Multithreaded programs execute nondeterministically on conventional architectures and operating systems. This complicates many tasks, including debugging and testing. Deterministic multithreading (DMT) makes the output of a multithreaded program depend on its inputs only, which can totally solve the above problem. However, current DMT implementations suffer from a common inefficiency: they use frequent global barriers to enforce a deterministic ordering on memory accesses. In this paper, we eliminate that inefficiency using an execution model we call deterministic lazy release consistency (DLRC). Our execution model uses the Kendo algorithm to enforce a deterministic ordering on synchronization, and it uses a deterministic version of the lazy release consistency memory model to propagate memory updates across threads. Our approach guarantees that programs execute deterministically even when they contain data races. We implemented a DMT system based on these ideas (RFDet) and evaluated it using 17 parallel applications. Our implementation targets C/C++ programs that use POSIX threads. Results show that RFDet gains nearly 2x speedup compared with DThreads-a start-of-the-art DMT system.

With the development of the economy, products are significantly enriched, and uncertainty has been their inherent quality. The probabilistic dynamic skyline (PDS) query is a powerful tool for customers to use in selecting products according to their preferences. However, this query suffers several limitations: it requires the specification of a probabilistic threshold, which reports undesirable results and disregards important results; it only focuses on the objects that have large dynamic skyline probabilities; additionally, the results are not stable. To address this concern, in this paper, we formulate an uncertain dynamic skyline (UDS) query over a probabilistic product set. Furthermore, we propose effective pruning strategies for the UDS query, and integrate them into effective algorithms. In addition, a novel query type, namely the top k favorite probabilistic products (TFPP) query, is presented. The TFPP query is utilized to select k products which can meet the needs of a customer set at the maximum level. To tackle the TFPP query, we propose a TFPP algorithm and its efficient parallelization. Extensive experiments with a variety of experimental settings illustrate the efficiency and effectiveness of our proposed algorithms.Index Terms-Data management, dynamic skyline query, parallel algorithm, probabilistic products. ! • The authors are with the . Her research interests include parallel computing and data management. Kenli Li received the PhD degree in computer science from Huazhong University of Science and Technology, China, in 2003. He is currently a full professor of computer science and technology at Hunan University. His major research includes parallel computing, cloud computing, and DNA computing. He has published more than 110 papers in international conferences and journals, such as IEEE-TC, IEEE-TPDS, JPDC. Guoqing Xiao is currently pursuing his Ph.D. degree in Keqin Li is a SUNY Distinguished Professor of computer science. His current research interests include parallel computing and distributed computing. He has published over 350 journal articles, book chapters, and refereed conference papers, and has received several best paper awards. He is currently or has served on the editorial boards of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Computers, IEEE Transactions on Cloud Computing. He is an IEEE Fellow.

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