Publications

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We report on the use and design of a portable, extensible performance data collection tool motivated by modeling needs of the high performance computing systems co-design com- munity. The lightweight performance data collectors with Eiger support is intended to be a tailorable tool, not a shrink-wrapped library product, as pro ling needs vary widely. A single code markup scheme is reported which, based on compilation ags, can send perfor- mance data from parallel applications to CSV les, to an Eiger mysql database, or (in a non-database environment) to at les for later merging and loading on a host with mysql available. The tool supports C, C++, and Fortran applications.

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I report the progress to date of my work on scaling the CPAPR algorithm and necessary supporting code to enable processing large (gigabyte to 100 gigabyte) data sets and benchmarking the same. Where possible, I also report background information possibly of relevance in future modifications of the code. The results include: minor repairs and additions to the TTB library for portability, algorithmic improvements relevant to both serial and multithreaded implementations, algorithmic improvements taking advantage of multithreading hardware, support library additions (binary IO routines) needed for efficiently and reproducibly benchmarking the algorithms. For this optimization work, no large scale data sets are available. Therefore, scalability of data synthesis algorithms is addressed as well.

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The goal of this research was to explore first principles associated with mixing of diverse implementations in a redundant fashion to increase the security and/or reliability of information systems. Inspired by basic results in computer science on the undecidable behavior of programs and by previous work on fault tolerance in hardware and software, we have investigated the problem and solution space for addressing potentially unknown and unknowable vulnerabilities via ensembles of implementations. We have obtained theoretical results on the degree of security and reliability benefits from particular diverse system designs, and mapped promising approaches for generating and measuring diversity. We have also empirically studied some vulnerabilities in common implementations of the Linux operating system and demonstrated the potential for diversity to mitigate these vulnerabilities. Our results provide foundational insights for further research on diversity and redundancy approaches for information systems.

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This report presents the results of our efforts to apply high-performance computing to entity-based simulations with a multi-use plugin for parallel computing. We use the term 'Entity-based simulation' to describe a class of simulation which includes both discrete event simulation and agent based simulation. What simulations of this class share, and what differs from more traditional models, is that the result sought is emergent from a large number of contributing entities. Logistic, economic and social simulations are members of this class where things or people are organized or self-organize to produce a solution. Entity-based problems never have an a priori ergodic principle that will greatly simplify calculations. Because the results of entity-based simulations can only be realized at scale, scalable computing is de rigueur for large problems. Having said that, the absence of a spatial organizing principal makes the decomposition of the problem onto processors problematic. In addition, practitioners in this domain commonly use the Java programming language which presents its own problems in a high-performance setting. The plugin we have developed, called the Parallel Particle Data Model, overcomes both of these obstacles and is now being used by two Sandia frameworks: the Decision Analysis Center, and the Seldon social simulation facility. While the ability to engage U.S.-sized problems is now available to the Decision Analysis Center, this plugin is central to the success of Seldon. Because Seldon relies on computationally intensive cognitive sub-models, this work is necessary to achieve the scale necessary for realistic results. With the recent upheavals in the financial markets, and the inscrutability of terrorist activity, this simulation domain will likely need a capability with ever greater fidelity. High-performance computing will play an important part in enabling that greater fidelity.

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Ccaffeine is a Common Component Architecture (CCA) framework devoted to high-performance computing. In this note we give an overview of the system features of Ccaffeine and CCA that support component-based HPC application development. Object-oriented, single-threaded and lightweight, Ccaffeine is designed to get completely out of the way of the running application after it has been composed from components. Ccaffeine is one of the few frameworks, CCA or otherwise, that can compose and run applications on a parallel machine interactively and then automatically generate a static, possibly self-tuning, executable for production runs. Users can experiment with and debug applications interactively, improving their productivity. When the application is ready, a script is automatically generated, parsed and turned into a static executable for production runs. Within this static executable, dynamic replacement of components can be performed by self-tuning applications.