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\n\n \n \n \n \n \n A Prediction Framework for Fast Sparse Triangular Solves.\n \n \n \n\n\n \n Ahmad, N.; Yilmaz, B.; and Unat, D.\n\n\n \n\n\n\n In
Euro-Par, 2020. \n
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@inproceedings{Ahmad2020APF,\n author = "Ahmad, Najeeb and Yilmaz, Buse and Unat, D.",\n title = "A Prediction Framework for Fast Sparse Triangular Solves",\n booktitle = "Euro-Par",\n year = "2020",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n RESDN: A Novel Metric and Method for Energy Efficient Routing in Software Defined Networks.\n \n \n \n\n\n \n Assefa, B. G.; and Özkasap, Ö.\n\n\n \n\n\n\n
IEEE Transactions on Network and Service Management, 17: 736-749. 2020.\n
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@article{Assefa2020RESDNAN,\n author = {Assefa, Beakal Gizachew and {\\"O}zkasap, {\\"O}znur},\n title = "RESDN: A Novel Metric and Method for Energy Efficient Routing in Software Defined Networks",\n journal = "IEEE Transactions on Network and Service Management",\n year = "2020",\n volume = "17",\n pages = "736-749",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading.\n \n \n \n\n\n \n Ali, F.; Aloqaily, M.; Alfandi, O.; and Ozkasap, O.\n\n\n \n\n\n\n
Computer, 53: 56-65. 2020.\n
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@article{Ali2020CyberphysicalBP,\n author = "Ali, Faizan and Aloqaily, M. and Alfandi, O. and Ozkasap, O.",\n title = "Cyberphysical Blockchain-Enabled Peer-to-Peer Energy Trading",\n journal = "Computer",\n year = "2020",\n volume = "53",\n pages = "56-65",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n Adaptive Level Binning: A New Algorithm for Solving Sparse Triangular Systems.\n \n \n \n\n\n \n Yilmaz, B.; Sipahiogrlu, B.; Ahmad, N.; and Unat, D.\n\n\n \n\n\n\n
Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region. 2020.\n
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@article{Yilmaz2020AdaptiveLB,\n author = "Yilmaz, Buse and Sipahiogrlu, Bugrra and Ahmad, Najeeb and Unat, D.",\n title = "Adaptive Level Binning: A New Algorithm for Solving Sparse Triangular Systems",\n journal = "Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region",\n year = "2020",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n Decentralized Utility- and Locality-Aware Replication for Heterogeneous DHT-Based P2P Cloud Storage Systems.\n \n \n \n\n\n \n Hassanzadeh-Nazarabadi, Y.; Küpçü, A.; and Özkasap, Ö.\n\n\n \n\n\n\n
IEEE Transactions on Parallel and Distributed Systems, 31: 1183-1193. 2020.\n
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@article{HassanzadehNazarabadi2020DecentralizedUA,\n author = {Hassanzadeh-Nazarabadi, Yahya and K{\\"u}p{\\c{c}}{\\"u}, Alptekin and {\\"O}zkasap, {\\"O}znur},\n title = "Decentralized Utility- and Locality-Aware Replication for Heterogeneous DHT-Based P2P Cloud Storage Systems",\n journal = "IEEE Transactions on Parallel and Distributed Systems",\n year = "2020",\n volume = "31",\n pages = "1183-1193",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n \n Tiling-Based Programming Model for Structured Grids on GPU Clusters.\n \n \n \n \n\n\n \n Bastem, B.; and Unat, D.\n\n\n \n\n\n\n In
Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, pages 43–51, New York, NY, USA, 2020. Association for Computing Machinery\n
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@inproceedings{10.1145/3368474.3368485,\n author = "Bastem, Burak and Unat, Didem",\n title = "Tiling-Based Programming Model for Structured Grids on GPU Clusters",\n year = "2020",\n isbn = "9781450372367",\n publisher = "Association for Computing Machinery",\n address = "New York, NY, USA",\n url = "https://doi.org/10.1145/3368474.3368485",\n doi = "10.1145/3368474.3368485",\n abstract = "Currently, more than 25\\% of supercomputers employ GPUs due to their massively parallel and power-efficient architectures. However, programming GPUs efficiently in a large scale system is a demanding task not only for computational scientists but also for programming experts as multi-GPU programming requires managing distinct address spaces, generating GPU-specific code and handling inter-device communication. To ease the programming effort, we propose a tiling-based high-level GPU programming model for structured grid problems. The model abstracts data decomposition, memory management and generation of GPU specific code, and hides all types of data transfer overheads. We demonstrate the effectiveness of the programming model on a heat simulation and a real-life cardiac modeling on a single GPU, on a single node with multiple-GPUs and multiple-nodes with multiple-GPUs. We also present performance comparisons under different hardware and software configurations. The results show that the programming model successfully overlaps communication and provides good speedup on 192 GPUs.",\n booktitle = "Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region",\n pages = "43–51",\n keywords = "multi-GPU,GPU programming,GPU streams,tiling,communication overlap,GPU cluster,SAI"\n}\n\n
