Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Article Performance and Accuracy Predictions of Approximation Methods for Shortest-Path Algorithms on Gpus(Elsevier, 2022) Aktılav, Busenur; Öz, IşılApproximate computing techniques, where less-than-perfect solutions are acceptable, present performance-accuracy trade-offs by performing inexact computations. Moreover, heterogeneous architectures, a combination of miscellaneous compute units, offer high performance as well as energy efficiency. Graph algorithms utilize the parallel computation units of heterogeneous GPU architectures as well as performance improvements offered by approximation methods. Since different approximations yield different speedup and accuracy loss for the target execution, it becomes impractical to test all methods with various parameters. In this work, we perform approximate computations for the three shortest-path graph algorithms and propose a machine learning framework to predict the impact of the approximations on program performance and output accuracy. We evaluate random predictions for both synthetic and real road-network graphs, and predictions of the large graph cases from small graph instances. We achieve less than 5% prediction error rates for speedup and inaccuracy values.Article Citation - WoS: 1Citation - Scopus: 1Hybrid Probabilistic Timing Analysis With Extreme Value Theory and Copulas(Elsevier, 2022) Bekdemir, Levent; Bazlamaçcı, Cüneyt F.The primary challenge of time-critical systems is to guarantee that a task completes its execution before its deadline. In order to ensure compliance with timing requirements, it is necessary to analyze the timing behavior of the overall software. Worst-Case Execution Time (WCET) represents the maximum amount of time an individual software unit takes to execute and is used for scheduling analysis in safety-critical systems. Recent studies focus on statistical approaches, which augments measurement-based timing analysis with probabilistic confidence level by applying stochastic methods. Common approaches either utilize Extreme Value Theory (EVT) for end-to-end measurements or convolution techniques for a group of program units to derive probabilistic upper bounds for the program. The former method does not ensure path coverage while the latter suffers from ignoring possible extreme cases. Furthermore, current state-of-the-art convolution methods employed in a commercial WCET analysis tool overestimates the results because of using the assumption of worst-case dependence between basic blocks. In this paper, we propose a hybrid probabilistic timing analysis framework and modeling the program units with EVT to capture extreme cases and use Copulas to model the dependency between the units to derive tighter distributional bounds in order to mitigate the effects of co-monotonic assumptions.Article Citation - WoS: 12Citation - Scopus: 21A Change Management Model and Its Application in Software Development Projects(Elsevier, 2019) Efe, Pınar; Demirörs, OnurChange is inevitable in software projects and software engineers strive to find ways to manage changes. A complete task could be easily in a team's agenda sometime later due to change demands. Change demands are caused by failures and/or improvements and require additional effort which in most cases have not been planned upfront and affect project progress significantly. Earned Value Management (EVM) is a powerful performance management and feedback tool for project management. EVM depicts the project progress in terms of scope, cost, and schedule and provides future predictions based on trends and patterns of the past. Even though EVM works quite well and widely used in disciplines like construction and mining, it is not the case for software discipline. Software projects require special attention and adoption for change. In this study, we present a model to measure change and subsequent rework and evolution costs to monitor software projects accurately. We have performed five case studies in five different companies to explore the usability of the proposed model. This paper depicts the proposed model and discusses the results of the case studies.Article Citation - WoS: 17Citation - Scopus: 19Pro-Metastatic Functions of Notch Signaling Is Mediated by Cyr61 in Breast Cells(Elsevier, 2020) Küçükköse, Cansu; Efe, Eda; Günyüz, Zehra Elif; Fıratlıgil, Burcu; Doğan, Hülya; Yalçın Özuysal, Özden; İlhan, MustafaMetastasis is the main cause of cancer related deaths, and unfolding the molecular mechanisms underlying metastatic progression is critical for the development of novel therapeutic approaches. Notch is one of the key signaling pathways involved in breast tumorigenesis and metastasis. Notch activation induces pro-metastatic processes such as migration, invasion and epithelial to mesenchymal transition (EMT). However, molecular mediators working downstream of Notch in these processes are not fully elucidated. CYR61 is a secreted protein implicated in metastasis, and its inhibition by a monoclonal antibody suppresses metastasis in xenograft breast tumors, indicating the clinical importance of CYR61 targeting. Here, we aimed to investigate whether CYR61 works downstream of Notch in inducing pro-metastatic phenotypes in breast cells. We showed that CYR61 expression is positively regulated by Notch activity in breast cells. Notch1-induced migration, invasion and anchorage independent growth of a normal breast cell line, MCF10A, were abrogated by CYR61 silencing. Furthermore, upregulation of core EMT markers upon Notch1-activation was impaired in the absence of CYR61. However, reduced migration and invasion of highly metastatic cell line, MDA MB 231, cells upon Notch inhibition was not dependent on CYR61 downregulation. In conclusion, we showed that in normal breast cell line MCF10A, CYR61 is a mediator of Notch1-induced pro-metastatic phenotypes partly via induction of EMT. Our results imply CYR61 as a prominent therapeutic candidate for a subpopulation of breast tumors with high Notch activity.