Research Outputs

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    A framework for task placement on multicore architectures
    (Koç University, 2018) Soomro, Pirah Noor; Erten, Didem Unat; 0000-0002-2351-0770; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 219274
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    Analysis of the relation between mobility signatures and product usage of bank customers on product recommendation systems
    (Koç University, 2017) Ürküp, Çağan; Salman, Fatma Sibel; 0000-0001-6833-2552; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 178838
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    Estimating process simulation errors of an industrial fractionator column by using data-driven modelling techniques
    (Koç University, 2017) Ödemiş, Sadık; Ali, Özden Gür; 0000-0002-9409-4532; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 57780
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    GAN inversion based image manipulation with text-guided encoders
    (Koç University, 2022) Baykal, Ahmet Canberk; Yüret, Deniz; 0000-0002-7039-0046; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 179996
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    Runtime race detection for shared memory programming models
    (Koç University, 2018) Matar, Hassan Salehe; Erten, Didem Unat; 0000-0002-2351-0770; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 219274
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    Spatio-temporal expenditure forecasting for bank customers using transactional data
    (Koç University, 2017) Telciler, Kaan; Salman, Fatma Sibel; 0000-0001-6833-2552; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 178838
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    Trade: precise dynamic race detection for scalable transactional memory systems
    (Association for Computing Machinery, 2015) Kestor, Gökçen; Ünsal, Osman S.; Cristal, Adrian; Department of Computer Engineering; Taşıran, Serdar; Faculty Member; Department of Computer Engineering; College of Engineering; N/A
    As other multithreaded programs, transactional memory (TM) programs are prone to race conditions. Previous work focuses on extending existing definitions of data race for lock-based applications to TM applications, which requires all transactions to be totally ordered "as if" serialized by a global lock. This approach poses implementation constraints on the STM that severely limits TM applications' performance. This article shows that forcing total ordering among all running transactions, while sufficient, is not necessary. We introduce an alternative data race definition, relaxed transactional data race, that requires ordering of only conflicting transactions. The advantages of our relaxed definition are twofold: First, unlike the previous definition, this definition can be applied to a wide range of TMs, including those that do not enforce transaction total ordering. Second, within a single execution, it exposes a higher number of data races, which considerably reduces debugging time. Based on this definition, we propose a novel and precise race detection tool for C/C++ TM applications (TRADE), which detects data races by tracking happens-before edges among conflicting transactions. Our experiments reveal that TRADE precisely detects data races for STAMP applications running on modern STMs with overhead comparable to state-of-the-art race detectors for lock-based applications. Our experiments also show that in a single run, TRADE identifies several races not discovered by 10 separate runs of a race detection tool based on the previous data race definition.
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    Transfers of personal data from the EU to the US: a legal assessment of the current and future framework
    (Koç University, 2019) Parlar, Cihan; Ayata, Zeynep; 0000-0002-0820-1267; Koç University Graduate School of Social Sciences and Humanities; Law; 186694
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    Verification of concurrent programs via refinement proofs
    (Koç University, 2018) Mutluergil, Süha Orhun; Gürsoy, Attila; Enea, Constantin; 0000-0002-2297-2113; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 8745