Sequential Data Structures Research Articles

Locksynth: Deriving Synchronization Code for Concurrent Data Structures with ASP

AbstractWe present Locksynth, a tool that automatically derives synchronization needed for destructive updates to concurrent data structures that involve a constant number of shared heap memory write operations. Locksynth serves as the implementation of our prior work on deriving abstract synchronization code. Designing concurrent data structures involves inferring correct synchronization code starting with a prior understanding of the sequential data structure’s operations. Further, an understanding of shared memory model and the synchronization primitives is also required. The reasoning involved transforming a sequential data structure into its concurrent version can be performed using Answer Set Programming, and we mechanized our approach in previous work. The reasoning involves deduction and abduction that can be succinctly modeled in ASP. We assume that the abstract sequential code of the data structure’s operations is provided, alongside axioms that describe concurrent behavior. This information is used to automatically derive concurrent code for that data structure, such as dictionary operations for linked lists and binary search trees that involve a constant number of destructive update operations. We also are able to infer the correct set of locks (but not code synthesis) for external height-balanced binary search trees that involve left/right tree rotations. Locksynth performs the analyses required to infer correct sets of locks and as a final step, also derives the C++ synchronization code for the synthesized data structures. We also provide a performance analysis of the C++ code synthesized by Locksynth with the hand-crafted versions available from the Synchrobench microbenchmark suite. To the best of our knowledge, our tool is the first to employ ASP as a backend reasoner to perform concurrent data structure synthesis.

Sequential linked data: The state of affairs

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.

Generating Concurrent Programs From Sequential Data Structure Knowledge Using Answer Set Programming

We tackle the problem of automatically designing concurrent data structure operations given a sequential data structure specification and knowledge about concurrent behavior. Designing concurrent code is a non-trivial task even in simplest of cases. Humans often design concurrent data structure operations by transforming sequential versions into their respective concurrent versions. This requires an understanding of the data structure, its sequential behavior, thread interactions during concurrent execution and shared memory synchronization primitives. We mechanize this design process using automated commonsense reasoning. We assume that the data structure description is provided as axioms alongside the sequential code of its algebraic operations. This information is used to automatically derive concurrent code for that data structure, such as dictionary operations for linked lists and binary search trees. Knowledge in our case is expressed using Answer Set Programming (ASP), and we employ deduction and abduction -- just as humans do -- in the reasoning involved. ASP allows for succinct modeling of first order theories of pointer data structures, run-time thread interactions and shared memory synchronization. Our reasoner can systematically make the same judgments as a human reasoner, while constructing provably safe concurrent code. We present several reasoning challenges involved in transforming the sequential data structure into its equivalent concurrent version. All the reasoning tasks are encoded in ASP and our reasoner can make sound judgments to transform sequential code into concurrent code. To the best of our knowledge, our work is the first one to use commonsense reasoning to automatically transform sequential programs into concurrent code. We also have developed a tool that we describe that relies on state-of-the-art ASP solvers and performs the reasoning tasks involved to generate concurrent code.

Support structure representation learning for sequential data clustering

Sequential data clustering is a challenging task in data mining (e.g., motion recognition and video segmentation). For good performance in dealing with complex local correlation and high-dimensional structure of sequential data, representation based methods have become one of the hot topics for sequential data clustering, in which subspace clustering is a representative tool. Subspace clustering methods divide the sequence into disjoint segments according to a locally continuous and connected representation of raw data. Although the subspace clustering methods maintain the successive property of sequential data well, there exist redundant connections in the intersection of two subsequences, which will destroy the integrity of a cluster and easily cause the chained partition of the sequence. So it is necessary to learn a more specific structure representation of a sequence to preserves both sequential information and efficient connections. Besides, the representation that conducive to clustering should have sparsity and connectivity under some assumptions. To this end, we propose a novel method to learn the support structure representation of sequence, which can extract sufficient information about instances and get the compact structure of sequential data. Furthermore, a new subspace clustering method is proposed based on the representation based method. Theoretical analysis and experimental results show the effectiveness of the proposed method.

