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Search Results: 1 - 10 of 4602 matches for " Erik Saule "
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Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi
Erik Saule,Kamer Kaya,Umit V. Catalyurek
Computer Science , 2013,
Abstract: Intel Xeon Phi is a recently released high-performance coprocessor which features 61 cores each supporting 4 hardware threads with 512-bit wide SIMD registers achieving a peak theoretical performance of 1Tflop/s in double precision. Many scientific applications involve operations on large sparse matrices such as linear solvers, eigensolver, and graph mining algorithms. The core of most of these applications involves the multiplication of a large, sparse matrix with a dense vector (SpMV). In this paper, we investigate the performance of the Xeon Phi coprocessor for SpMV. We first provide a comprehensive introduction to this new architecture and analyze its peak performance with a number of micro benchmarks. Although the design of a Xeon Phi core is not much different than those of the cores in modern processors, its large number of cores and hyperthreading capability allow many application to saturate the available memory bandwidth, which is not the case for many cutting-edge processors. Yet, our performance studies show that it is the memory latency not the bandwidth which creates a bottleneck for SpMV on this architecture. Finally, our experiments show that Xeon Phi's sparse kernel performance is very promising and even better than that of cutting-edge general purpose processors and GPUs.
Information Estimation on Extraction and Contents of Technological Redistribution at Steel Production  [PDF]
Kazhikenova Saule
Geomaterials (GM) , 2012, DOI: 10.4236/gm.2012.21004
Abstract: The work suggests a formula for estimating complex indeterminancy of a group of technological operations undergoing analyses before and after their improvement, as well as technological schemes as a whole in the information units. The formula allows to estimate the complex indeterminancy of a group of technological operations undergoing analyses, as well as technological schemes as a whole, which will result in determining predictability and technological reliability of these operations.
Research of Crystallization of Aluminums Cast Iron  [PDF]
Saule Kaldybayeva
Journal of Minerals and Materials Characterization and Engineering (JMMCE) , 2011, DOI: 10.4236/jmmce.2011.1013092
Abstract: In work was researched the composition and structure of high-alloyed aluminum cast iron ЧЮ22Ш. Crystalization of cast iron ЧЮ22Ш (ЧЮ22Ш standard cast iron) was researched by phase transformation, leaking upon its harden and cooling –down. High-alloyed materials are widely applied as the heat-resistant materials. Overall content of that reaches 30-50% and more. Previous performed researches allowed to optimize the content high-alloyed aluminum cast iron ЧЮ22Ш, to research its structure, casting and operational characteristics, to develop technological mode of melting, casting and thermal / heat treatment casts, to held its industrial examination and to determine perspective direction of its application. However, in present time ability of the aluminum cast iron ЧЮ22Ш is being used not sufficiently.
Load-Balancing Spatially Located Computations using Rectangular Partitions
Erik Saule,Erdeniz ?. Ba?,ümit V. ?atalyürek
Computer Science , 2011,
Abstract: Distributing spatially located heterogeneous workloads is an important problem in parallel scientific computing. We investigate the problem of partitioning such workloads (represented as a matrix of non-negative integers) into rectangles, such that the load of the most loaded rectangle (processor) is minimized. Since finding the optimal arbitrary rectangle-based partition is an NP-hard problem, we investigate particular classes of solutions: rectilinear, jagged and hierarchical. We present a new class of solutions called m-way jagged partitions, propose new optimal algorithms for m-way jagged partitions and hierarchical partitions, propose new heuristic algorithms, and provide worst case performance analyses for some existing and new heuristics. Moreover, the algorithms are tested in simulation on a wide set of instances. Results show that two of the algorithms we introduce lead to a much better load balance than the state-of-the-art algorithms. We also show how to design a two-phase algorithm that reaches different time/quality tradeoff.
Incremental Algorithms for Network Management and Analysis based on Closeness Centrality
Ahmet Erdem Sariyuce,Kamer Kaya,Erik Saule,Umit V. Catalyurek
Computer Science , 2013,
Abstract: Analyzing networks requires complex algorithms to extract meaningful information. Centrality metrics have shown to be correlated with the importance and loads of the nodes in network traffic. Here, we are interested in the problem of centrality-based network management. The problem has many applications such as verifying the robustness of the networks and controlling or improving the entity dissemination. It can be defined as finding a small set of topological network modifications which yield a desired closeness centrality configuration. As a fundamental building block to tackle that problem, we propose incremental algorithms which efficiently update the closeness centrality values upon changes in network topology, i.e., edge insertions and deletions. Our algorithms are proven to be efficient on many real-life networks, especially on small-world networks, which have a small diameter and a spike-shaped shortest distance distribution. In addition to closeness centrality, they can also be a great arsenal for the shortest-path-based management and analysis of the networks. We experimentally validate the efficiency of our algorithms on large networks and show that they update the closeness centrality values of the temporal DBLP-coauthorship network of 1.2 million users 460 times faster than it would take to compute them from scratch. To the best of our knowledge, this is the first work which can yield practical large-scale network management based on closeness centrality values.
