54 research outputs found
Brief Announcement: Non-Blocking Dynamic Unbounded Graphs with Worst-Case Amortized Bounds
No full textThis paper reports a new concurrent graph data structure that supports updates of both edges and vertices and queries: Breadth-first search, Single-source shortest-path, and Betweenness centrality. The operations are provably linearizable and non-blocking
Non-Blocking Dynamic Unbounded Graphs with Worst-Case Amortized Bounds
No full textThis paper reports a new concurrent graph data structure that supports updates of both edges and vertices and queries: Breadth-first search, Single-source shortest-path, and Betweenness centrality. The operations are provably linearizable and non-blocking. © Bapi Chatterjee, Sathya Peri, and Muktikanta Sa; licensed under Creative Commons License CC-BY 4.
Non-Blocking Dynamic Unbounded Graphs with Worst-Case Amortized Bounds
No full textToday’s graph-based analytics tasks in domains such as blockchains, social networks, biological networks, and several others demand real-time data updates at high speed. The real-time updates are efficiently ingested if the data structure naturally supports dynamic addition and removal of both edges and vertices. These dynamic updates are best facilitated by concurrency in the underlying data structure. Unfortunately, the existing dynamic graph frameworks broadly refurbish the static processing models using approaches such as versioning and incremental computation. Consequently, they carry their original design traits such as high memory footprint and batch processing that do not honor the real-time changes. At the same time, multi-core processors-a natural setting for concurrent data structures-are now commonplace, and the analytics tasks are moving closer to data sources over lightweight devices. Thus, exploring a fresh design approach for real-time graph analytics is significant.
This paper reports a novel concurrent graph data structure that provides breadth-first search, single-source shortest-path, and betweenness centrality with concurrent dynamic updates of both edges and vertices. We evaluate the proposed data structure theoretically - by an amortized analysis - and experimentally via a C++ implementation. The experimental results show that (a) our algorithm outperforms the current state-of-the-art by a throughput speed-up of up to three orders of magnitude in several cases, and (b) it offers up to 80x lighter memory-footprint compared to existing methods. The experiments include several counterparts: Stinger, Ligra and GraphOne. We prove that the presented concurrent algorithms are non-blocking and linearizable
Lock-free linearizable 1-dimensional range queries
No full text\ua9 2016 ACM.Efficient concurrent data structures that support range queries are highly sought-after in a number of application areas. For example, the contemporary big-data processing platforms employ them as in-memory index structures for fast and scalable real-time updates and analytics, where analytics utilizes the range queries. In this paper, we present a generic algorithm to perform linearizable range queries in lock-free ordered 1-dimensional data structures. The algorithm requires single-word atomic compare-and-swap (CAS) primitives. Our method generalizes the lock-free data structure snapshot of Petrank et al. [25]. Fundamentally, we utilize a partial snapshot object derived from the snapshot object of Jayanti [20]. We experimentally evaluate the proposed algorithm in a lock-free linked-list, skip-list and binary search tree (BST). The experiments demonstrate that our algorithm is scalable even in the presence of high percentage of concurrent modify operations and outperforms an existing range search algorithm in lock-free k-ary trees in several scenarios
Concurrent linearizable nearest neighbour search in lockfree-kd-Tree
No full textThe Nearest neighbour search (NNS) is a fundamental problem in many application domains dealing with multidimensional data. In a concurrent setting, where dynamic modi-fications are allowed, a linearizable implementation of 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 (CAS) 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 signi cant speed-up compared to the implementations of an existing sequential kD-Tree and a recently proposed multidimensional indexing structure, PH-Tree.\ua0\ua9 2018 Copyright held by the owner/author(s)
Efficient Implementation of Concurrent Data Structures on Multi-core and Many-core Architectures
No full textSynchronization of concurrent threads is the central problem in order to design efficient concurrent data-structures. The compute systems widely available in market are increasingly becoming heterogeneous involving multi-core Central Processing Units (CPUs) and many-core Graphics Processing Units (GPUs). This thesis contributes to the research of efficient synchronization in concurrent data-structures in more than one way. It is divided into two parts. In the first part, a novel design of a Set Abstract Data Type (ADT) based on an efficient lock-free Binary Search Tree (BST) with improved amortized bounds of the time complexity of set operations - Add, Remove and Contains, is presented. In the second part, a comprehensive evaluation of concurrent Queue implementations on multi-core CPUs as well as many-core GPUs are presented.
Efficient Lock-free BST -To the best of our knowledge, the lock-free BST presented in this thesis is the first to achieve an amortized complexity of O(H(n)+c) for all Set operations where H(n) is the height of a BST on n nodes and c is the contention measure. Also, the presented lock-free algorithm of BST comes with an improved disjoint-access-parallelism compared to the previously existing concurrent BST algorithms. This algorithm uses single-word compare-and-swap (CAS) primitives. The presented algorithm is linearizable. We implemented the algorithm in Java and it shows good scalability.
