This paper proposes predictive log-synchronization, an alternative paradigm to the software transactional memory approach for simplifying the design of concurrent data structures. Predictive log-synchronization simplifies concurrent programming and program verification by requiring programmers to write only specialized sequential code. This sequential code is then automatically transformed into a non-blocking concurrent program in which threads coordinate all data structure operations via a shared lock-controlled log. The non-blocking progress property is achieved by having threads that fail to acquire the lock predict the outcome of their operations by reading the log and state and computing the effect of these operations without modifying the actual data structure.Log-synchronization is founded on the belief (at this point unsubstantiated by statistical data) that in many concurrent data structures used in real-world applications, the ratio of high level operations that modify the structure to ones that simply read it, greatly favors read-only operations, and what's more, that many natural data structures have inherent sequential bottlenecks limiting the concurrency among operations that modify the structure. It follows that delegating all data structure modifications to a single lock-controlled thread at a time will not significantly harm the throughput of modifying operations. Moreover, as we show, it can boost read-only throughput by significantly reducing the overhead of coordination among concurrent operations, and provides a way to simplify concurrent data structures.Initial experimental testing using a Java-based implementation of predictive log-synchronization showed that a log-synchronized concurrent red-black tree is up to five times faster than a simple lock-based one. This paper presents our current understanding of the advantages, drawbacks, and scope of predictive log-synchronization.

We present the first lock-free implementation of an extensible hash table running on current architectures. Our algorithm provides concurrent insert, delete, and find operations with an expected O (1) cost. It consists of very simple code, easily implementable using only load, store, and compare-and-swap operations. The new mathematical structure at the core of our algorithm is recursive split-ordering , a way of ordering elements in a linked list so that they can be repeatedly “split” using a single compare-and-swap operation. Metaphorically speaking, our algorithm differs from prior known algorithms in that extensibility is derived by “moving the buckets among the items” rather than “the items among the buckets.” Though lock-free algorithms are expected to work best in multiprogrammed environments, empirical tests we conducted on a large shared memory multiprocessor show that even in non-multiprogrammed environments, the new algorithm performs as well as the most efficient known lock-based resizable hash-table algorithm, and in high load cases it significantly outperforms it.

This paper shows for the first time that elimination, a scaling technique formerly applied only to counters and LIFO structures, can be applied to FIFO data structures, specifically, to linearizable FIFO queues. We show how to transform existing nonscalable FIFO queue implementations into scalable implementations using the elimination technique, while preserving lock-freedom and linearizablity.We apply our transformation to the FIFO queue algorithm of Michael and Scott, which is included in the Java™ Concurrency Package. Empirical evaluation on a state-of-the-art CMT multiprocessor chip shows that by using elimination as a backoff technique for the Michael and Scott queue algorithm, we can achieve comparable performance at low loads, and improved scalability as load increases.

We present the first lock-free implementation of an extensible hash table running on current architectures. It provides concurrent insert, delete, and search operations with an expected O(1) cost. It consists of very simple code, easily implementable using only load, store, and compare-and-swap operations. The new mathematical structure at the core of our algorithm is recursive split-ordering, a way of ordering elements in a linked list so that they can be repeatedly "split" using a single compare-and-swap operation. Empirical tests conducted on a large shared memory multiprocessor show that even in non-multiprogrammed environments, the new algorithm significantly outperforms the most efficient known lock-based algorithm at all concurrency levels, exhibiting up to four times higher throughput at peak load. The incremental nature of our algorithm makes it well suited for real-time applications, as it offers predictable performance without unexpected breaks for resizing.