Publications

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Over the course of the last year, a sparse matrix-matrix multiplication routine has been developed for the Tpetra package. This routine is based on the same algorithm that is used in EpetraExt with heavy modifications. Since it achieved a working state, several major optimizations have been made in an effort to speed up the routine. This report will discuss the optimizations made to the routine, its current state, and where future work needs to be done.

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We consider the design of a sensor network to serve as an early warning system against a potential suite of contamination incidents. Given any measure for evaluating the quality of a sensor placement, there are two ways to model the objective. One is to minimize the impact or damage to the network, the other is to maximize the reduction in impact compared to the network without sensors. These objectives are the same when the problem is solved optimally. But when given equally-good approximation algorithms for each of this pair of complementary objectives, either one might be a better choice. The choice generally depends upon the quality of the approximation algorithms, the impact when there are no sensors, and the number of sensors available. We examine when each objective is better than the other by examining multiple real world networks. When assuming perfect sensors, minimizing impact is frequently superior for virulent contaminants. But when there are long response delays, or it is very difficult to reduce impact, maximizing impact reduction may be better. © 2011 ASCE.

In this paper we performed analysis of speech communications in order to determine if we can differentiate between expert and novice teams based on communication patterns. Two pairs of experts and novices performed numerous test sessions on the E-2 Enhanced Deployable Readiness Trainer (EDRT) which is a medium-fidelity simulator of the Naval Flight Officer (NFO) stations positioned at bank end of the E-2 Hawkeye. Results indicate that experts and novices can be differentiated based on communication patterns. First, experts and novices differ significantly with regard to the frequency of utterances, with both expert teams making many fewer radio calls than both novice teams. Next, the semantic content of utterances was considered. Using both manual and automated speech-to-text conversion, the resulting text documents were compared. For 7 of 8 subjects, the two most similar subjects (using cosine-similarity of term vectors) were in the same category of expertise (novice/expert). This means that the semantic content of utterances by experts was more similar to other experts, than novices, and vice versa. Finally, using machine learning techniques we constructed a classifier that, given as input the text of the speech of a subject, could identify whether the individual was an expert or novice with a very low error rate. By looking at the parameters of the machine learning algorithm we were also able to identify terms that are strongly associated with novices and experts. © 2011 Springer-Verlag.

In this paper we performed analysis of speech communications in order to determine if we can differentiate between expert and novice teams based on communication patterns. Two pairs of experts and novices performed numerous test sessions on the E-2 Enhanced Deployable Readiness Trainer (EDRT) which is a medium-fidelity simulator of the Naval Flight Officer (NFO) stations positioned at bank end of the E-2 Hawkeye. Results indicate that experts and novices can be differentiated based on communication patterns. First, experts and novices differ significantly with regard to the frequency of utterances, with both expert teams making many fewer radio calls than both novice teams. Next, the semantic content of utterances was considered. Using both manual and automated speech-to-text conversion, the resulting text documents were compared. For 7 of 8 subjects, the two most similar subjects (using cosine-similarity of term vectors) were in the same category of expertise (novice/expert). This means that the semantic content of utterances by experts was more similar to other experts, than novices, and vice versa. Finally, using machine learning techniques we constructed a classifier that, given as input the text of the speech of a subject, could identify whether the individual was an expert or novice with a very low error rate. By looking at the parameters of the machine learning algorithm we were also able to identify terms that are strongly associated with novices and experts. © 2011 Springer-Verlag.

This paper describes the design of a system to enable large-scale testing of new software stacks and prospective high-end computing architectures. The proposed architecture combines system virtualization, time dilation, architectural simulation, and slack simulation to provide scalable emulation of hypothetical systems. We also describe virtualization-based full-system measurement and monitoring tools to aid in using the proposed system for co-design of high-performance computing system software and architectural features for future systems. Finally, we provide a description of the implementation strategy and status of the proposed system. © 2011 ACM.

It is often observed that software engineering (SE) processes and practices for computational science and engineering (CSE) lag behind other SE areas [7]. This issue has been a concern for funding agencies, since new research increasingly relies upon and produces computational tools. At the same time, CSE research organizations find it difficult to prescribe formal SE practices for funded projects. Theoretical and experimental science rely heavily on independent verification of results as part of the scientific process. Computational science should have the same regard for independent verification but it does not. In this paper, we present an argument for using reproducibility and independent verification requirements as a driver to improve SE processes and practices. We describe existing efforts that support our argument, how these requirements can impact SE, challenges we face, and new opportunities for using reproducibility requirements as a driver for higher quality CSE software. Copyright 2011 ACM.

Virtualization has the potential to dramatically increase the usability and reliability of high performance computing (HPC) systems. However, this potential will remain unrealized unless overheads can be minimized. This is particularly challenging on large scale machines that run carefully crafted HPC OSes supporting tightlycoupled, parallel applications. In this paper, we show how careful use of hardware and VMM features enables the virtualization of a large-scale HPC system, specifically a Cray XT4 machine, with .5% overhead on key HPC applications, microbenchmarks, and guests at scales of up to 4096 nodes. We describe three techniques essential for achieving such low overhead: passthrough I/O, workload-sensitive selection of paging mechanisms, and carefully controlled preemption. These techniques are forms of symbiotic virtualization, an approach on which we elaborate. Copyright © 2011 ACM.

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