text: Adjacency lists are frequently used in graphing or map based applications. Although efficient concurrent linked-list algorithms are well known, it can be difficult to adapt these approaches to build a high-performance adjacency list. Furthermore, it can often be desirable to execute operations in these data structures transactionally, or perform a sequence of operations in one atomic step. In this paper, we present a lock-free transactional adjacency list based on a multi-dimensional list (MDList). We are able to combine known linked list strategies with the capability of the MDList in order to efficiently organize graph vertexes and their edges. We design our underlying data structure to be node-based and linearizable, then use the Lock-Free Transactional Transformation (LFTT) methodology to efficiently enable transactional execution. In our performance evaluation, our lock-free transactional adjacency list achieves an average of 50 speedup over a transactional boosting implementation.
  @cite_19: We describe a methodology for transforming a large class of highly-concurrent linearizable objects into highly-concurrent transactional objects. As long as the linearizable implementation satisfies certain regularity properties (informally, that every method has an inverse), we define a simple wrapper for the linearizable implementation that guarantees that concurrent transactions without inherent conflicts can synchronize at the same granularity as the original linearizable implementation.
summary: @cite_19 present transactional boosting, a methodology for transforming highly-concurrent linearizable objects into highly-concurrent transactional objects. Transactional boosting uses a high-level semantic conflict detection to allow commutative operations in separate transactions to proceed concurrently using the thread-level synchronization of the base linearizable data structure; non-commutative operations require transaction-level synchronization through the acquisition of an abstract lock. If a transaction aborts, it recovers the correct abstract state by invoking the inverse operations recorded in the undo log.