Synch: A framework for concurrent data-structures and benchmarks

The recent advancements in multicore machines highlight the need to simplify concurrent programming in order to leverage their computational power. One way to achieve this is by designing efficient concurrent data structures (e.g. stacks, queues, hash-tables, etc.) and synchronization techniques (e.g. locks, combining techniques, etc.) that perform well in machines with large amounts of cores. In contrast to ordinary, sequential data-structures, the concurrent data-structures allow multiple threads to simultaneously access and/or modify them. Synch is an open-source framework that not only provides some common high-performant concurrent data-structures, but it also provides researchers with the tools for designing and benchmarking high performant concurrent data-structures. The Synch framework contains a substantial set of concurrent data-structures such as queues, stacks, combining-objects, hash-tables, locks, etc. and it provides a user-friendly runtime for developing and benchmarking concurrent data-structures. Among other features, the provided runtime provides functionality for creating threads easily (both POSIX and user-level threads), tools for measuring performance, etc. Moreover, the provided concurrent data-structures and the runtime are highly optimized for contemporary NUMA multiprocessors such as AMD Epyc and Intel Xeon.

Verifying correctness of persistent concurrent data structures: a sound and complete method

Non-volatile memory (NVM), aka persistent memory, is a new memory paradigm that preserves its contents even after power loss. The expected ubiquity of NVM has stimulated interest in the design of persistentconcurrent data structures, together with associated notions of correctness. In this paper, we present a formal proof technique for durable linearizability, which is a correctness criterion that extends linearizability to handle crashes and recovery in the context ofNVM.Our proofs are based on refinement of Input/Output automata (IOA) representations of concurrent data structures. To this end, we develop a generic procedure for transforming any standard sequential data structure into a durable specification and prove that this transformation is both sound and complete. Since the durable specification only exhibits durably linearizable behaviours, it serves as the abstract specification in our refinement proof. We exemplify our technique on a recently proposed persistentmemory queue that builds on Michael and Scott’s lock-free queue. To support the proofs, we describe an automated translation procedure from code to IOA and a thread-local proof technique for verifying correctness of invariants.

Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data

Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However, most of sequential data, as seen with videos, have complex dependency structures that imply variable-length semantic flows and their compositions, and those are hard to be captured by conventional methods. Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. The CB-GLNs represent video data as a graph, with nodes and edges corresponding to frames of the video and their dependencies respectively. The CB-GLNs find compositional dependencies of the data in multilevel graph forms via a parameterized kernel with graph-cut and a message passing framework. We evaluate the proposed method on the two different tasks for video understanding: Video theme classification (Youtube-8M dataset (Abu-El-Haija et al. 2016)) and Video Question and Answering (TVQA dataset(Lei et al. 2018)). The experimental results show that our model efficiently learns the semantic compositional structure of video data. Furthermore, our model achieves the highest performance in comparison to other baseline methods.

How to implement any concurrent data structure for modern servers

In this paper, we propose a method to implement any concurrent data structure. Our method produces implementations that work particularly well in non-uniform memory access (NUMA) machines. Due to recent architecture trends, highly concurrent servers today are NUMA machines, where the cost of accessing a memory location is not the same across every core. To fully leverage these machines, programmers need efficient concurrent data structures that are aware of the NUMA performance artifacts.We propose Node Replication (NR), a black-box approach to obtaining such data structures. NR takes an arbitrary sequential data structure and automatically transforms it into a NUMA-aware concurrent data structure satisfying linearizability. Using NR requires no expertise in concurrent data structure design, and the result is free of concurrency bugs. NR draws ideas from two disciplines: shared-memory algorithms and distributed systems. Briefly, NR implements a NUMA-aware shared log, and then uses the log to replicate data structures consistently across NUMA nodes. The cost of NR is additional memory for its log and replicas.

