5 research outputs found
Maine State Government Administrative Report 1998-1999
https://digitalmaine.com/me_annual_reports/1025/thumbnail.jp
Efficient algorithms and architectures for protein 3-D structure comparison
Η σύγκριση δομών πρωτεϊνών είναι ανεπτυγμένος τομέας της υπολογιστικής πρωτεϊνωμικής που χρησιμοποιείται ευρέως στη δομική βιολογία και την ανακάλυψη φαρμάκων. Οι αυξανόμενες υπολογιστικές απαιτήσεις του είναι αποτέλεσμα τριών παραγόντων: ταχεία επέκταση των βάσεων δεδομένων με νέες δομές πρωτεϊνών, υψηλή υπολογιστική πολυπλοκότητα των αλγορίθμων σύγκρισης δομών πρωτεϊνών κατά ζεύγη (PSC), και τάση χρήσης πολλαπλών μεθόδων σύγκρισης και συνδυασμού των αποτελεσμάτων τους (multi criteria protein structure comparison-MCPSC-), μιας και δεν υπάρχει PSC μέθοδος κοινά αποδεκτή ως η καλύτερη. Αναπτύξαμε πλαίσιο λογισμικού που εκμεταλλεύεται επεξεργαστές πολλών πυρήνων για την υλοποίηση παράλληλων στρατηγικών MCPSC με βάση τρεις δημοφιλείς PSC μεθόδους, τις TMalign, CE και USM. Συγκρίνουμε την απόδοση και αποδοτικότητα δύο παράλληλων υλοποιήσεων MCPSC στον πειραματικό επεξεργαστή δικτύου σε ψηφίδα (Network on Chip) Intel Single-Chip Cloud Computer και τον δημοφιλή επεξεργαστή Intel Core i7. Επιπλέον, αναπτύξαμε εκτενές υπολογιστικό pipeline και υλοποίησή του με πρόγραμμα Python, που ονομάζεται pyMCPSC, που επιτρέπει στους χρήστες να εκτελούν MCPSC διεργασίες σε επεξεργαστές πολλαπλών πυρήνων. Το pyMCPSC, το οποίο συνδυάζει πέντε μεθόδους PSC και υποστηρίζει πέντε διαφορετικά σχήματα συναίνεσης MCPSC, υποστηρίζει τη συγκριτική ανάλυση μεγάλων συνόλων με δομές πρωτεϊνών και μπορεί να επεκταθεί ώστε να ενσωματώσει και νέες μεθόδους PSC στις βαθμολογίες συναίνεσης, καθώς αυτές καθίστανται διαθέσιμες.Protein Structure Comparison (PSC) is a well developed field of computational proteomics with active interest since it is widely used in structural biology and drug discovery. Fast increasing computational demand for all-to-all protein structures comparison is a result of mainly three factors: rapidly expanding structural proteomics databases, high computational complexity of pairwise PSC algorithms, and the trend towards using multiple criteria for comparison and combining their results (MCPSC). In this thesis we have developed a software framework that exploits many-core and multi-core CPUs to implement efficient parallel MCPSC schemes in modern processors based on three popular PSC methods, namely, TMalign, CE, and USM. We evaluate and compare the performance and efficiency of two parallel MCPSC implementations using Intel’s experimental many-core Single-Chip Cloud Computer (SCC) CPU as well as Intel’s Core i7 multi-core processor. Further, we have developed a dataset processing pipeline and implemented it in a Python utility, called pyMCPSC, allowing users to perform MCPSC efficiently on multi-core CPU. pyMCPSC, which combines five PSC methods and five different consensus scoring schemes, facilitates the analysis of similarities in protein domain datasets and can be easily extended to incorporate more PSC methods in the consensus scoring as they are becoming available
Independent task assignment for heterogeneous systems
Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent Univ., 2013.Thesis (Ph. D.) -- Bilkent University, 2013.Includes bibliographical references leaves 136-150.We study the problem of assigning nonuniform tasks onto heterogeneous systems.
