In the last few years there have been several important advances in the understanding of cerebrovascular disorder pathophysiology that have impacted on stroke management. The development of timely and effective treatment strategies was and is still considered a high priority issue. Therapeutic options dramatically increased both in the prevention and overall in the treatment of acute ischaemic stroke (AIS). At present, whereas neuroprotection remains experimental, intravenous (i.v.) thrombolysis is the only specific therapy effective in reducing mortality and disability associated with stroke. The efficacy and safety of the antithrombotic therapy in AIS treatment are not well established, and few issues in clinical stroke management are more controversial. However, some studies have brought new light and new doubts on the roles of these traditional therapies.
The large diffusion of multi-core machines has pushed the research in the field of Parallel Discrete Event Simulation (PDES) toward new programming paradigms, based on the exploitation of shared memory. On the opposite side, the advent of Cloud computing—and the possibility to group together many (low-cost) virtual machines to form a distributed memory cluster capable of hosting simulation applications—has raised the need to bridge shared memory programming and seamless distributed execution. In this article, we present the design of a distributed middleware that transparently allows a PDES application coded for shared memory systems to run on clusters of (Cloud) resources. Our middleware is based on a synchronization protocol called Event and Cross State Synchronization. It allows cross-simulation-object access by event handlers, thus representing a powerful tool for the development of various types of PDES applications. We also provide data for an experimental assessment of our middleware architecture, which has been integrated into the open source ROOT-Sim speculative PDES platform.
Along the years, Parallel Discrete Event Simulation (PDES) has been enriched with programming facilities to bypass state disjointness across the concurrent Logical Processes (LPs). New supports have been proposed, offering the programmer approaches alternative to message passing to code complex LPs' relations. Along this path we find Event & Cross-State (ECS), which allows writing event handlers which can perform in-place accesses to the state of any LP, by simply relying on pointers. This programming model has been shipped with a runtime support enabling concurrent speculative execution of LPs limited to shared-memory machines. In this paper, we present the design of a middleware layer that allows ECS to be ported to distributed-memory clusters of machines. A core application of our middleware is to let ECS-coded models be hosted on top of (low-cost) resources from the Cloud. Overall, ECS-coded models no longer demand for powerful shared-memory machines to execute in reasonable time. Thanks to our solution, we retain indeed the possibility to rely on the enriched ECS programming model while still enabling deployments of PDES models on convenient (Cloudbased) infrastructures. An experimental assessment of our proposal is also provided. CCS CONCEPTS • Computing methodologies → Discrete-event simulation; • Theory of computation → Shared memory algorithms; • Software and its engineering → Distributed memory;
A rollback operation in a speculative parallel discrete event simulator has traditionally targeted the perfect reconstruction of the state to be restored after a timestamp-order violation. This imposes that the rollback support entails specific capabilities and consequently pays given costs. In this article we propose approximated rollbacks, which allow a simulation object to perfectly realign its virtual time to the timestamp of the state to be restored, but lead the reconstructed state to be an approximation of what it should really be. The advantage is an important reduction of the cost for managing the state restore task in a rollback phase, as well as for managing the activities (i.e. state saving) that actually enable rollbacks to be executed. Our proposal is suited for stochastic simulations, and explores a tradeoff between the statistical representativeness of the outcome of the simulation run and the execution performance. We provide mechanisms that enable the application programmer to control this tradeoff, as well as simulation-platform level mechanisms that constitute the basis for managing approximate rollbacks in general simulation scenarios. A study on the aforementioned tradeoff is also presented.
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