State duration and interval modeling in hidden semi-Markov model for sequential data analysis

Sequential data modeling and analysis have become indispensable tools for analyzing sequential data, such as time-series data, because larger amounts of sensed event data have become available. These methods capture the sequential structure of data of interest, such as input-output relations and correlation among datasets. However, because most studies in this area are specialized or limited to their respective applications, rigorous requirement analysis of such models has not been undertaken from a general perspective. Therefore, we particularly examine the structure of sequential data, and extract the necessity of `state duration' and `state interval' of events for efficient and rich representation of sequential data. Specifically addressing the hidden semi-Markov model (HSMM) that represents such state duration inside a model, we attempt to add representational capability of a state interval of events onto HSMM. To this end, we propose two extended models: an interval state hidden semi-Markov model (IS-HSMM) to express the length of a state interval with a special state node designated as "interval state node"; and an interval length probability hidden semi-Markov model (ILP-HSMM) which represents the length of the state interval with a new probabilistic parameter "interval length probability." Exhaustive simulations have revealed superior performance of the proposed models in comparison with HSMM. These proposed models are the first reported extensions of HMM to support state interval representation as well as state duration representation.

Black-box Concurrent Data Structures for NUMA Architectures

High-performance servers are Non-Uniform Memory Access (NUMA) machines. To fully leverage these machines, programmers need efficient concurrent data structures that are aware of the NUMA performance artifacts. We propose Node Replication (NR), a black-box approach to obtaining such data structures. NR takes an arbitrary sequential data structure and automatically transforms it into a NUMA-aware concurrent data structure satisfying linearizability. Using NR requires no expertise in concurrent data structure design, and the result is free of concurrency bugs. NR draws ideas from two disciplines: shared-memory algorithms and distributed systems. Briefly, NR implements a NUMA-aware shared log, and then uses the log to replicate data structures consistently across NUMA nodes. NR is best suited for contended data structures, where it can outperform lock-free algorithms by 3.1x, and lock-based solutions by 30x. To show the benefits of NR to a real application, we apply NR to the data structures of Redis, an in-memory storage system. The result outperforms other methods by up to 14x. The cost of NR is additional memory for its log and replicas.

Black-box Concurrent Data Structures for NUMA Architectures

High-performance servers are Non-Uniform Memory Access (NUMA) machines. To fully leverage these machines, programmers need efficient concurrent data structures that are aware of the NUMA performance artifacts. We propose Node Replication (NR), a black-box approach to obtaining such data structures. NR takes an arbitrary sequential data structure and automatically transforms it into a NUMA-aware concurrent data structure satisfying linearizability. Using NR requires no expertise in concurrent data structure design, and the result is free of concurrency bugs. NR draws ideas from two disciplines: shared-memory algorithms and distributed systems. Briefly, NR implements a NUMA-aware shared log, and then uses the log to replicate data structures consistently across NUMA nodes. NR is best suited for contended data structures, where it can outperform lock-free algorithms by 3.1x, and lock-based solutions by 30x. To show the benefits of NR to a real application, we apply NR to the data structures of Redis, an in-memory storage system. The result outperforms other methods by up to 14x. The cost of NR is additional memory for its log and replicas.

Optimisation of data locality in energy calculations for large-scale molecular dynamics simulations

The main bottleneck of molecular dynamic simulations is the estimation of nonbonded pairwise interaction, which often employs neighbour search algorithms to find out interacting atom pairs. These methods have some drawbacks in fulfilling data locality principle, which is unable to take full advantage of modern computer architecture. In this article, we developed a new method by introducing a temporary list to reduce the sparsity in data access. This list permits to obtain a compact and sequential data structure which benefits to efficiently fulfil the data locality principle. We tested and compared the performance of the new method with that of the extensively used reordering method. The new method based on linked cell list is shown to increase 13% of computation speed and have better parallelism in comparison with reordering method. The increase in parallel efficiency makes the new method a promising option for large-scale molecular simulations.

The computability of relaxed data structures: queues and stacks as examples

Most concurrent data structures being designed today are versions of known sequential data structures. However, in various cases it makes sense to relax the semantics of traditional concurrent data structures in order to get simpler and possibly more efficient and scalable implementations. For example, when solving the classical producer-consumer problem by implementing a concurrent queue, it might be enough to allow the dequeue operation (by a consumer) to return and remove one of the two oldest values in the queue, and not necessarily the oldest one. We define infinitely many possible relaxations of several traditional data structures and objects: queues, stacks, multisets and registers, and examine their relative computational power.

