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

Massively Parallel Algorithm for Solving the Eikonal Equation on Multiple Accelerator Platforms

The research presented in this thesis investigates parallel implementations of the Fast Sweeping Method (FSM) for Graphics Processing Unit (GPU)-based computational plat forms and proposes a new parallel algorithm for distributed computing platforms with accelerators. Hardware accelerators such as GPUs and co-processors have emerged as general- purpose processors in today’s high performance computing (HPC) platforms, thereby increasing platforms’ performance capabilities. This trend has allowed greater parallelism and substantial acceleration of scientific simulation software. In order to leverage the power of new HPC platforms, scientific applications must be written in specific lower-level programming languages, which used to be platform specific. Newer programming models such as OpenACC simplifies implementation and assures portability of applications to run across GPUs from different vendors and multi-core processors. The distance field is a representation of a surface geometry or shape required by many algorithms within the areas of computer graphics, visualization, computational fluid dynamics and more. It can be calculated by solving the eikonal equation using the FSM. The parallel FSMs explored in this thesis have not been implemented on GPU platforms and do not scale to a large problem size. This thesis addresses this problem by designing a parallel algorithm that utilizes a domain decomposition strategy for multi-accelerated distributed platforms. The proposed algorithm applies first coarse grain parallelism using MPI to distribute subdomains across multiple nodes and then fine grain parallelism to optimize performance by utilizing accelerators. The results of the parallel implementations of FSM for GPU-based platforms showed speedup greater than 20× compared to the serial version for some problems and the newly developed parallel algorithm eliminates the limitation of current algorithms to solve large memory problems with comparable runtime efficiency

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Heterogeneous hierarchical workflow composition

Workflow systems promise scientists an automated end-to-end path from hypothesis to discovery. However, expecting any single workflow system to deliver such a wide range of capabilities is impractical. A more practical solution is to compose the end-to-end workflow from more than one system. With this goal in mind, the integration of task-based and in situ workflows is explored, where the result is a hierarchical heterogeneous workflow composed of subworkflows, with different levels of the hierarchy using different programming, execution, and data models. Materials science use cases demonstrate the advantages of such heterogeneous hierarchical workflow composition.This work is a collaboration between Argonne National Laboratory and the Barcelona Supercomputing Center within the Joint Laboratory for Extreme-Scale Computing. This research is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC02- 06CH11357, program manager Laura Biven, and by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contract 2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Building Near-Real-Time Processing Pipelines with the Spark-MPI Platform

Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three Vs (Volume, Velocity, and Variety) of experimental data and the scale of computational tasks produced the demand for new real-time processing systems at experimental facilities. Recently, this demand was addressed by the Spark-MPI approach connecting the Spark data-intensive platform with the MPI high-performance framework. In contrast with existing data management and analytics systems, Spark introduced a new middleware based on resilient distributed datasets (RDDs), which decoupled various data sources from high-level processing algorithms. The RDD middleware significantly advanced the scope of data-intensive applications, spreading from SQL queries to machine learning to graph processing. Spark-MPI further extended the Spark ecosystem with the MPI applications using the Process Management Interface. The paper explores this integrated platform within the context of online ptychographic and tomographic reconstruction pipelines.Comment: New York Scientific Data Summit, August 6-9, 201

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Inviwo -- A Visualization System with Usage Abstraction Levels

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities