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—We present the skip vector, a novel highperformance concurrent data structure based on the skip list. The key innovation in the skip vector is to flatten the index and data layers of the skip list into vectors. This increases spatial locality, reduces synchronization overhead, and avoids much of the costly pointer chasing that skip lists incur. We evaluate a skip vector implementation in C++. Our implementation coordinates interactions among threads by utilizing optimistic traversal with sequence locks. To ensure memory safety, it employs hazard pointers; this leads to tight bounds on wasted space, but due to the skip vector design, does not lead to high overhead. Performance of the skip vector for small data set sizes is higher than for a comparable skip list, and as the amount of data increases, the benefits of the skip vector over a skip list increase. 

The Transactional Data Structure Library (TDSL) methodology improves the programmability and performance of concurrent software by making it possible for programmers to compose multiple concurrent data structure operations into coarse-grained transactions. Like transactional memory, TDSL enables arbitrarily many operations on arbitrarily many data structures to appear to other threads as a single atomic, isolated transaction. Like concurrent data structures, the individual operations on a TDSL data structure are optimized to avoid artificial contention. We introduce techniques for reducing false conflicts in TDSL implementations. Our approach allows expressing the postconditions of operations entirely via semantic properties, instead of through low-level structural properties. Our design is general enough to support lists, deques, ordered and unordered maps, and vectors. It supports richer programming interfaces than are available in existing TDSL implementations. It is also capable of precise memory management, which is necessary in low-level languages like C++. 

This paper introduces MEDS, a modular and elastic framework that simplifies the development of high-performance concurrent data structures that support linearizable primitive (i.e., add, remove, contains) and bulk (e.g., range query) operations.