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Your search keyword '"Kaya, Kamer"' showing total 19 results
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19 results on '"Kaya, Kamer"'

1. Open Problems in (Hyper)Graph Decomposition

Author

Ajwani, Deepak, Bisseling, Rob H., Casel, Katrin, Çatalyürek, Ümit V., Chevalier, Cédric, Chudigiewitsch, Florian, Faraj, Marcelo Fonseca, Fellows, Michael, Gottesbüren, Lars, Heuer, Tobias, Karypis, George, Kaya, Kamer, Lacki, Jakub, Langguth, Johannes, Li, Xiaoye Sherry, Mayer, Ruben, Meintrup, Johannes, Mizutani, Yosuke, Pellegrini, François, Petrini, Fabrizio, Rosamond, Frances, Safro, Ilya, Schlag, Sebastian, Schulz, Christian, Sharma, Roohani, Strash, Darren, Sullivan, Blair D., Uçar, Bora, and Yzelman, Albert-Jan

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Large networks are useful in a wide range of applications. Sometimes problem instances are composed of billions of entities. Decomposing and analyzing these structures helps us gain new insights about our surroundings. Even if the final application concerns a different problem (such as traversal, finding paths, trees, and flows), decomposing large graphs is often an important subproblem for complexity reduction or parallelization. This report is a summary of discussions that happened at Dagstuhl seminar 23331 on "Recent Trends in Graph Decomposition" and presents currently open problems and future directions in the area of (hyper)graph decomposition.

Published
2023

2. Boosting Graph Embedding on a Single GPU

Author

Aljundi, Amro Alabsi, Akyıldız, Taha Atahan, and Kaya, Kamer

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract

Graphs are ubiquitous, and they can model unique characteristics and complex relations of real-life systems. Although using machine learning (ML) on graphs is promising, their raw representation is not suitable for ML algorithms. Graph embedding represents each node of a graph as a d-dimensional vector which is more suitable for ML tasks. However, the embedding process is expensive, and CPU-based tools do not scale to real-world graphs. In this work, we present GOSH, a GPU-based tool for embedding large-scale graphs with minimum hardware constraints. GOSH employs a novel graph coarsening algorithm to enhance the impact of updates and minimize the work for embedding. It also incorporates a decomposition schema that enables any arbitrarily large graph to be embedded with a single GPU. As a result, GOSH sets a new state-of-the-art in link prediction both in accuracy and speed, and delivers high-quality embeddings for node classification at a fraction of the time compared to the state-of-the-art. For instance, it can embed a graph with over 65 million vertices and 1.8 billion edges in less than 30 minutes on a single GPU., Comment: 12 pages, 11 tables, 6 figures, submitted for publication at Special Section on Parallel and Distributed Computing Techniques for AI, ML, and DL

Published
2021

9. Determination of Pancreatic Beta Cell Reserve in Antidepressant Users and Evaluation of Its Relationship with Metabolic Parameters: A Cross-Sectional Study.

Author

KAYA, Kamer, GÜROK, Mehmet Gürkan, KILINÇ, Faruk, ÜSTÜNDAĞ, Bilal, ÖRÜM, Mehmet Hamdi, and ATMACA, Murad

Subjects
PANCREATIC beta cells, ANTIDEPRESSANTS, METABOLISM, INSULIN resistance, BIOCHEMISTRY
Abstract

Copyright of Firat Universitesi Sağlik Bilimleri Tip Dergisi is the property of Firat Universitesiu, Saglik Bilimleri Enstitusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024

14. Fast and Error-Adaptive Influence Maximization Based on Count-Distinct Sketches

Author

Gokturk, Gokhan and Kaya, Kamer

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Computer Science - Social and Information Networks, Data Structures and Algorithms (cs.DS)
Abstract

Influence maximization (IM) is the problem of finding a seed vertex set that maximizes the expected number of vertices influenced under a given diffusion model. Due to the NP-Hardness of finding an optimal seed set, approximation algorithms are frequently used for IM. In this work, we describe a fast, error-adaptive approach that leverages Count-Distinct sketches and hash-based fused sampling. To estimate the number of influenced vertices throughout a diffusion, we use per-vertex Flajolet-Martin sketches where each sketch corresponds to a sampled subgraph. To efficiently simulate the diffusions, the reach-set cardinalities of a single vertex are stored in memory in a consecutive fashion. This allows the proposed algorithm to estimate the number of influenced vertices in a single step for simulations at once. For a faster IM kernel, we rebuild the sketches in parallel only after observing estimation errors above a given threshold. Our experimental results show that the proposed algorithm yields high-quality seed sets while being up to 119x faster than a state-of-the-art approximation algorithm. In addition, it is up to 62x faster than a sketch-based approach while producing seed sets with 3%-12% better influence scores, Comment: 12 pages. Sent to IEEE Transactions on Knowledge and Data Engineering as a regular paper

