establish common vocabularies and capture knowledge about a particular domain with community-wide agreement. Ontologies can support significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved knowledge discovery capabilities. This paper discusses the design and development of an ontology for Modeling and Simulation called the Discrete-event Modeling Ontology (DeMO), and it presents prototype applications that demonstrate various uses and benefits that such an ontology may provide to the Modeling and Simulation community.