Composability of transactions using closed nesting in software transactional memory

With the boom in the development of multi-core machines and the development of multi-threaded applications as such, concurrent programming has gained increasingly more significance than ever before. However, concurrent programming using traditional methods such as locks, mutex and monitors is not easy, as they require a programmer to predetermine the lock management scheme for each case. This approach is error-prone. Besides, it is very difficult to trace the bugs in such programs. Software transactional memory (STM) is a new technology that solves this problem by offering automatic management of locks. As such, in recent years STM has gained a lot of attention in both industry and academia. However, most of the work in STM is restricted to non-nested transactions, while the domain of nested transactions remains largely unexplored. One of the striking features of STM is its ability to support composability of transactions through three types of nesting, namely at nesting, closed nesting and open nesting. In this thesis, we study the complexities involved in designing STM protocols for closed nested transactions. To this end, we extend Imbs and Raynal's STM protocol [1], which is designed for non-nested transactions, to closed nested transactions. We propose several extensions, employing different modes of concurrency for subtransactions in the transaction tree : (i) serial execution (no concurrency) of subtransactions at each level; (ii) pessimistic concurrency control at all nodes; (iii) optimistic concurrency control at all nodes; and (iv) a mixture of optimistic concurrency control at some nodes while pessimistic concurrency control at other nodes in the same transaction tree

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

TMbarrier: speculative barriers using hardware transactional memory

Barrier is a very common synchronization method used in parallel programming. Barriers are used typically to enforce a partial thread execution order, since there may be dependences between code sections before and after the barrier. This work proposes TMbarrier, a new design of a barrier intended to be used in transactional applications. TMbarrier allows threads to continue executing speculatively after the barrier assuming that there are not dependences with safe threads that have not yet reached the barrier. Our design leverages transactional memory (TM) (specifically, the implementation offered by the IBM POWER8 processor) to hold the speculative updates and to detect possible conflicts between speculative and safe threads. Despite the limitations of the best-effort hardware TM implementation present in current processors, experiments show a reduction in wasted time due to synchronization compared to standard barriers.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools

Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and of critical importance in robotic surgical data science. We propose two novel deep learning architectures for automatic segmentation of non-rigid surgical instruments. Both methods take advantage of automated deep-learning-based multi-scale feature extraction while trying to maintain an accurate segmentation quality at all resolutions. The two proposed methods encode the multi-scale constraint inside the network architecture. The first proposed architecture enforces it by cascaded aggregation of predictions and the second proposed network does it by means of a holistically-nested architecture where the loss at each scale is taken into account for the optimization process. As the proposed methods are for real-time semantic labeling, both present a reduced number of parameters. We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets. We compare the proposed architectures against state-of-the-art fully convolutional networks. We validate our methods using existing benchmark datasets, including ex vivo cases with phantom tissue and different robotic surgical instruments present in the scene. Our results show a statistically significant improved Dice Similarity Coefficient over previous instrument segmentation methods. We analyze our design choices and discuss the key drivers for improving accuracy.Comment: Paper accepted at IROS 201