Published
2022

15. Machine Learning-Based Load Distribution And Balancing In Heterogeneous Database Management Systems

Author

Abdennebi, Anes, Elakas, Anil, Tasyaran, Fatih, Ozturk, Erdinc, Kaya, Kamer, and Yildirim, Sinan

Abstract

For dynamic and continuous data analysis, conventional OLTP systems are slow in performance. Today's cutting-edge high-performance computing hardware, such as GPUs, has been used as accelerators for data analysis tasks, which traditionally leverage CPUs on classical database management systems (DBMS). When CPUs and GPUs are used together, the architectural heterogeneity, that is, leveraging hardware with different performance characteristics jointly, creates complex problems that need careful treatment for performance optimization. Load distribution and balancing are crucial problems for DBMSs working on heterogeneous architectures. In this work, focusing on a hybrid, CPU-GPU database management system to process users' queries, we propose heuristical and machine-learning-based (ML-based) load distribution and balancing models. In more detail, we employ multiple linear regression (MLR), random forest (RF), and Adaboost (Ada) models to dynamically decide the processing unit for each incoming query based on the response time predictions on both CPU and GPU. The ML-based models outperformed the other algorithms, as well as the CPU and GPU-only running modes with up to 27%, 29%, and 40%, respectively, in overall performance (response time) while answering intense real-life working scenarios. Finally, we propose to use a hybrid load-balancing model that would be more efficient than the models we tested in this work.

Published
2022

17. Boosting Graph Embedding on a Single GPU.

Author

Aljundi, Amro Alabsi, Akyildiz, Taha Atahan, and Kaya, Kamer

Subjects
GRAPH algorithms, MACHINE learning, GRAPHICS processing units, CLASSIFICATION algorithms, CHARTS, diagrams, etc.
Abstract

Graphs are ubiquitous, and they can model unique characteristics and complex relations of real-life systems. Although using machine learning (ML) on graphs is promising, their raw representation is not suitable for ML algorithms. Graph embedding represents each node of a graph as a $d$ d -dimensional vector which is more suitable for ML tasks. However, the embedding process is expensive, and CPU-based tools do not scale to real-world graphs. In this work, we present GOSH, a GPU-based tool for embedding large-scale graphs with minimum hardware constraints. GOSH employs a novel graph coarsening algorithm to enhance the impact of updates and minimize the work for embedding. It also incorporates a decomposition schema that enables any arbitrarily large graph to be embedded with a single GPU. As a result, GOSH sets a new state-of-the-art in link prediction both in accuracy and speed, and delivers high-quality embeddings for node classification at a fraction of the time compared to the state-of-the-art. For instance, it can embed a graph with over 65 million vertices and 1.8 billion edges in less than 30 minutes on a single GPU. [ABSTRACT FROM AUTHOR]

Published
2022
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19. Second and fourth (2D:4D) digit ratio in heroin and cannabis addicted patients.

Author

Gurok, Mehmet Gurkan, Yıldız, Sevler, Bakış Aksoy, Dilek, Kılıçaslan, Aslı Kazğan, Tabara, Muhammed Fatih, Yılmaz, Tuba, Kaya, Kamer, Danacı Keleş, Denizhan, Karakaş, Şahin, Şimşek, Düzgün, and Atmaca, Murad

Abstract

Abstract The etiology of addiction has not yet been fully elucidated. The ratio between the length of the second and fourth fingers (2D:4D ratio) has been linked with prenatal androgen concentrations, but also with addictive behvaiors. Aim: The present study aimed to evaluate the differences of 2D:4D ratio of individuals with cannabis and heroin addiction by examining them together with the control group. A total of sixty two male patients (33 opiate use disorder and 29 cannabis use disorder) with substance use disorder and the twenty-nine healthy controls were included in the present investigation. We obtained the lengths of 2D and 4D of the subjects by using sensitive calipers and calculated the 2D:4D. Heroin-addicted patients had lower 2D:4D ratio in in the right hand (significant difference between control group) (p < 0.001), there was no significant difference found between heroin-cannabis (p = 0.242) and control-cannabis 2D:4D ratios (p < 0.06). In the left hand, it was significant between the heroin-control groups (p < 0.037) and the cannabis-control groups (p < 0.023), while it was not significant between the heroin-cannabis groups (p = 1). In conclusion, we suggest that heroin-and cannabis addicted patients seem to have a lower ratio of 2D:4D compared to healthy control subjects. Our findings can be considered promising as to whether prenatal hormonal factors are important in the etiopathogenesis of addiction. [ABSTRACT FROM AUTHOR]

Published
2023
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