Task-based Visualization Methods for Scalable Analysis of Large Data Sets

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In addition to theory and experiment, simulation of technical and natural phenomena has become the third pillar of modern science and engineering. The analysis of resulting simulation data sets using scientific visualization techniques is an essential component of this approach. As a platform for simulation, massively-parallel high-performance computers are employed with a steadily increasing number of execution units (cores). As the resulting amount of data is growing proportionally to available computing power, the development of scalable, parallel visualization techniques is of ever increasing importance. A similar development can be observed in commodity hardware (PCs); thus corresponding schemes will also be needed on these architectures in the medium to long term future. Research into parallelization of visualization algorithms has thus far focused mostly on individual approaches. For several individual techniques, good results regarding scalability and efficiency have been shown. In contrast, real-world applications of visualization often require a combination of methods. Statements about the efficiency of such a combination, especially where different parallelization paradigms are concerned, are currently not possible. This is a significant hindrance towards the choice of suitable algorithms, especially as an unsuitable choice may result in significant or even prohibitive inefficiencies.In recent years, task-based parallelization has been established as useful across a wide range of applications. Here, an algorithm is formulated as a set of tasks, each of which represents an atomic step of the computation. Dependencies between tasks are modeled explicitly. Through this, as long as dependencies are not violated, a mostly arbitrary and concurrent sequence of execution of the tasks can be chosen in order to optimize the computation.The proposed project aims at the investigation of task-based formulations of established visualization techniques. The overarching goal is to increase applicability and utility of visualization for large data sets on contemporary and future architectures. In this regard, the general suitability of different classes of visualization algorithms towards a task-based formulation will be considered, as well as the resulting efficiency and runtime behavior. In particular, the composition of different techniques, as it is often applied in practice, will be examined. Initial work by the applicants indicates that this approach is promising.