On Design and Applications of Practical Concurrent Data Structures

The proliferation of multicore processors is having an enormous impact on software design and development. In order to exploit parallelism available in multicores, there is a need to design and implement abstractions that programmers can use for general purpose applications development. A common abstraction for coordinated access to memory is a concurrent data structure. Concurrent data structures are challenging to design and implement as they are required to be correct, scalable, and practical under various application constraints. In this thesis, we contribute to the design of efficient concurrent data structures, propose new design techniques and improvements to existing implementations. Additionally, we explore the utilization of concurrent data structures in demanding application contexts such as data stream processing.In the first part of the thesis, we focus on data structures that are difficult to parallelize due to inherent sequential bottlenecks. We present a lock-free vector design that efficiently addresses synchronization bottlenecks by utilizing the combining technique. Typical combining techniques are blocking. Our design introduces combining without sacrificing non-blocking progress guarantees. We extend the vector to present a concurrent lock-free unbounded binary heap that implements a priority queue with mutable priorities.In the second part of the thesis, we shift our focus to concurrent search data structures. In order to offer strong progress guarantee, typical implementations of non-blocking search data structures employ a "helping" mechanism. However, helping may result in performance degradation. We propose help-optimality, which expresses optimization in amortized step complexity of concurrent operations. To describe the concept, we revisit the lock-free designs of a linked-list and a binary search tree and present improved algorithms. We design the algorithms without using any language/platform specific constructs; we do not use bit-stealing or runtime type introspection of objects. Thus, our algorithms are portable. We further delve into multi-dimensional data and similarity search. We present the first lock-free multi-dimensional data structure and linearizable nearest neighbor search algorithm. Our algorithm for nearest neighbor search is generic and can be adapted to other data structures.In the last part of the thesis, we explore the utilization of concurrent data structures for deterministic stream processing. We propose solutions to two challenges prevalent in data stream processing: (1) efficient processing on cloud as well as edge devices and (2) deterministic data-parallel processing at high-throughput and low-latency. As a first step, we present a methodology for customization of streaming aggregation on low-power multicore embedded platforms. Then we introduce Viper, a communication module that can be integrated into stream processing engines for the coordination of threads analyzing data in parallel

Power models, energy models and libraries for energy-efficient concurrent data structures and algorithms

EXCESS deliverable D2.3. More information at http://www.excess-project.eu/This deliverable reports the results of the power models, energy models and librariesfor energy-efficient concurrent data structures and algorithms as available by projectmonth 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 onproviding programming abstractions and libraries for developing energy-efficient datastructures and algorithms and ii) the improved results of Task 2.1 on investigating andmodeling the trade-off between energy and performance of concurrent data structuresand algorithms. The work has been conducted on two main EXCESS platforms: Intelplatforms with recent Intel multicore CPUs and Movidius Myriad platforms

Models for energy consumption of data structures and algorithms

EXCESS deliverable D2.1. More information at http://www.excess-project.eu/This deliverable reports our early energy models for data structures and algorithms based on both micro-benchmarks and concurrent algorithms. It reports the early results of Task 2.1 on investigating and modeling the trade-off between energy and performance in concurrent data structures and algorithms, which forms the basis for the whole work package 2 (WP2). The work has been conducted on the two main EXCESS platforms: (1) Intel platform with recent Intel multi-core CPUs and (2) Movidius embedded platform

Scalable Lock-Free Vector with Combining

Dynamic vectors are among the most commonly used data structures in programming. They provide constant time random access and resizable data storage. Additionally, they provide constant time insertion (pushback) and deletion (popback) at the end of the sequence. However, in a multithreaded system, concurrent pushback and popback operations attempt to update the same shared object, creating a synchronization bottleneck. In this paper, we present a lock-free vector design that efficiently addresses the synchronization bottlenecks by utilizing a combining technique on pushback operations. Typical combining techniques come with the price of blocking. Our design introduces combining without sacrificing lock-freedom. We evaluate the performance of our design on a dual socket NUMA Intel server. The results show that our design performs comparably at low loads, and out-performs prior concurrent blocking and non-blocking vector implementations at high contention, by as much as 2.7x

Concurrent Linearizable Nearest Neighbour Search in LockFree-kD-tree

The Nearest neighbour search (NNS) is an important problem in a large number of application domains dealing with multidimensional data. In concurrent settings, where dynamic modi?cations are allowed, a linearizable implementation of NNS is highly desirable to discover the latest nearest neighbour of a given target data-point. In this paper, we introduce the LockFree-kD-tree (LFkD-tree): a lock-free concurrent kD-tree, which implements an abstract data type (ADT) that provides the operations Add, Remove, Contains, and NNS. Our implementation is linearizable. The operations in the LFkD-tree use single-word read and compare-and-swap (CAS) atomic primitives, which are readily supported on commonly available multi-core processors. We experimentally evaluate the LFkD-tree using several benchmarks comprising real-world and synthetic datasets. The experiments show that the presented design is scalable and achieves significant speed-up compared to the implementations of an existing sequential kD-tree and a recently proposed multidimensional indexingstructure, PH-tree

Scalable Lock-Free Vector with Combining

Dynamic vectors are among the most commonly used data structures in programming. They provide constant time random access and resizable data storage. Additionally, they provide constant time insertion (pushback) and deletion (popback) at the end of the sequence. However, in a multithreaded system, concurrent pushback and popback operations attempt to update the same shared object, creating a synchronization bottleneck. In this paper, we present a lock-free vector design that efficiently addresses the synchronization bottlenecks by utilizing a combining technique on pushback operations. Typical combining techniques come with the price of blocking. Our design introduces combining without sacrificing lock-freedom. We evaluate the performance of our design on a dual socket NUMA Intel server. The results show that our design performs comparably at low loads, and out-performs prior concurrent blocking and non-blocking vector implementations at high contention, by as much as 2.7x

Concurrent linearizable nearest neighbour search in LockFree-kD-tree

The Nearest neighbour search (NNS) is a fundamental problem in many application domains dealing with multidimensional data. In a concurrent setting, where dynamic modifications are allowed, a linearizable implementation of the NNS is highly desirable.This paper introduces the LockFree-kD-tree (LFkD-tree ): a lock-free concurrent kD-tree, which implements an abstract data type (ADT) that provides the operations Add, Remove, Contains, and NNS. Our implementation is linearizable. The operations in the LFkD-tree use single-word read and compare-and-swap (Image 1 ) atomic primitives, which are readily supported on available multi-core processors. We experimentally evaluate the LFkD-tree using several benchmarks comprising real-world and synthetic datasets. The experiments show that the presented design is scalable and achieves significant speed-up compared to the implementations of an existing sequential kD-tree and a recently proposed multidimensional indexing structure, PH-tree