Abstract-Cost estimation is a vital task in most important software project decisions such as resource allocation and bidding. Analogy-based cost estimation is particularly transparent, as it relies on historical information from similar past projects, whereby similarities are determined by comparing the projects' key attributes and features. However, one crucial aspect of the analogy-based method is not yet fully accounted for: the different impact or weighting of a project's various features. Current approaches either try to find the dominant features or require experts to weight the features. Neither of these yields optimal estimation performance. Therefore, we propose to allocate separate weights to each project feature and to find the optimal weights by extensive search. We test this approach on several real-world data sets and measure the improvements with commonly used quality metrics. We find that this method 1) increases estimation accuracy and reliability, 2) reduces the model's volatility and, thus, is likely to increase its acceptance in practice, and 3) indicates upper limits for analogy-based estimation quality as measured by standard metrics.

A number of scientific applications run on current HPC systems would benefit from an approximate assessment of parallel overhead. In many instances a quick and simple method to obtain a general overview on the subject is regarded useful auxiliary information by the routine HPC user. Here we present such a method using just execution times for increasing numbers of parallel processing cores. We start out with several common scientific applications and measure the fraction of time spent in MPI communication. Forming the ratio of MPI time to overall execution time we obtain a smooth curve that can be parameterized by only two constants. We then use this two-parameter expression and extend Amdahl's theorem with a new term representing parallel overhead in general. Fitting the original data set with this extended Amdahl expression yields an estimate for the parallel overhead closely matching the MPI time determined previously.

Photovoltaics is one of the key areas in renewable energy research with remarkable progress made every year. Here we consider the case of a photoactive material and study its structural composition and the resulting consequences for the fundamental processes driving solar energy conversion. A multiscale approach is used to characterize essential molecular properties of the light-absorbing layer. A selection of bulk-representative pairs of donor/acceptor molecules is extracted from the molecular dynamics simulation of the bulk heterojunction and analyzed at increasing levels of detail. Significantly increased ground state energies together with an array of additional structural characteristics are identified that all point towards an auxiliary role of the material's structural organization in mediating charge-transfer and -separation. Mechanistic studies of the type presented here can provide important insights into fundamental principles governing solar energy conversion in next-generation photovoltaic devices.

Porting scientific key algorithms to HPC architectures requires a thorough understanding of the subtle balance between gain in performance and introduced overhead. Here we continue the development of our recently proposed technique that uses plain execution times to predict the extent of parallel overhead. The focus here is on an analytic solution that takes into account as many data points as there are unknowns, i.e. model parameters. A test set of 9 applications frequently used in scientific computing can be well described by the suggested model even including atypical cases that were originally not considered part of the development. However, the choice about which particular set of explicit data points will lead to an optimal prediction cannot be made a-priori.