We investigate two distinct problems in this context. The first problem is the
one-dimensional partitioning of nonuniform workload arrays with optimal load
balancing. The second problem is the assignment of nonuniform independent
tasks onto heterogeneous systems.
For one-dimensional partitioning of nonuniform workload arrays, we investigate
two cases: chain-on-chain partitioning (CCP), where the order of the processors
is specified, and chain partitioning (CP), where processor permutation
is allowed. We present polynomial time algorithms to solve the CCP problem
optimally, while we prove that the CP problem is NP complete. Our empirical
studies show that our proposed exact algorithms for the CCP problem produce
substantially better results than the state-of-the-art heuristics while the solution
times remain comparable.
For the independent task assignment problem, we investigate improving the
performance of the well-known and widely used constructive heuristics MinMin,
MaxMin and Sufferage. All three heuristics are known to run in O(KN2
) time in
assigning N tasks to K processors. In this thesis, we present our work on an algorithmic
improvement that asymptotically decreases the running time complexity
of MinMin to O(KN log N) without affecting its solution quality. Furthermore,
we combine the newly proposed MinMin algorithm with MaxMin as well as Sufferage,
obtaining two hybrid algorithms. The motivation behind the former hybrid
algorithm is to address the drawback of MaxMin in solving problem instances
with highly skewed cost distributions while also improving the running time performance
of MaxMin. The latter hybrid algorithm improves the running time
performance of Sufferage without degrading its solution quality. The proposed
algorithms are easy to implement and we illustrate them through detailed pseudocodes.
The experimental results over a large number of real-life datasets show
that the proposed fast MinMin algorithm and the proposed hybrid algorithms
perform significantly better than their traditional counterparts as well as more
recent state-of-the-art assignment heuristics. For the large datasets used in the
experiments, MinMin, MaxMin, and Sufferage, as well as recent state-of-the-art
heuristics, require days, weeks, or even months to produce a solution, whereas all
of the proposed algorithms produce solutions within only two or three minutes.
For the independent task assignment problem, we also investigate adopting
the multi-level framework which was successfully utilized in several applications
including graph and hypergraph partitioning. For the coarsening phase of the
multi-level framework, we present an efficient matching algorithm which runs in
O(KN) time in most cases. For the uncoarsening phase, we present two refinement
algorithms: an efficient O(KN)-time move-based refinement and an efficient
O(K2N log N)-time swap-based refinement. Our results indicate that multi-level
approach improves the quality of task assignments, while also improving the running
time performance, especially for large datasets.
As a realistic distributed application of the independent task assignment problem,
we introduce the site-to-crawler assignment problem, where a large number
of geographically distributed web servers are crawled by a multi-site distributed
crawling system and the objective is to minimize the duration of the crawl. We
show that this problem can be modeled as an independent task assignment problem.
As a solution to the problem, we evaluate a large number of state-of-the-art
task assignment heuristics selected from the literature as well as the improved
versions and the newly developed multi-level task assignment algorithm. We
compare the performance of different approaches through simulations on very
large, real-life web datasets. Our results indicate that multi-site web crawling
efficiency can be considerably improved using the independent task assignment
approach, when compared to relatively easy-to-implement, yet naive baselines.Tabak, E KartalPh.D
NASA RECON: Course Development, Administration, and Evaluation
The R and D activities addressing the development, administration, and evaluation of a set of transportable, college-level courses to educate science and engineering students in the effective use of automated scientific and technical information storage and retrieval systems, and, in particular, in the use of the NASA RECON system, are discussed. The long-range scope and objectives of these contracted activities are overviewed and the progress which has been made toward these objectives during FY 1983-1984 is highlighted. In addition, the results of a survey of 237 colleges and universities addressing course needs are presented
Bibliography of Lewis Research Center technical publications announced in 1984
This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1984. All the publications were announced in the 1984 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses