Composing Relaxed Transactions

As the classical transactional abstraction is sometimes considered too restrictive in leveraging parallelism, a lot of work has been devoted to devising relaxed transactional models with the goal of improving concurrency. Nevertheless, the quest for improving concurrency has somehow led to neglect one of the most appealing aspects of transactions: software composition, namely, the ability to develop pieces of software independently and compose them into applications that behave correctly in the face of concurrency. Indeed, a closer look at relaxed transactional models reveals that they do jeopardize composition, raising the fundamental question whether it is at all possible to devise such models while preserving composition. This paper shows that the answer is positive. We present outheritance, a necessary and sufficient condition for a (potentially relaxed) transactional memory to support composition. Basically, outheritance requires child transactions to pass their conflict information to their parent transaction, which in turn maintains this information until commit time. Concrete instantiations of this idea have been used before, classical transactions being the most prevalent example, but we believe to be the first to capture this as a general principle as well as to prove that it is, strictly speaking, equivalent to ensuring composition. We illustrate the benefits of outheritance using elastic transactions and show how they can satisfy outheritance and provide composition without hampering concurrency. We leverage this to present a new (transactional) Java package, a composable alternative to the concurrency package of the JDK, and evaluate efficiency through an implementation that speeds up state of the art software transactional memory implementations (TL2, LSA, SwissTM) by almost a factor of 3

Practical Dynamic Transactional Data Structures

Multicore programming presents the challenge of synchronizing multiple threads. Traditionally, mutual exclusion locks are used to limit access to a shared resource to a single thread at a time. Whether this lock is applied to an entire data structure, or only a single element, the pitfalls of lock-based programming persist. Deadlock, livelock, starvation, and priority inversion are some of the hazards of lock-based programming that can be avoided by using non-blocking techniques. Non-blocking data structures allow scalable and thread-safe access to shared data by guaranteeing, at least, system-wide progress. In this work, we present the first wait-free hash map which allows a large number of threads to concurrently insert, get, and remove information. Wait-freedom means that all threads make progress in a finite amount of time --- an attribute that can be critical in real-time environments. We only use atomic operations that are provided by the hardware; therefore, our hash map can be utilized by a variety of data-intensive applications including those within the domains of embedded systems and supercomputers. The challenges of providing this guarantee make the design and implementation of wait-free objects difficult. As such, there are few wait-free data structures described in the literature; in particular, there are no wait-free hash maps. It often becomes necessary to sacrifice performance in order to achieve wait-freedom. However, our experimental evaluation shows that our hash map design is, on average, 7 times faster than a traditional blocking design. Our solution outperforms the best available alternative non-blocking designs in a large majority of cases, typically by a factor of 15 or higher. The main drawback of non-blocking data structures is that only one linearizable operation can be executed by each thread, at any one time. To overcome this limitation we present a framework for developing dynamic transactional data containers. Transactional containers are those that execute a sequence of operations atomically and in such a way that concurrent transactions appear to take effect in some sequential order. We take an existing algorithm that transforms non-blocking sets into static transactional versions (LFTT), and we modify it to support maps. We implement a non-blocking transactional hash map using this new approach. We continue to build on LFTT by implementing a lock-free vector using a methodology to allow LFTT to be compatible with non-linked data structures. A static transaction requires all operands and operations to be specified at compile-time, and no code may be executed between transactions. These limitations render static transactions impractical for most use cases. We modify LFTT to support dynamic transactions, and we enhance it with additional features. Dynamic transactions allow operands to be specified at runtime rather than compile-time, and threads can execute code between the data structure operations of a transaction. We build a framework for transforming non-blocking containers into dynamic transactional data structures, called Dynamic Transactional Transformation (DTT), and provide a library of novel transactional containers. Our library provides the wait-free progress guarantee and supports transactions among multiple data structures, whereas previous work on data structure transactions has been limited to operating on a single container. Our approach is 3 times faster than software transactional memory, and its performance matches its lock-free transactional counterpart

High-Performance Composable Transactional Data Structures

Exploiting the parallelism in multiprocessor systems is a major challenge in the post ``power wall\u27\u27 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\u27s 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\u27 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