INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

A Scalable GVT Estimation Algorithm for PDES: Using Lower Bound of Event-Bulk-Time

Global Virtual Time computation of Parallel Discrete Event Simulation is crucial for conducting fossil collection and detecting the termination of simulation. The triggering condition of GVT computation in typical approaches is generally based on the wall-clock time or logical time intervals. However, the GVT value depends on the timestamps of events rather than the wall-clock time or logical time intervals. Therefore, it is difficult for the existing approaches to select appropriate time intervals to compute the GVT value. In this study, we propose a scalable GVT estimation algorithm based on Lower Bound of Event-Bulk-Time, which triggers the computation of the GVT value according to the number of processed events. In order to calculate the number of transient messages, our algorithm employs Event-Bulk to record the messages sent and received by Logical Processes. To eliminate the performance bottleneck, we adopt an overlapping computation approach to distribute the workload of GVT computation to all worker-threads. We compare our algorithm with the fast asynchronous GVT algorithm using PHOLD benchmark on the shared memory machine. Experimental results indicate that our algorithm has a light overhead and shows higher speedup and accuracy of GVT computation than the fast asynchronous GVT algorithm

Virtual time-aware virtual machine systems

Discrete dynamic system models that track, maintain, utilize, and evolve virtual time are referred to as virtual time systems (VTS). The realization of VTS using virtual machine (VM) technology offers several benefits including fidelity, scalability, interoperability, fault tolerance and load balancing. The usage of VTS with VMs appears in two ways: (a) VMs within VTS, and (b) VTS over VMs. The former is prevalent in high-fidelity cyber infrastructure simulations and cyber-physical system simulations, wherein VMs form a crucial component of VTS. The latter appears in the popular Cloud computing services, where VMs are offered as computing commodities and the VTS utilizes VMs as parallel execution platforms. Prior to our work presented here, the simulation community using VM within VTS (specifically, cyber infrastructure simulations) had little awareness of the existence of a fundamental virtual time-ordering problem. The correctness problem was largely unnoticed and unaddressed because of the unrecognized effects of fair-share multiplexing of VMs to realize virtual time evolution of VMs within VTS. The dissertation research reported here demonstrated the latent incorrectness of existing methods, defined key correctness benchmarks, quantitatively measured the incorrectness, proposed and implemented novel algorithms to overcome incorrectness, and optimized the solutions to execute without a performance penalty. In fact our novel, correctness-enforcing design yields better runtime performance than the traditional (incorrect) methods. Similarly, the VTS execution over VM platforms such as Cloud computing services incurs large performance degradation, which was not known until our research uncovered the fundamental mismatch between the scheduling needs of VTS execution and those of traditional parallel workloads. Consequently, we designed a novel VTS-aware hypervisor scheduler and showed significant performance gains in VTS execution over VM platforms. Prior to our work, the performance concern of VTS over VM was largely unaddressed due to the absence of an understanding of execution policy mismatch between VMs and VTS applications. VTS follows virtual-time order execution whereas the conventional VM execution follows fair-share policy. Our research quantitatively uncovered the exact cause of poor performance of VTS in VM platforms. Moreover, we proposed and implemented a novel virtual time-aware execution methodology that relieves the degradation and provides over an order of magnitude faster execution than the traditional virtual time-unaware execution.Ph.D