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This work presents a new trace-based parallel discrete event simulation framework designed for predicting the behavior of a novel computing platform running energy-aware parallel applications. Discrete event traces capture the runtime behavior of parallel applications on existing systems and form the basis for the simulation. The simulation framework processes the events of the input trace by applying simulation models that modify event properties. Thus, the output are again event traces that describe the predicted application behavior on the simulated target platform. Both input and simulated traces can be visualized and analyzed with established tools. The modular design of the framework enables the simulation of different aspects such as temporal performance and energy efficiency by applying distinct simulation models e.g.: (i) A performance model for communication that allows to evaluate the target communication topology and link properties. (ii) An energy model for computations that is based on measurements of current hardware. We showcase the potential of this simulation by simulating the execution of benchmark applications to explore design alternatives of highly adaptive and energy-efficient computing applications and platforms.

The objective of the German BMBF research project Highly Efficient Implementation of CFD Codes for HPC Many-Core Architectures (HICFD) is to develop new methods and tools for the analysis and optimization of the performance of parallel computational fluid dynamics (CFD) codes on high performance computer systems with many-core processors. In the work packages of the project it is investigated how the performance of parallel CFD codes written in C can be in

The utility of simulations depends on the confidence in the simulation implementation and its results. This study discusses the verification of the communication models in the parallel trace-driven simulation framework HAEC-SIM. As simulation input, a parallel application is executed and recorded on an existing HPC system. The simulation focuses on modeling the transfer times of point-to-point messages within the application and the indirect effects resulting in an output trace of application events of the simulated execution on a target platform. Consequently, via verification the message transfer times obtained with HAEC-SIM are compared with those of an independent implementation of the communication models. Both implementations consider the number of hops, the size and the target system parameters for each message. During verification the following factors are varied: application benchmarks, network topologies, mapping strategies, and resilient communication models.Verification yields an almost perfect agreement: the transfer times differ for a tiny percentage (0.00019 %) of messages by a negligible deviation of one picosecond, which is the finest granularity of the time data type. This result strengthens the confidence in a correct implementation of the communication models in simulation.

Data-driven methods based on artificial intelligence (AI) are powerful yet flexible tools for gathering knowledge and automating complex tasks in many areas of science and practice. Despite the rapid development of the field, the existing potential of AI methods to solve recent industrial, corporate and social challenges has not yet been fully exploited. Research shows the insufficient practicality of AI in domain-specific contexts as one of the main application hurdles. Focusing on industrial demands, this publication introduces a new paradigm in terms of applicability of AI methods, called Usable AI (UAI). Aspects of easily accessible, domain-specific AI methods are derived, which address essential user-oriented AI services within the UAI paradigm: usability, suitability, integrability and interoperability. The relevance of UAI is clarified by describing challenges, hurdles and peculiarities of AI applications in the production area, whereby the following user roles have been abstracted: developers of cyber–physical production systems (CPPS), developers of processes and operators of processes. The analysis shows that target artifacts, motivation, knowledge horizon and challenges differ for the user roles. Therefore, UAI shall enable domain- and user-role-specific adaptation of affordances accompanied by adaptive support of vertical and horizontal integration across the domains and user roles.