A review of literature on parallel constraint solving

As multicore computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multicore computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation

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

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

FieldPlacer - A flexible, fast and unconstrained force-directed placement method for heterogeneous reconfigurable logic architectures

The field of placement methods for components of integrated circuits, especially in the domain of reconfigurable chip architectures, is mainly dominated by a handful of concepts. While some of these are easy to apply but difficult to adapt to new situations, others are more flexible but rather complex to realize. This work presents the FieldPlacer framework, a flexible, fast and unconstrained force-directed placement method for heterogeneous reconfigurable logic architectures, in particular for the ever important heterogeneous FPGAs. In contrast to many other force-directed placers, this approach is called ‘unconstrained’ as it does not require a priori fixed logic elements in order to calculate a force equilibrium as the solution to a system of equations. Instead, it is based on a free spring embedder simulation of a graph representation which includes all logic block types of a design simultaneously. The FieldPlacer framework offers a huge amount of flexibility in applying different distance norms (e. g., the Manhattan distance) for the force-directed layout and aims at creating adapted layouts for various objective functions, e. g., highest performance or improved routability. Depending on the individual situation, a runtime-quality trade-off can be considered to either produce a decent placement in a very short time or to generate an exceptionally good placement, which takes longer. An extensive comparison with the latest simulated annealing placement method from the well-known Versatile Place and Route (VPR) framework shows that the FieldPlacer approach can create placements of comparable quality much faster than VPR or, alternatively, generate better placements in the same time. The flexibility in defining arbitrary objective functions and the intuitive adaptability of the method, which, among others, includes different concepts from the field of graph drawing, should facilitate further developments with this framework, e. g., for new upcoming optimization targets like the energy consumption of an implemented design

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward