Exploiting the parallelism in multiprocessor systems is a major challenge in the post ``power wall'' era. Programming for multicore demands a change in the way we design and use fundamental data structures. Concurrent data structures allow scalable and thread-safe accesses to shared data. They provide operations that appear to take effect atomically when invoked individually. A main obstacle to the practical use of concurrent data structures is their inability to support composable operations, i.e., to execute multiple operations atomically in a transactional manner. The problem stems from the inability of concurrent data structure to ensure atomicity of transactions composed from operations on a single or multiple data structures instances. This greatly hinders software reuse because users can only invoke data structure operations in a limited number of ways. Existing solutions, such as software transactional memory (STM) and transactional boosting, manage transaction synchronization in an external layer separated from the data structure's own thread-level concurrency control. Although this reduces programming effort, it leads to significant overhead associated with additional synchronization and the need to rollback aborted transactions. In this dissertation, I address the practicality and efficiency concerns by designing, implementing, and evaluating high-performance transactional data structures that facilitate the development of future highly concurrent software systems. Firstly, I present two methodologies for implementing high-performance transactional data structures based on existing concurrent data structures using either lock-based or lock-free synchronizations. For lock-based data structures, the idea is to treat data accessed by multiple operations as resources. The challenge is for each thread to acquire exclusive access to desired resources while preventing deadlock or starvation. Existing locking strategies, like two-phase locking and resource hierarchy, suffer from performance degradation under heavy contention, while lacking a desirable fairness guarantee. To overcome these issues, I introduce a scalable lock algorithm for shared-memory multiprocessors addressing the resource allocation problem. It is the first multi-resource lock algorithm that guarantees the strongest first-in, first-out (FIFO) fairness. For lock-free data structures, I present a methodology for transforming them into high-performance lock-free transactional data structures without revamping the data structures' original synchronization design. My approach leverages the semantic knowledge of the data structure to eliminate the overhead of false conflicts and rollbacks. Secondly, I apply the proposed methodologies and present a suite of novel transactional search data structures in the form of an open source library. This is interesting not only because the fundamental importance of search data structures in computer science and their wide use in real world programs, but also because it demonstrate the implementation issues that arise when using the methodologies I have developed. This library is not only a compilation of a large number of fundamental data structures for multiprocessor applications, but also a framework for enabling composable transactions, and moreover, an infrastructure for continuous integration of new data structures. By taking such a top-down approach, I am able to identify and consider the interplay of data structure interface operations as a whole, which allows for scrutinizing their commutativity rules and hence opens up possibilities for design optimizations. Lastly, I evaluate the throughput of the proposed data structures using transactions with randomly generated operations on two difference hardware systems. To ensure the strongest possible competition, I chose the best performing alternatives from state-of-the-art locking protocols and transactional memory systems in the literature. The results show that it is straightforward to build efficient transactional data structures when using my multi-resource lock as a drop-in replacement for transactional boosted data structures. Furthermore, this work shows that it is possible to build efficient lock-free transactional data structures with all perceived benefits of lock-freedom and with performance far better than generic transactional memory systems.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Zhang, Deli, "High-Performance Composable Transactional Data Structures" (2016). Electronic Theses and Dissertations. 5069.
Restricted to the UCF community until August 2016; it will then be open access.