Strong reasons exist for executing a large-scale discrete-event simulation on a cluster of processor nodes (each of which may be a shared-memory multiprocessor or a uniprocessor). This is the architecture of the largest scale parallel machines, and so the largest simulation problems can only be solved this way. It is a common architecture even in less esoteric settings, and is suitable for memory-bound simulations. This paper describes our approach to porting the SSF simulation kernel to this architecture, using the Message Passing Interface (MPI) system. The notable feature of this transformation is to support an efficient two-level synchronization and communication scheme that addresses cost discrepancies between shared-memory and distributed memory. In the initial implementation, we use a globally synchronous approach between distributed-memory nodes, and an asynchronous shared-memory approach within a SMP cluster. The SSF API reflects inherently shared-memory assumptions; we report therefore on our approach for porting an SSF kernel to a cluster of SMP nodes. Experimental results on two architectures are described, for a model of TCP/IP traffic flows over a hierarchical network. The performance on a distributed network of commodity SMPs connected through ethernet is seen to frequently exceed performance on a Sun shared-memory multiprocessor.
在处理器节点的集群(每个节点可能是共享内存多处理器或单处理器)上执行大规模离散事件模拟的原因很深。这是最大规模的并行机的体系结构,因此只能通过这种方式解决最大的仿真问题。即使是在较深奥的设置中,它也是一种通用的体系结构,并且适合于内存受限的仿真。本文介绍了使用消息传递接口(MPI)系统将SSF仿真内核移植到此体系结构的方法。此转换的显着特征是支持有效的两级同步和通信方案,该方案可解决共享内存和分布式内存之间的成本差异。在最初的实现中,我们在分布式内存节点之间使用全局同步方法,并在SMP群集中使用异步共享内存方法。 I> SSF API反映了固有的共享内存假设;因此,我们报告了将 I> SSF 内核移植到SMP节点集群的方法。描述了两种体系结构上的实验结果,用于通过分层网络的TCP / IP流量模型。通过以太网连接的商品SMP的分布式网络上的性能通常被认为超过了Sun共享内存多处理器上的性能。 P>
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