Evaluation of concurrent data-structures - We have evaluated the performance of a number of concurrent FIFO Queue algorithms on multi-core CPUs and many-core GPUs. We studied the portability of existing design of concurrent Queues from CPUs to GPUs which are inherently designed for SIMD programs. We observed that in general concurrent queues offer them to efficient implementation on GPUs with faster cache memory and better performance support for atomic synchronization primitives such as CAS. To the best of our knowledge, this is the first attempt to evaluate a concurrent data-structure on GPUs
Lock-free Concurrent Search
No full textThe contemporary computers typically consist of multiple computing cores with high compute power. Such computers make excellent concurrent asynchronous shared memory system. On the other hand, though many celebrated books on data structure and algorithm provide a comprehensive study of sequential search data structures, unfortunately, we do not have such a luxury if concurrency comes in the setting. The present dissertation aims to address this paucity. We describe novel lock-free algorithms for concurrent data structures that target a variety of search problems.<br /><br />(i) Point search (membership query, predecessor query, nearest neighbour query) for 1-dimensional data: Lock-free linked-list; lock-free internal and external binary search trees (BST).<br />(ii) Range search for 1-dimensional data: A range search method for lock-free ordered set data structures - linked-list, skip-list and BST.<br />(iii) Point search for multi-dimensional data: Lock-free kD-tree, specially, a generic method for nearest neighbour search.<br /><br />We prove that the presented algorithms are linearizable i.e. the concurrent data structure operations intuitively display their sequential behaviour to an observer of the concurrent system. The lock-freedom in the introduced algorithms guarantee overall progress in an asynchronous shared memory system. We present the amortized analysis of lock-free data structures to show their efficiency. Moreover, we provide sample implementations of the algorithms and test them over extensive micro-benchmarks. Our experiments demonstrate that the implementations are scalable and perform well when compared to related existing alternative implementations on common multi-core computers.<br /><br />Our focus is on propounding the generic methodologies for efficient lock-free concurrent search. In this direction, we present the notion of help-optimality, which captures the optimization of amortized step complexity of the operations. In addition to that, we explore the language-portable design of lock-free data structures that aims to simplify an implementation from programmer’s point of view. Finally, our techniques to implement lock-free linearizable range search and nearest neighbour search are independent of the underlying data structures and thus are adaptive to similar data structures
Lock-free Concurrent Search [Elektronisk resurs]
No full textThe contemporary computers typically consist of multiple computing cores with high compute power. Such computers make excellent concurrent asynchronous shared memory system. On the other hand, though many celebrated books on data structure and algorithm provide a comprehensive study of sequential search data structures, unfortunately, we do not have such a luxury if concurrency comes in the setting. The present dissertation aims to address this paucity. We describe novel lock-free algorithms for concurrent data structures that target a variety of search problems. (i) Point search (membership query, predecessor query, nearest neighbour query) for 1-dimensional data: Lock-free linked-list; lock-free internal and external binary search trees (BST). (ii) Range search for 1-dimensional data: A range search method for lock-free ordered set data structures - linked-list, skip-list and BST. (iii) Point search for multi-dimensional data: Lock-free kD-tree, specially, a generic method for nearest neighbour search. We prove that the presented algorithms are linearizable i.e. the concurrent data structure operations intuitively display their sequential behaviour to an observer of the concurrent system. The lock-freedom in the introduced algorithms guarantee overall progress in an asynchronous shared memory system. We present the amortized analysis of lock-free data structures to show their efficiency. Moreover, we provide sample implementations of the algorithms and test them over extensive micro-benchmarks. Our experiments demonstrate that the implementations are scalable and perform well when compared to related existing alternative implementations on common multi-core computers. Our focus is on propounding the generic methodologies for efficient lock-free concurrent search. In this direction, we present the notion of help-optimality, which captures the optimization of amortized step complexity of the operations. In addition to that, we explore the language-portable design of lock-free data structures that aims to simplify an implementation from programmer’s point of view. Finally, our techniques to implement lock-free linearizable range search and nearest neighbour search are independent of the underlying data structures and thus are adaptive to similar data structures
Concurrent linearizable nearest neighbour search in LockFree-kD-tree
No full textThe 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 ([Formula presented] ) 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
A simple and practical concurrent non-blocking unbounded graph with linearizable reachability qeries
No full textGraph algorithms applied in many applications, including social networks, communication networks, VLSI design, graphics, and several others, require dynamic modifications - addition and removal of vertices and/or edges - in the graph. This paper presents a novel concurrent non-blocking algorithm to implement a dynamic unbounded directed graph in a shared-memory machine. The addition and removal operations of vertices and edges are lock-free. For a finite sized graph, the lookup operations are wait-free. Most significant component of the presented algorithm is the reachability query in a concurrent graph. The reachability queries in our algorithm are obstruction-free and thus impose minimal additional synchronization cost over other operations. We prove that each of the data structure operations are linearizable. We extensively evaluate a sample C/C++ implementation of the algorithm through a number of micro-benchmarks. The experimental results show that the proposed algorithm scales well with the number of threads and on an average provides 5 to 7x performance improvement over a concurrent graph implementation using coarse-grained locking
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