Accelerating data-intensive scientific visualization and computing through parallelization

Many extreme-scale scientific applications generate colossal amounts of data that require an increasing number of processors for parallel processing. The research in this dissertation is focused on optimizing the performance of data-intensive parallel scientific visualization and computing. In parallel scientific visualization, there exist three well-known parallel architectures, i.e., sort-first/middle/last. The research in this dissertation studies the composition stage of the sort-last architecture for scientific visualization and proposes a generalized method, namely, Grouping More and Pairing Less (GMPL), for order-independent image composition workflow scheduling in sort-last parallel rendering. The technical merits of GMPL are two-fold: i) it takes a prime factorization-based approach for processor grouping, which not only obviates the common restriction in existing methods on the total number of processors to fully utilize computing resources, but also breaks down processors to the lowest level with a minimum number of peers in each group to achieve high concurrency and save communication cost; ii) within each group, it employs an improved direct send method to narrow down each processor’s pairing scope to further reduce communication overhead and increase composition efficiency. The performance superiority of GMPL over existing methods is evaluated through rigorous theoretical analysis and further verified by extensive experimental results on a high-performance visualization cluster. The research in this dissertation also parallelizes the over operator, which is commonly used for α-blending in various visualization techniques. Compared with its predecessor, the fully generalized over operator is n-operator compatible. To demonstrate the advantages of the proposed operator, the proposed operator is applied to the asynchronous and order-dependent image composition problem in parallel visualization. In addition, the dissertation research also proposes a very-high-speed pipeline-based architecture for parallel sort-last visualization of big data by developing and integrating three component techniques: i) a fully parallelized per-ray integration method that significantly reduces the number of iterations required for image rendering; ii) a real-time over operator that not only eliminates the restriction of pre-sorting and order-dependency, but also facilitates a high degree of parallelization for image composition. In parallel scientific computing, the research goal is to optimize QR decomposition, which is one primary algebraic decomposition procedure and plays an important role in scientific computing. QR decomposition produces orthogonal bases, i.e.,“core” bases for a given matrix, and oftentimes can be leveraged to build a complete solution to many fundamental scientific computing problems including Least Squares Problem, Linear Equations Problem, Eigenvalue Problem. A new matrix decomposition method is proposed to improve time efficiency of parallel computing and provide a rigorous proof of its numerical stability. The proposed solutions demonstrate significant performance improvement over existing methods for data-intensive parallel scientific visualization and computing. Considering the ever-increasing data volume in various science domains, the research in this dissertation have a great impact on the success of next-generation large-scale scientific applications

Kernel-based approximate dynamic programming using Bellman residual elimination

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 207-221).Many sequential decision-making problems related to multi-agent robotic systems can be naturally posed as Markov Decision Processes (MDPs). An important advantage of the MDP framework is the ability to utilize stochastic system models, thereby allowing the system to make sound decisions even if there is randomness in the system evolution over time. Unfortunately, the curse of dimensionality prevents most MDPs of practical size from being solved exactly. One main focus of the thesis is on the development of a new family of algorithms for computing approximate solutions to large-scale MDPs. Our algorithms are similar in spirit to Bellman residual methods, which attempt to minimize the error incurred in solving Bellman's equation at a set of sample states. However, by exploiting kernel-based regression techniques (such as support vector regression and Gaussian process regression) with nondegenerate kernel functions as the underlying cost-to-go function approximation architecture, our algorithms are able to construct cost-to-go solutions for which the Bellman residuals are explicitly forced to zero at the sample states. For this reason, we have named our approach Bellman residual elimination (BRE). In addition to developing the basic ideas behind BRE, we present multi-stage and model-free extensions to the approach. The multistage extension allows for automatic selection of an appropriate kernel for the MDP at hand, while the model-free extension can use simulated or real state trajectory data to learn an approximate policy when a system model is unavailable.(cont.) We present theoretical analysis of all BRE algorithms proving convergence to the optimal policy in the limit of sampling the entire state space, and show computational results on several benchmark problems. Another challenge in implementing control policies based on MDPs is that there may be parameters of the system model that are poorly known and/or vary with time as the system operates. System performance can suer if the model used to compute the policy differs from the true model. To address this challenge, we develop an adaptive architecture that allows for online MDP model learning and simultaneous re-computation of the policy. As a result, the adaptive architecture allows the system to continuously re-tune its control policy to account for better model information 3 obtained through observations of the actual system in operation, and react to changes in the model as they occur. Planning in complex, large-scale multi-agent robotic systems is another focus of the thesis. In particular, we investigate the persistent surveillance problem, in which one or more unmanned aerial vehicles (UAVs) and/or unmanned ground vehicles (UGVs) must provide sensor coverage over a designated location on a continuous basis. This continuous coverage must be maintained even in the event that agents suer failures over the course of the mission. The persistent surveillance problem is pertinent to a number of applications, including search and rescue, natural disaster relief operations, urban traffic monitoring, etc.(cont.) Using both simulations and actual flight experiments conducted in the MIT RAVEN indoor flight facility, we demonstrate the successful application of the BRE algorithms and the adaptive MDP architecture in achieving high mission performance despite the random occurrence of failures. Furthermore, we demonstrate performance benefits of our approach over a deterministic planning approach that does not account for these failures.by Brett M. Bethke.Ph.D