Going through Rough Times: from Non-Equilibrium Surface Growth to Algorithmic Scalability

Efficient and faithful parallel simulation of large asynchronous systems is a challenging computational problem. It requires using the concept of local simulated times and a synchronization scheme. We study the scalability of massively parallel algorithms for discrete-event simulations which employ conservative synchronization to enforce causality. We do this by looking at the simulated time horizon as a complex evolving system, and we identify its universal characteristics. We find that the time horizon for the conservative parallel discrete-event simulation scheme exhibits Kardar-Parisi-Zhang-like kinetic roughening. This implies that the algorithm is asymptotically scalable in the sense that the average progress rate of the simulation approaches a non-zero constant. It also implies, however, that there are diverging memory requirements associated with such schemes.Comment: to appear in the Proceedings of the MRS, Fall 200

Do System Test Cases Grow Old?

Companies increasingly use either manual or automated system testing to ensure the quality of their software products. As a system evolves and is extended with new features the test suite also typically grows as new test cases are added. To ensure software quality throughout this process the test suite is continously executed, often on a daily basis. It seems likely that newly added tests would be more likely to fail than older tests but this has not been investigated in any detail on large-scale, industrial software systems. Also it is not clear which methods should be used to conduct such an analysis. This paper proposes three main concepts that can be used to investigate aging effects in the use and failure behavior of system test cases: test case activation curves, test case hazard curves, and test case half-life. To evaluate these concepts and the type of analysis they enable we apply them on an industrial software system containing more than one million lines of code. The data sets comes from a total of 1,620 system test cases executed a total of more than half a million times over a time period of two and a half years. For the investigated system we find that system test cases stay active as they age but really do grow old; they go through an infant mortality phase with higher failure rates which then decline over time. The test case half-life is between 5 to 12 months for the two studied data sets.Comment: Updated with nicer figs without border around the

Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing

Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal

Efficient and Effective Event Pattern Management

The goal of this thesis is to reduce the barriers stopping more enterprises from accessing CEP technology by providing additional support in managing relevant business situations. Therefore we outline the role of event pattern management and present a methodology, methods and tools aiming at an efficient and effective event pattern management. We provide a meta model for event patterns, an event pattern life cycle methodology, methods for guidance, refinement and evolution