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\n Currently, more than 25% of supercomputers employ GPUs due to their massively parallel and power-efficient architectures. However, programming GPUs efficiently in a large scale system is a demanding task not only for computational scientists but also for programming experts as multi-GPU programming requires managing distinct address spaces, generating GPU-specific code and handling inter-device communication. To ease the programming effort, we propose a tiling-based high-level GPU programming model for structured grid problems. The model abstracts data decomposition, memory management and generation of GPU specific code, and hides all types of data transfer overheads. We demonstrate the effectiveness of the programming model on a heat simulation and a real-life cardiac modeling on a single GPU, on a single node with multiple-GPUs and multiple-nodes with multiple-GPUs. We also present performance comparisons under different hardware and software configurations. The results show that the programming model successfully overlaps communication and provides good speedup on 192 GPUs.\n
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\n\n \n \n \n \n \n \n Adaptive Level Binning: A New Algorithm for Solving Sparse Triangular Systems.\n \n \n \n \n\n\n \n Yılmaz, B.; Sipahioğrlu, B.; Ahmad, N.; and Unat, D.\n\n\n \n\n\n\n In
Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, pages 188–198, New York, NY, USA, 2020. Association for Computing Machinery\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3368474.3368486,\n author = "Y\\i{}lmaz, Buse and Sipahio\\u{g}rlu, Bu\\u{g}rra and Ahmad, Najeeb and Unat, Didem",\n title = "Adaptive Level Binning: A New Algorithm for Solving Sparse Triangular Systems",\n year = "2020",\n isbn = "9781450372367",\n publisher = "Association for Computing Machinery",\n address = "New York, NY, USA",\n url = "https://doi.org/10.1145/3368474.3368486",\n doi = "10.1145/3368474.3368486",\n abstract = "Sparse triangular solve (SpTRSV) is an important scientific kernel used in several applications such as preconditioners for Krylov methods. Parallelizing SpTRSV on multi-core systems is challenging since it exhibits limited parallelism due to computational dependencies and introduces high parallelization overhead due to finegrained and unbalanced nature of workloads. We propose a novel method, named Adaptive Level Binning (ALB), that addresses these challenges by eliminating redundant synchronization points and adapting the work granularity with an efficient load balancing strategy. Similar to the commonly used level-set methods for solving SpTRSV, ALB constructs level-sets of rows, where each level can be computed in parallel. Differently, ALB bins rows to levels adaptively and reduces redundant dependencies between rows. On an Intel® Xeon® Gold 6148 processor and NVIDIA® Tesla V100 GPU, ALB obtains 1.83x speedup on average and up to 5.28x speedup over Intel MKL and, over NVIDIA cuSPARSE, an average speedup of 2.80x and a maximum speedup of 39.40x for 29 matrices selected from Suite Sparse Matrix Collection.",\n booktitle = "Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region",\n pages = "188–198",\n keywords = "sparse triangular solvers,CPU,fine-grained parallelism,level-set,SAI"\n}\n\n
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\n Sparse triangular solve (SpTRSV) is an important scientific kernel used in several applications such as preconditioners for Krylov methods. Parallelizing SpTRSV on multi-core systems is challenging since it exhibits limited parallelism due to computational dependencies and introduces high parallelization overhead due to finegrained and unbalanced nature of workloads. We propose a novel method, named Adaptive Level Binning (ALB), that addresses these challenges by eliminating redundant synchronization points and adapting the work granularity with an efficient load balancing strategy. Similar to the commonly used level-set methods for solving SpTRSV, ALB constructs level-sets of rows, where each level can be computed in parallel. Differently, ALB bins rows to levels adaptively and reduces redundant dependencies between rows. On an Intel® Xeon® Gold 6148 processor and NVIDIA® Tesla V100 GPU, ALB obtains 1.83x speedup on average and up to 5.28x speedup over Intel MKL and, over NVIDIA cuSPARSE, an average speedup of 2.80x and a maximum speedup of 39.40x for 29 matrices selected from Suite Sparse Matrix Collection.\n
