An overview of decision table literature.

The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems

Performance visualization for parallel programs: task-based, object-oriented approach

Developing and analyzing the performance of concurrent programs on distributed memory concurrent systems is normally a challenging task. Recently, performance visualization gains its importance as a critical tool for programmers. Programmers can have an insight into the development of parallel programs through a performance visualization. Most of the visualization tool to date have been developed for ad-hoc environments in hardware and software, and therefore its lifetime is limited. Since, however, new architectures keep emerging and application domains for distributed memory concurrent computer systems keep growing, the visualization tool should be flexible enough to accommodate unknown future demands of users (eg. new performance perspectives, application-specific views and disparate trace record formats);The Concurrent Object-Oriented ParaGraph (COOPG) is a prototype, general-purpose performance visualization package developed using an object-oriented approach. An object-oriented approach, both in design and implementation, provides a mechanism to build a simple, flexible, effective, and extensible performance visualization tool. The salient features of the COOPG include its flexible adaptability to disparate trace record formats and the incremental extensibility for incorporating user\u27s special-purpose views

Twenty-eighth annual report of the Power Affiliates Program.

Includes bibliographical references

Enabling and scaling biomolecular simulations of 100 million atoms on petascale machines with a multicore-optimized message-driven runtime

A 100-million-atom biomolecular simulation with NAMD is one of the three benchmarks for the NSF-funded sustainable petascale machine. Simulating this large molecular system on a petascale machine presents great challenges, including handling I/O, large memory footprint and getting good strong-scaling results. In this paper, we present parallel I/O techniques to enable the simula-tion. A new SMP model is designed to efficiently utilize ubiquitous wide multicore clusters by extending the CHARM++ asynchronous message-driven runtime. We exploit node-aware techniques to op-timize both the application and the underlying SMP runtime. Hi-erarchical load balancing is further exploited to scale NAMD to the full Jaguar PF Cray XT5 (224,076 cores) at Oak Ridge Na-tional Laboratory, both with and without PME full electrostatics, achieving 93 % parallel efficiency (vs 6720 cores) at 9 ms per step for a simple cutoff calculation. Excellent scaling is also obtained on 65,536 cores of the Intrepid Blue Gene/P at Argonne National Laboratory. 1