Eunice E. Santos

LogP: towards a realistic model of parallel computation

A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding development of techniques that yield performance across a range of current and future parallel machines. This paper offers a new parallel machine model, called LogP, that reflects the critical technology trends underlying parallel computers. it is intended to serve as a basis for developing fast, portable parallel algorithms and to offer guidelines to machine designers. Such a model must strike a balance between detail and simplicity in order to reveal important bottlenecks without making analysis of interesting problems intractable. The model is based on four parameters that specify abstractly the computing bandwidth, the communication bandwidth, the communication delay, and the efficiency of coupling communication and computation. Portable parallel algorithms typically adapt to the machine configuration, in terms of these parameters. The utility of the model is demonstrated through examples that are implemented on the CM-5.

Optimal broadcast and summation in the LogP model

We consider several natural broadcasting problems for the LogP model of distributed memory machines recently proposed by Culler et al. For each of these problems, we present algorithms that yield an optimal communication schedule. Our algorithms are absolutely best possible in that not even the constant factors can be improved upon. We also devise an (absolutely) optimal algorithm for summing a list of elements (using a non- commutative operation) using one of the optimal broadcast algorithms.

An Effective Anytime Anywhere Parallel Approach for Centrality Measurements in Social Network Analysis

With the broad application of electronic communication monitoring tools and data-sharing techniques, the size of networks to be studied by social network analysis (SNA) has grown rapidly. However, current SNA techniques are not particularly scalable. For example, even centrality, which is one of the most frequently used SNA parameters, cannot be measured by most current SNA software when the network is large. This paper presents the design of an effective and scalable anytime anywhere parallel methodology for SNA with large-scale networks emphasizing centrality measurement algorithms. The efficiency and effectiveness of the methodology is validated by experiments of centrality analysis for large networks.

Optimal and Efficient Algorithms for Summing and Prefix Summing on Parallel Machines

The problem of designing efficient parallel algorithms for summing and prefix summing for certain classes of the LogP model is studied. We present optimal algorithms for summing and show that any optimal summing algorithm must have a certain inherent structure. Moreover, we present optimal or near-optimal algorithms for prefix summing for both non-commutative and commutative binary operators. Furthermore, we show that the optimal algorithms for prefix summing for these two types of operators are not equivalent.

Fusing multiple Bayesian knowledge sources

We address the problem of information fusion in uncertain environments. Imagine there are multiple experts building probabilistic models of the same situation and we wish to aggregate the information they provide. There are several problems we may run into by naively merging the information from each. For example, the experts may disagree on the probability of a certain event or they may disagree on the direction of causality between two events (e.g., one thinks A causes B while another thinks B causes A). They may even disagree on the entire structure of dependencies among a set of variables in a probabilistic network. In our proposed solution to this problem, we represent the probabilistic models as Bayesian Knowledge Bases (BKBs) and propose an algorithm called Bayesian knowledge fusion that allows the fusion of multiple BKBs into a single BKB that retains the information from all input sources. This allows for easy aggregation and de-aggregation of information from multiple expert sources and facilitates multi-expert decision making by providing a framework in which all opinions can be preserved and reasoned over.

Optimal and Near-Optimal Algorithms fork-Item Broadcast

Since many distributed-memory machines rely only on point-to-point com- munication between processors, various broadcast operations must be created using this type of primitive. In this paper we consider the fundamental problem of broadcastingk-items from one processor to all the remaining processors on a parallel machine. Using point-to-point communication and the LogP model, we design an algorithm fork-item broadcast whose running time is within an additive constant of the lower bound. We also present an optimal algorithm fork-item broadcast on a variant of LogP.

Modeling complex social scenarios using Culturally Infused Social Networks

Modeling complex real world scenarios require representing and analyzing information from multiple domains including social, economic and political aspects. However, most of the current frameworks in social networks are not generic enough to incorporate multi-domain information or to be applied in different scenarios. Current frameworks also make simplifications in other modeling aspects such as incorporating dynamism and providing multi-scale analyses. Representing culture is critical to truly capture the nuances of various social processes. It also helps to make the framework generic enough to be applied in multiple application domains.We will leverage a novel framework called the Culturally Infused Social Network (CISN) to represent culture using probabilistic reasoning networks called Bayesian Knowledge Bases (BKBs), in representations known as cultural fragments. Cultural fragments model the intent of actors by relating their actions to underlying beliefs and goals. CISN also supports analysis algorithms to make predictions and provide explanations. We validate CISN by simulating the 2006 Somali conflict involving the Islamic Court Union (ICU). The Somali conflict is a complex scenario requiring deep understanding of myriad factors. We focus on analyzing the group stability of ICU, how changing alliance caused conflicts and led to its ultimate demise. We define a metric to measure instability in a group, identify critical factors that led to instability in ICU and provide analyses.

An anytime-anywhere approach for maximal clique enumeration in social network analysis

Social network analysis (SNA) is a set of broadly used techniques designed for analyzing structural information contained in interactions. However, current SNA tools have a poor ability to handle large and dynamic social networks. One particular problem of interest is that of maximal clique enumeration used for studying modularity/community. Critical challenges for this problem include limited scalability and poor ability for handling dynamism. In this paper, we design and implement an anytime anywhere approach for maximal clique enumeration problem in SNA. Through a set of experiments on random graphs, we validate and demonstrate the effectiveness and efficiency of our approach.

Infusing Social Networks With Culture

Social Network Analysis (SNA) is a powerful tool for analyzing social phenomena that is based on studying how actors are connected or interact with each other. All Social Networks (SNs) are inherently embedded in particular cultures. However, the effect of cultural influence is often missing from SNA techniques. Moreover, to incorporate culture, modeling approaches have to deal with inaccurate, unrealistic, and incomplete cultural data. In order to address this problem, we propose a generic approach to systematically represent culture in the form of relevant factors and relationships, while leveraging relevant social theories, and to infuse them into SNs in order to obtain more realistic and complete analyses. Using two sets of experiments, we validate the effectiveness of our approach and demonstrate the significant advantages obtained through culturally infused SNA.

Reducing the computational load of energy evaluations for protein folding

Predicting the native conformation using computational protein models requires a large number of energy evaluations even with simplified models such as hydrophobic-hydrophilic (HP) models. Clearly, energy evaluations constitute a significant portion of computational time. We hypothesize that given the structured nature of algorithms that search for candidate conformations such as stochastic methods, energy evaluation computations can be cached and reused, thus saving computational time and effort. In this paper, we present a caching approach and apply it to the triangular 2D-HP lattice model. We provide theoretical analysis and prediction of the expected savings from caching as applied this model. We conduct experiments using a sophisticated evolutionary algorithm that contains elements of local search, memetic algorithms, diversity replacement, etc. in order to verify our hypothesis and demonstrate a significant level of savings in computational effort and time that caching can provide.