Recommendation on Academic Networks using Direction Aware Citation Analysis
Onur Kü?üktun?,Erik Saule,Kamer Kaya,ümit V. ?atalyürek
Computer Science , 2012,
Abstract: The literature search has always been an important part of an academic research. It greatly helps to improve the quality of the research process and output, and increase the efficiency of the researchers in terms of their novel contribution to science. As the number of published papers increases every year, a manual search becomes more exhaustive even with the help of today's search engines since they are not specialized for this task. In academics, two relevant papers do not always have to share keywords, cite one another, or even be in the same field. Although a well-known paper is usually an easy pray in such a hunt, relevant papers using a different terminology, especially recent ones, are not obvious to the eye. In this work, we propose paper recommendation algorithms by using the citation information among papers. The proposed algorithms are direction aware in the sense that they can be tuned to find either recent or traditional papers. The algorithms require a set of papers as input and recommend a set of related ones. If the user wants to give negative or positive feedback on the suggested paper set, the recommendation is refined. The search process can be easily guided in that sense by relevance feedback. We show that this slight guidance helps the user to reach a desired paper in a more efficient way. We adapt our models and algorithms also for the venue and reviewer recommendation tasks. Accuracy of the models and algorithms is thoroughly evaluated by comparison with multiple baselines and algorithms from the literature in terms of several objectives specific to citation, venue, and reviewer recommendation tasks. All of these algorithms are implemented within a publicly available web-service framework (http://theadvisor.osu.edu/) which currently uses the data from DBLP and CiteSeer to construct the proposed citation graph.
Diversifying Citation Recommendations
Onur Kü?üktun?,Erik Saule,Kamer Kaya,ümit V. ?atalyürek
Computer Science , 2012,
Abstract: Literature search is arguably one of the most important phases of the academic and non-academic research. The increase in the number of published papers each year makes manual search inefficient and furthermore insufficient. Hence, automatized methods such as search engines have been of interest in the last thirty years. Unfortunately, these traditional engines use keyword-based approaches to solve the search problem, but these approaches are prone to ambiguity and synonymy. On the other hand, bibliographic search techniques based only on the citation information are not prone to these problems since they do not consider textual similarity. For many particular research areas and topics, the amount of knowledge to humankind is immense, and obtaining the desired information is as hard as looking for a needle in a haystack. Furthermore, sometimes, what we are looking for is a set of documents where each one is different than the others, but at the same time, as a whole we want them to cover all the important parts of the literature relevant to our search. This paper targets the problem of result diversification in citation-based bibliographic search. It surveys a set of techniques which aim to find a set of papers with satisfactory quality and diversity. We enhance these algorithms with a direction-awareness functionality to allow the users to reach either old, well-cited, well-known research papers or recent, less-known ones. We also propose a set of novel techniques for a better diversification of the results. All the techniques considered are compared by performing a rigorous experimentation. The results show that some of the proposed techniques are very successful in practice while performing a search in a bibliographic database.
Shattering and Compressing Networks for Centrality Analysis
Ahmet Erdem Sar?yüce,Erik Saule,Kamer Kaya,ümit V. ?atalyürek
Computer Science , 2012,
Abstract: Who is more important in a network? Who controls the flow between the nodes or whose contribution is significant for connections? Centrality metrics play an important role while answering these questions. The betweenness metric is useful for network analysis and implemented in various tools. Since it is one of the most computationally expensive kernels in graph mining, several techniques have been proposed for fast computation of betweenness centrality. In this work, we propose and investigate techniques which compress a network and shatter it into pieces so that the rest of the computation can be handled independently for each piece. Although we designed and tuned the shattering process for betweenness, it can be adapted for other centrality metrics in a straightforward manner. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types of networks.
On Distributed Graph Coloring with Iterative Recoloring
Ahmet Erdem Sar?yüce,Erik Saule,ümit V. ?atalyürek
Computer Science , 2014,
Abstract: Identifying the sets of operations that can be executed simultaneously is an important problem appearing in many parallel applications. By modeling the operations and their interactions as a graph, one can identify the independent operations by solving a graph coloring problem. Many efficient sequential algorithms are known for this NP-Complete problem, but they are typically unsuitable when the operations and their interactions are distributed in the memory of large parallel computers. On top of an existing distributed-memory graph coloring algorithm, we investigate two compatible techniques in this paper for fast and scalable distributed-memory graph coloring. First, we introduce an improvement for the distributed post-processing operation, called recoloring, which drastically improves the number of colors. We propose a novel and efficient communication scheme for recoloring which enables it to scale gracefully. Recoloring must be seeded with an existing coloring of the graph. Our second contribution is to introduce a randomized color selection strategy for initial coloring which quickly produces solutions of modest quality. We extensively evaluate the impact of our new techniques on existing distributed algorithms and show the time-quality tradeoffs. We show that combining an initial randomized coloring with multiple recoloring iterations yields better quality solutions with the smaller runtime at large scale.
Calcium deficit in diet as risk factor for osteopenic syndrome in pregnant women of young age
Saule Kabylova
Medical and Health Science Journal , 2010,
Abstract: The study features of bone tissue mineral density and their correlation with bloodlevels of calcium, magnesium and non-organic phosphorus in young pregnantwomen. Clinical and ambulatory examination of 120 pregnant women of ages 16-25 was made. Estimation of bone tissue mineral density and blood levels ofcalcium, phosphorus and magnesium was performed during the pregnancy stageof 12 to 28 weeks. Among young pregnant women during II trimester, rate ofosteopenic syndrome amounts to 17.5%, including osteoporosis of 1.7%.Development of osteopenic syndrome is attributed to general blood calciumdeficiency. Development of osteopenic syndrome in young pregnant women isassociated with decreased dietary consumption of calcium (owing to exclusion ofmilk products). Bone tissue mineral density is directly correlated with bloodplasma levels of calcium.
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