Koç University Research Hub

Welcome to the Koç University Institutional Academic Repository

The Koç University Institutional Academic Repository (KUHub Research) is a state-of-the-art academic repository that consolidates all academic outputs—such as articles, conference proceedings, theses, research data, and more—produced by the university since its establishment in 1993.

KUHub Research adheres to the principles of Open Access and FAIR (Findable, Accessible, Interoperable, Reusable), providing access to the bibliographic records, research data, and full texts of research outputs. Equipped with innovative features such as Author Profile pages and enhanced tools, KUHub Research aims to enhance the visibility of researchers both nationally and internationally. KUHub Research aims to preserve and maintain Koç University's valuable academic legacy for future generations while also serving the needs of today’s researchers.

For more information or inquiries please contact openaccess@ku.edu.tr

To learn how to deposit your publications in your KUHub Research profile, watch our video guide: “How to Deposit Publications into KUHub Research?”

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Academic Entities 8079
Research Outputs 26509

Recent Submissions

Access status: Metadata only ,
Single-session versus Hypofractionated Gamma Knife radiosurgery for large parasagittal meningiomas: a retrospective comparative study
(MDPI, 2026-10)
Nazari, Amir Salar
;
School of Medicine
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KUH (Koç University Hospital)
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Yes
;
Veznikli, Mert
Large parasagittal meningiomas (PSMs) present significant surgical challenges due to their proximity to the superior sagittal sinus, and the optimal radiosurgical strategy for large tumors remains debated. This study compared local control (LC) and treatment-related radiological and clinical outcomes after single-session (ss-GKRS) and hypofractionated (hf-GKRS) Gamma Knife radiosurgery in PSMs ≥8 cm3. Methods: Eighty-seven patients with 87 radiologically confirmed PSMs treated between April 2006 and February 2025 were reviewed. Patients with histopathologically confirmed atypical or anaplastic meningiomas or imaging features suspicious for higher-grade disease were excluded. Fifty-nine patients underwent ss-GKRS and 28 received hf-GKRS. The median tumor volume was 11.2 cm3 (range 8–37.8 cm3). LC was defined as tumor volume regression or stability on serial MRI. Radiological complications were defined as new or worsening peritumoral T2 hyperintensity, and clinical complications as new neurological symptoms. Time to local progression was compared using the log-rank test, with an exploratory volume cut point from ROC analysis. Results: The median follow-up was 62 months (range 12–186 months). Overall crude LC was 93.1%, including 89.8% (53/59) after ss-GKRS and 100% (28/28; exact 95% CI 87.7–100%) after hf-GKRS. All six local progressions occurred in the ss-GKRS group; however, freedom from local progression did not differ significantly between the groups (log-rank p = 0.137). In an exploratory volume-stratified analysis, LC was 100% in both groups for tumors ≤11.75 cm3. Among tumors >11.75 cm3, crude LC was 71.4% (15/21) after ss-GKRS and 100% (16/16) after hf-GKRS (Fisher exact p = 0.027). Post-treatment radiological edema occurred in 39.0% after ss-GKRS versus 25.0% after hf-GKRS (p = 0.235). Conclusions: Overall freedom from local progression did not differ significantly between ss-GKRS and hf-GKRS. No local failures were observed after hf-GKRS during its shorter available follow-up, including among tumors >11.75 cm3 in an exploratory analysis. These findings support hf-GKRS as a feasible treatment option for selected larger PSMs, but neither treatment superiority nor a definitive tumor-volume threshold can be established from this retrospective cohort.
Access status: Metadata only ,
The landscape of GPU-Centric communication
(Association for Computing Machinery, 2026-09-22)
Sağbili, Doğan
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Turimbetov, İlyas
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Graduate School of Sciences and Engineering
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Department of Computer Engineering
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Yes
In recent years, GPUs have become the preferred accelerators for HPC and ML applications due to their parallelism and high memory bandwidth. While GPUs boost computation, inter-GPU communication can create scalability bottlenecks, especially as the number of GPUs per node and cluster grows. Traditionally, the CPU managed multi-GPU communication, but advancements in GPU-centric communication now challenge this CPU dominance by reducing its involvement, granting GPUs more autonomy in communication tasks, and addressing mismatches in multi-GPU communication and computation. This article provides a landscape of GPU-centric communication, focusing on vendor mechanisms and user-level library supports. It aims to clarify the complexities and diverse options in this field, define the terminology, and categorize existing approaches within and across nodes. The article discusses vendor-provided mechanisms for communication and memory management in multi-GPU execution and reviews major communication libraries, their benefits, challenges, and performance insights. Then, it explores key research paradigms, future outlooks, and open research questions. By extensively describing GPU-centric communication techniques across the software and hardware stacks, we provide researchers, programmers, engineers, and library designers insights on how to exploit multi-GPU systems at their best.
Mahmoudi Azar, Sepehr
PhD Student
Yaşar, İrem
Undergraduate Student
Morkavuk, Şevket Barış
Doctor