Software Transactional Memory (STM) systems allow developers to more easily exploit multicore architectures by wrapping arbitrary sequential code in transactions that are executed concurrently. In recent years, the performance of STM systems has approached that of hand-tuned data structures through techniques that avoid unnecessary aborts and exploit the semantics of underlying data structures. Despite achieving excellent performance, most STM systems do not fully address the concerns they targeted in the first place: safety, usability, and generality. In particular, these systems place restrictions on the data types that may be updated transactionally, such as requiring that these types fit within a word, and can require modification of data layout. Moreover, most STM systems abort transactions in the middle of client code to ensure correctness. This can cause space leaks and other bugs not present in the original code. We present ustm, a novel STM system addressing all of these shortcomings while still maintaining excellent performance, all within 300 lines of code. uSTM supports general types while maintaining data layout. Aborts are deferred until the end of the transaction, allowing client code within a transaction to terminate normally. To ensure that uSTM guarantees opacity, we implement a novel timestamping algorithm we call split-increment timestamps. We compare the performance of uSTM to a variety of state-of-the-art (SOTA) STM systems, demonstrating that uSTM matches or outperforms the SOTA on a variety of workloads.
The primary goal of a distributed storage system is to ensure that clients can both write and read data in a reliable and consistent manner, even in the presence of failures. While existing asynchronous verifiable information dispersal (AVID) protocols achieve optimal space complexity for storage and communication complexity for data retrieval in a Byzantine setting, the crucial operations of data dispersal and node recovery have received less attention. We propose an efficient AVID protocol that simultaneously guarantees low complexities for dispersal, storage, retrieval, and recovery. At the core of the proposed protocol lies a novel mechanism to encode data in a two-dimensional matrix and a bespoke dispersal algorithm. The protocol maintains an optimal communication complexity for retrieval while substantially improving upon the state of the art for recovery. Additionally, we describe how the protocol can be modified in order to reduce the space complexity and the communication complexity for dispersal, at the expense of a higher communication complexity for retrieval and recovery. As the proposed protocols strike a balance across all considered metrics, they are suitable for a broad range of real-world use cases.
