Abnormal cerebrospinal fluid (CSF) flow is suspected to be a contributor to the pathogenesis of neurodegenerative diseases such as Alzheimer's through the accumulation of toxic metabolites, and to the malfunction of intracranial pressure regulation, possibly through disruption of neuroendocrine communication. For the understanding of transport processes involved in either, knowledge of in vivo CSF dynamics is important. We present a three-dimensional, transient, subject-specific computational analysis of CSF flow in the human cranial subarachnoid space (SAS) based on in vivo magnetic resonance imaging. We observed large variations in the spatial distribution of flow velocities with a temporal peak of 5 cm s 21 in the anterior SAS and less than 4 mm s 21 in the superior part. This could reflect dissimilar flushing requirements of brain areas that may show differences in susceptibility to pathological CSF flow. Our methods can be used to compare the transport of metabolites and neuroendocrine substances in healthy and diseased brains.

The new challenges presented by exascale system architectures have resulted in difficulty achieving the desired scalability using traditional distributed-memory runtimes. Asynchronous many-task systems (AMT) are based on a new paradigm showing promise in addressing these challenges, providing application developers with a productive and performant approach to programming on next generation systems. HPX is a C++ Library for concurrency and parallelism that is developed by The STE||AR Group, an international group of collaborators working in the field of distributed and parallel programming (Heller, Diehl, Byerly, Biddiscombe, & Kaiser, 2017; Kaiser et al., n.d.; Tabbal, Anderson, Brodowicz, Kaiser, & Sterling, 2011). It is a runtime system written using modern C++ techniques that are linked as part of an application. HPX exposes extended services and functionalities supporting the implementation of parallel, concurrent, and distributed capabilities for applications in any domain; it has been used in scientific computing, gaming, finances, data mining, and other fields.

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Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed-memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing; plotting over time; or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.

We present a highly scalable demonstration of a portable asynchronous many-task programming model and runtime system applied to a grid-based adaptive mesh refinement hydrodynamic simulation of a double white dwarf merger with 14 levels of refinement that spans 17 orders of magnitude in astrophysical densities. The code uses the portable Cþþ parallel programming model that is embodied in the HPX library and being incorporated into the ISO Cþþ standard. The model represents a significant shift from existing bulk synchronous parallel programming models under consideration for exascale systems. Through the use of the Futurization technique, seemingly sequential code is transformed into wait-free asynchronous tasks. We demonstrate the potential of our model by showing results from strong scaling runs on National Energy Research Scientific Computing Center's Cori system (658,784 Intel Knight's Landing cores) that achieve a parallel efficiency of 96.8% using billions of asynchronous tasks.