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As data sets grow to exascale, automated data analysis and visualization are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of $O(\mathrm{lg}\; V \;\mathrm{lg} t)$ O (lg V lg t) parallel steps and $O(V\; \mathrm{lg}\; V)$ O (V lg V) work for data with $V$ V samples and $t$ t contour tree supernodes, and implementations with more than $30\times$ 30 × parallel speed up on both CPU using TBB and GPU using Thrust and up $70\times$ 70 × speed up compared to the serial sweep and merge algorithm. [ABSTRACT FROM AUTHOR]

Large-scale simulation codes typically execute for extended periods of time and often on distributed computational resources. Because these simulations can run for hours, or even days, scientists like to get feedback about the state of the computation and the validity of its results as it runs. It is also important that these capabilities be made available with little impact on the performance and stability of the simulation. Visualizing and exploring data in the early stages of the simulation can help scientists identify problems early, potentially avoiding a situation where a simulation runs for several days, only to discover that an error with an input parameter caused both time and resources to be wasted. We describe an application that aids in the monitoring and analysis of a simulation of the human arterial tree. The application provides researchers with high-level feedback about the state of the ongoing simulation and enables them to investigate particular areas of interest in greater detail. The application also offers monitoring information about the amount of data produced and data transfer performance among the various components of the application. [ABSTRACT FROM AUTHOR]