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\n\n \n \n \n \n \n \n ComScribe: Identifying Intra-node GPU Communication.\n \n \n \n \n\n\n \n Akhtar, P.; Qararyah, F. M.; and Unat, D.\n\n\n \n\n\n\n In
Benchmarking, Measuring, and Optimizing - Third BenchCouncil Internationa Symposium, Bench 2020, Virtual Event, November 15-16, 2020, Revised Selected Papers, 2020. \n
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@inproceedings{DBLP:conf/bench/AkhtarTQU20,\n author = "Akhtar, Palwisha and Qararyah, Fareed Mohammad and Unat, Didem",\n title = "ComScribe: Identifying Intra-node {GPU} Communication",\n booktitle = "Benchmarking, Measuring, and Optimizing - Third BenchCouncil Internationa Symposium, Bench 2020, Virtual Event, November 15-16, 2020, Revised Selected Papers",\n year = "2020",\n url = "https://doi.org/10.1007/978-3-030-71058-3\\\\_10",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n \n Can learned frame prediction compete with block motion compensation for video coding?.\n \n \n \n \n\n\n \n Sulun, S.; and Tekalp, A. M.\n\n\n \n\n\n\n
Signal, Image and Video Processing, 15(2): 401–410. Aug 2020.\n
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@article{Sulun_2020,\n author = "Sulun, Serkan and Tekalp, A. Murat",\n title = "Can learned frame prediction compete with block motion compensation for video coding?",\n volume = "15",\n issn = "1863-1711",\n url = "http://dx.doi.org/10.1007/s11760-020-01751-y",\n doi = "10.1007/s11760-020-01751-y",\n number = "2",\n journal = "Signal, Image and Video Processing",\n publisher = "Springer Science and Business Media LLC",\n year = "2020",\n month = "Aug",\n pages = "401–410",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n \n Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression.\n \n \n \n \n\n\n \n Soner, B.; Ulusoy, E.; Tekalp, A. M.; and Urey, H.\n\n\n \n\n\n\n
IEEE Transactions on Image Processing, 29: 4505–4515. 2020.\n
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@article{Soner_2020,\n author = "Soner, Burak and Ulusoy, Erdem and Tekalp, A. Murat and Urey, Hakan",\n title = "Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression",\n volume = "29",\n issn = "1941-0042",\n url = "http://dx.doi.org/10.1109/TIP.2020.2972112",\n doi = "10.1109/tip.2020.2972112",\n journal = "IEEE Transactions on Image Processing",\n publisher = "Institute of Electrical and Electronics Engineers (IEEE)",\n year = "2020",\n pages = "4505–4515",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n EdgeKV: Decentralized, scalable, and consistent storage for the edge.\n \n \n \n\n\n \n Sonbol, K.; Özkasap, Ö.; Al-Oqily, I.; and Aloqaily, M.\n\n\n \n\n\n\n 2020.\n
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@misc{sonbol2020edgekv,\n author = {Sonbol, Karim and {\\"O}zkasap, {\\"O}znur and Al-Oqily, Ibrahim and Aloqaily, Moayad},\n title = "EdgeKV: Decentralized, scalable, and consistent storage for the edge",\n year = "2020",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n Demo: Skip Graph Middleware Implementation.\n \n \n \n\n\n \n Hassanzadeh-Nazarabadi, Y.; Nayal, N.; Hamdan, S. S.; Sahin, A. U.; Özkasap, Ö.; and Küpçü, A.\n\n\n \n\n\n\n In
International Symposium on Reliable Distributed Systems, SRDS 2020, Shanghai, China, September 21-24, 2020, 2020. \n
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@inproceedings{DBLP:conf/srds/Hassanzadeh-Nazarabadi20,\n author = {Hassanzadeh{-}Nazarabadi, Yahya and Nayal, Nazir and Hamdan, Shadi Sameh and Sahin, Ali Utkan and {\\"{O}}zkasap, {\\"{O}}znur and K{\\"{u}}p{\\c{c}}{\\"{u}}, Alptekin},\n title = "Demo: Skip Graph Middleware Implementation",\n booktitle = "International Symposium on Reliable Distributed Systems, {SRDS} 2020, Shanghai, China, September 21-24, 2020",\n year = "2020",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n Demo: A Proof-of-Concept Implementation of Guard Secure Routing Protocol.\n \n \n \n\n\n \n Taheri-Boshrooyeh, S.; Şahin, A. U.; Hassanzadeh-Nazarabadi, Y.; and Özkasap, Ö.\n\n\n \n\n\n\n 2020.\n
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@misc{taheriboshrooyeh2020demo,\n author = {Taheri-Boshrooyeh, Sanaz and {\\c{S}}ahin, Ali Utkan and Hassanzadeh-Nazarabadi, Yahya and {\\"O}zkasap, {\\"O}znur},\n title = "Demo: A Proof-of-Concept Implementation of Guard Secure Routing Protocol",\n year = "2020",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n Joulin: Blockchain-based P2P Energy Trading Using Smart Contracts.\n \n \n \n\n\n \n Perk, B.; Bayraktaroglu, C.; Dogu, E. D.; Ali, F. S.; and Özkasap, Ö.\n\n\n \n\n\n\n In
IEEE Symposium on Computers and Communications, ISCC 2020, Rennes, France, July 7-10, 2020, 2020. \n
