The parallel computation of morse-smale complexes

pre-printTopology-based techniques are useful for multi-scale exploration of the feature space of scalar-valued functions, such as those derived from the output of large-scale simulations. The Morse-Smale (MS) complex, in particular, allows robust identification of gradient-based features, and therefore is suitable for analysis tasks in a wide range of application domains. In this paper, we develop a two-stage algorithm to construct the Morse-Smale complex in parallel, the first stage independently computing local features per block and the second stage merging to resolve global features. Our implementation is based on MPI and a distributed-memory architecture. Through a set of scalability studies on the IBM Blue Gene/P supercomputer, we characterize the performance of the algorithm as block sizes, process counts, merging strategy, and levels of topological simplification are varied, for datasets that vary in feature composition and size. We conclude with a strong scaling study using scientific datasets computed by combustion and hydrodynamics simulations

Parallel volume rendering for large scientific data

Data sets of immense size are regularly generated by large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a \u27visualization cluster,\u27 a small to medium scale cluster dedicated to performing visualization and analysis of massive data sets generated on larger scale supercomputers. These clusters are designed to fulfill a different need than traditional supercomputers, and therefore their design mandates different hardware choices, such as increased memory, and more recently, graphics processing units (GPUs). While there has been much previous work on distributed memory visualization as well as GPU visualization, there is a relative dearth of algorithms which effectively use GPUs at a large scale in a distributed memory environment. In this work, we study a common visualization technique in a GPU-accelerated, distributed memory setting, and present performance characteristics when scaling to extremely large data sets

Hybrid Parallelism for Volume Rendering on Large, Multi- and Many-core Systems

With the computing industry trending towards multi- and many-core processors, we study how a standard visualization algorithm, ray-casting volume rendering, can benefit from a hybrid parallelism approach. Hybrid parallelism provides the best of both worlds: using distributed-memory parallelism across a large numbers of nodes increases available FLOPs and memory, while exploiting shared-memory parallelism among the cores within each node ensures that each node performs its portion of the larger calculation as efficiently as possible. We demonstrate results from weak and strong scaling studies, at levels of concurrency ranging up to 216,000, and with datasets as large as 12.2 trillion cells. The greatest benefit from hybrid parallelism lies in the communication portion of the algorithm, the dominant cost at higher levels of concurrency. We show that reducing the number of participants with a hybrid approach significantly improves performance

A Fortran Kernel Generation Framework for Scientific Legacy Code

Quality assurance procedure is very important for software development. The complexity of modules and structure in software impedes the testing procedure and further development. For complex and poorly designed scientific software, module developers and software testers need to put a lot of extra efforts to monitor not related modules\u27 impacts and to test the whole system\u27s constraints. In addition, widely used benchmarks cannot help programmers with accurate and program specific system performance evaluation. In this situation, the generated kernels could provide considerable insight into better performance tuning. Therefore, in order to greatly improve the productivity of various scientific software engineering tasks such as performance tuning, debugging, and verification of simulation results, we developed an automatic compute kernel extraction prototype platform for complex legacy scientific code. In addition, considering that scientific research and experiment require long-term simulation procedure and the huge size of data transfer, we apply message passing based parallelization and I/O behavior optimization to highly improve the performance of the kernel extractor framework and then use profiling tools to give guidance for parallel distribution. Abnormal event detection is another important aspect for scientific research; dealing with huge observational datasets combined with simulation results it becomes not only essential but also extremely difficult. In this dissertation, for the sake of detecting high frequency event and low frequency events, we reconfigured this framework equipped with in-situ data transfer infrastructure. Through the method of combining signal processing data preprocess(decimation) with machine learning detection model to train the stream data, our framework can significantly decrease the amount of transferred data demand for concurrent data analysis (between distributed computing CPU/GPU nodes). Finally, the dissertation presents the implementation of the framework and a case study of the ACME Land Model (ALM) for demonstration. It turns out that the generated compute kernel with lower cost can be used in performance tuning experiments and quality assurance, which include debugging legacy code, verification of simulation results through single point and multiple points of variables tracking, collaborating with compiler vendors, and generating custom benchmark tests

Doctor of Philosophy

dissertationRay tracing presents an efficient rendering algorithm for scientific visualization using common visualization tools and scales with increasingly large geometry counts while allowing for accurate physically-based visualization and analysis, which enables enhanced rendering and new visualization techniques. Interactivity is of great importance for data exploration and analysis in order to gain insight into large-scale data. Increasingly large data sizes are pushing the limits of brute-force rasterization algorithms present in the most widely-used visualization software. Interactive ray tracing presents an alternative rendering solution which scales well on multicore shared memory machines and multinode distributed systems while scaling with increasing geometry counts through logarithmic acceleration structure traversals. Ray tracing within existing tools also provides enhanced rendering options over current implementations, giving users additional insight from better depth cues while also enabling publication-quality rendering and new models of visualization such as replicating photographic visualization techniques