Transactional data structures support threads executing a sequence of operations atomically. Dynamic transactions allow operands to be generated on the fly and allows threads to execute code in between the operations of a transaction, in contrast to static transactions which need to know the operands in advance. A framework called Lock-free Transactional Transformation (LFTT) allows data structures to run high-performance transactions, but it only supports static transactions. We extend LFTT to add support for dynamic transactions and wait-free progress while retaining its speed. The thread-helping scheme of LFTT presents a unique challenge to dynamic transactions. We overcome this challenge by changing the input of LFTT from a list of operations to a function, forcing helping threads to always start at the beginning of the transaction, and allowing threads to skip completed operations through the use of a list of return values. We thoroughly evaluate the performance impact of support for dynamic transactions and wait-free progress and find that these features do not hurt the performance of LFTT for our test cases. CCS Concepts • Computing methodologies → Shared memory algorithms; Concurrent algorithms.
Transactional data structures allow data structures to support transactional execution, in which a sequence of operations appears to execute atomically. We consider a paradigm in which a transaction commits its changes to the data structure only if all of its operations succeed; if one operation fails, then the transaction aborts. In this work, we introduce an optimization technique called Check-Wait-Pounce that increases performance by avoiding aborts that occur due to failed operations. Check-Wait-Pounce improves upon existing methodologies by delaying the execution of transactions until they are expected to succeed, using a thread-unsafe representation of the data structure as a heuristic. Our evaluation reveals that Check-Wait-Pounce reduces the number of aborts by an average of 49.0%. Because of this reduction in aborts, the tested transactional linked lists achieve average gains in throughput of 2.5x, while some achieve gains as high as 4x.
Transactional memory simplifies multiprocessor programming by providing the guarantee that a sequential block of code in the form of a transaction will exhibit atomicity and isolation. Transactional data structures offer the same guarantee to concurrent data structures by enabling the atomic execution of a composition of operations. The concurrency control of transactional memory systems preserves atomicity and isolation by detecting read/write conflicts among multiple concurrent transactions. State-of-the-art transactional data structures improve on this concurrency control protocol by providing explicit transaction-level synchronization for only non-commutative operations. Since read/write conflicts are handled by thread-level concurrency control, the correctness of transactional data structures cannot be evaluated according to the read/write histories. This presents a challenge for existing correctness verification techniques for transactional memory, because correctness is determined according to the transitions taken by the transactions in the presence of read/write conflicts. In this article, we present Transactional Correctness tool for Abstract Data Types (TxC-ADT), the first tool that can check the correctness of transactional data structures. TxC-ADT elevates the standard definitions of transactional correctness to be in terms of an abstract data type, an essential aspect for checking correctness of transactions that synchronize only for high-level semantic conflicts. To accommodate a diverse assortment of transactional correctness conditions, we present a technique for defining correctness as a happens-before relation. Defining a correctness condition in this manner enables an automated approach in which correctness is evaluated by generating and analyzing a transactional happens-before graph during model checking. A transactional happens-before graph is maintained on a per-thread basis, making our approach applicable to transactional correctness conditions that do not enforce a total order on a transactional execution. We demonstrate the practical applications of TxC-ADT by checking Lock Free Transactional Transformation and Transactional Data Structure Libraries for serializability, strict serializability, opacity, and causal consistency.
Smart contract transactions demonstrate issues of performance and correctness that application programmers must work around. Although the blockchain consensus mechanism approaches ACID compliance, use cases that rely on frequent state changes are impractical due to the block publishing interval of O(10 1 ) seconds. The effective isolation level is READ-COMMITTED, only revealing state transitions at the end of the block interval. Values read may be stale and not match program order, causing many transactions to fail when a block is committed. This paper perceives the blockchain as a transactional data structure, using this analogy in the development of a new algorithm, Hash-Mark-Set (HMS), that improves transaction throughput by providing a READ-UNCOMMITTED view of state variables. HMS creates a directed acyclic graph (DAG) from the pending transaction pool. The transaction order derived from the DAG is used to provide a READ-UNCOMMITTED view of the data for new transactions, which enter the DAG as they are received. An implementation of HMS is provided, interoperable with Ethereum and ready for use in smart contracts. Over a wide range of transaction mixes, HMS is demonstrated to improve throughput. A side product of the implementation is a new technique, Runtime Argument Augmentation (RAA), that allows smart contracts to communicate with external data services before submitting a transaction. RAA has use cases beyond HMS and can serve as a lightweight replacement for blockchain oracles.
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