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@inproceedings{DBLP:conf/iscc/PerkBDAO20,\n author = {Perk, Berrak and Bayraktaroglu, Can and Dogu, Engin Deniz and Ali, Faizan Safdar and {\\"{O}}zkasap, {\\"{O}}znur},\n title = "Joulin: Blockchain-based {P2P} Energy Trading Using Smart Contracts",\n booktitle = "{IEEE} Symposium on Computers and Communications, {ISCC} 2020, Rennes, France, July 7-10, 2020",\n year = "2020",\n keywords = "SAI"\n}\n\n
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\n\n \n \n \n \n \n \n Privado: Privacy-Preserving Group-Based Advertising Using Multiple Independent Social Network Providers.\n \n \n \n \n\n\n \n Boshrooyeh, S. T.; Küpçü, A.; and Özkasap, Ö.\n\n\n \n\n\n\n
ACM Trans. Priv. Secur.. 2020.\n
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\n\n \n \n Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{10.1145/3386154,\n author = {Boshrooyeh, Sanaz Taheri and K\\"{u}p\\c{c}\\"{u}, Alptekin and \\"{O}zkasap, \\"{O}znur},\n title = "Privado: Privacy-Preserving Group-Based Advertising Using Multiple Independent Social Network Providers",\n year = "2020",\n publisher = "Association for Computing Machinery",\n url = "https://doi.org/10.1145/3386154",\n doi = "10.1145/3386154",\n abstract = "Online Social Networks (OSNs) offer free storage and social networking services through which users can communicate personal information with one another. The personal information of the users collected by the OSN provider comes with privacy problems when being monetized for advertising purposes. To protect user privacy, existing studies propose utilizing data encryption that immediately prevents OSNs from monetizing users data and hence leaves secure OSNs with no convincing commercial model. To address this problem, we propose Privado as a privacy-preserving group-based advertising mechanism to be integrated into secure OSNs to re-empower monetizing ability. Privado is run by N servers, each provided by an independent provider. User privacy is protected against an active malicious adversary controlling N − 1 providers, all the advertisers, and a large fraction of the users. We base our design on the group-based advertising notion to protect user privacy, which is not possible in the personalized variant. Our design also delivers advertising transparency; the procedure of identifying target customers is operated solely by the OSN servers without getting users and advertisers involved. We carry out experiments to examine the advertising running time under various number of servers and group sizes. We also argue about the optimum number of servers with respect to user privacy and advertising running time.",\n journal = "ACM Trans. Priv. Secur.",\n keywords = "online social networks,privacy-preserving advertising,advertising,malicious adversary,active adversary,privacy,Unlinkability,SAI"\n}\n\n
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\n\n\n
\n Online Social Networks (OSNs) offer free storage and social networking services through which users can communicate personal information with one another. The personal information of the users collected by the OSN provider comes with privacy problems when being monetized for advertising purposes. To protect user privacy, existing studies propose utilizing data encryption that immediately prevents OSNs from monetizing users data and hence leaves secure OSNs with no convincing commercial model. To address this problem, we propose Privado as a privacy-preserving group-based advertising mechanism to be integrated into secure OSNs to re-empower monetizing ability. Privado is run by N servers, each provided by an independent provider. User privacy is protected against an active malicious adversary controlling N − 1 providers, all the advertisers, and a large fraction of the users. We base our design on the group-based advertising notion to protect user privacy, which is not possible in the personalized variant. Our design also delivers advertising transparency; the procedure of identifying target customers is operated solely by the OSN servers without getting users and advertisers involved. We carry out experiments to examine the advertising running time under various number of servers and group sizes. We also argue about the optimum number of servers with respect to user privacy and advertising running time.\n
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\n\n \n \n \n \n \n Peer-to-Peer Blockchain based Energy Trading.\n \n \n \n\n\n \n Ali, F. S.; Aloqaily, M.; Alfandi, O.; and Özkasap, Ö.\n\n\n \n\n\n\n
CoRR. 2020.\n
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@article{DBLP:journals/corr/abs-2001-00746,\n author = {Ali, Faizan Safdar and Aloqaily, Moayad and Alfandi, Omar and {\\"{O}}zkasap, {\\"{O}}znur},\n title = "Peer-to-Peer Blockchain based Energy Trading",\n journal = "CoRR",\n year = "2020",\n keywords = "SAI"\n}\n\n
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