Efficient similarity measure for comparing tree structures

The problem of comparing tree structures is known to be a task often characterised by a particularly high computational complexity. Any attempt to reduce this complexity by considering a tree as a linear structure has generally resulted in a loss of information. Indeed, a comparison of tree structures based on their similarity in order to classify them which considers a tree as a single vector, obviously takes less execution time, but unfortunately has less credibility with respect to the classification task. The hierarchical relationships are thus ignored or suppressed and tree structures then behave like sequential data structures. The goal in this paper is to find a compromise between the processing time, on the one hand, and the preservation of information, on the other hand. For this purpose, the proposed approach relies on two types of traversal algorithm, namely the depth-first traversal and breadth-first traversal. The strategy aims to exploit the advantages of combining the two types of algorithms. To validate our approach, we conducted experiments on two sets of tree structures obtained from two collections of real and synthetic XML documents, respectively.

A concurrent red–black tree

With the proliferation of multiprocessor computers, data structures capable of supporting several processes are a growing need. Concurrent data structures seek to provide similar performance to sequential data structures while being accessible concurrently by several processes and providing synchronization mechanisms transparently to those processes.Red–black trees are an important data structure used in many systems. Unfortunately, it is difficult to implement an efficient concurrent red–black tree for shared memory processes; so most research efforts have been directed towards other dictionary data structures, mainly skip-lists and AVL trees.In this paper we present a new type of concurrent red–black tree that uses optimistic concurrency techniques and new balancing operations to scale well and support contention.Our tree performs favorably compared to other similar dictionaries; in particular, in high contention scenarios it performs up to 14% better than the best-known concurrent dictionary solutions.

The SB-index and the HSB-index: efficient indices for spatial data warehouses

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSB-index mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

C++ components describing parallel domain decomposition and communication

Large-scale parallel codes require the data to be decomposed between the set of processes active in the computation. This data decomposition implies recurring communication schemes. The paper introduces generic template classes in C++ for describing the data decomposition. The aim is to store the data in arbitrary existent efficient sequential data structures. Each entry in the sequential data structure corresponds to an entry in the virtual global view of the container. Once the decomposition is setup the needed communication schemes can be created automatically and can be used to communicate values from containers of various types. Even containers with a varying number of values associated with an entry are possible. The framework abstracts the decomposition information and the communication in the client code from the eventual parallel paradigm choice. A prototype based on Message Passing Interface standard is presented. It relieves the user from specifying information that is already known at compile time.

Some Programming Languages Suggested by Game Models (Extended Abstract)

We consider a simple and well-known category of alternating games (also known as sequential data structures) and several categories derived from it. In each case, we present an extension of Plotkin's language FPC (or a suitable linearization thereof) which defines all computable strategies of appropriate types. The quest for such languages results in a novel selection of language primitives for state encapsulation, coroutining and backtracking.

Bistable Biorders: A Sequential Domain Theory

We give a simple order-theoretic construction of a Cartesian closed category of sequential functions. It is based on bistable biorders, which are sets with a partial order -- the extensional order -- and a bistable coherence, which captures equivalence of program behaviour, up to permutation of top (error) and bottom (divergence). We show that monotone and bistable functions (which are required to preserve bistably bounded meets and joins) are strongly sequential, and use this fact to prove universality results for the bistable biorder semantics of the simply-typed lambda-calculus (with atomic constants), and an extension with arithmetic and recursion. We also construct a bistable model of SPCF, a higher-order functional programming language with non-local control. We use our universality result for the lambda-calculus to show that the semantics of SPCF is fully abstract. We then establish a direct correspondence between bistable functions and sequential algorithms by showing that sequential data structures give rise to bistable biorders, and that each bistable function between such biorders is computed by a sequential algorithm.

D-Dimensional Range Search on Multicomputers

The range tree is a fundamental data structure for multidimensional point sets, and, as such, is central in a wide range of geometric and database applications. In this paper we describe the first nontrivial adaptation of range trees to the parallel distributed memory setting (BSP-like models). Given a set of n points in d -dimensional Cartesian space, we show how to construct on a coarse-grained multicomputer a distributed range tree T in time O( s / p + T c (s,p)) , where s = n log d-1 n is the size of the sequential data structure and T c (s,p) is the time to perform an h -relation with h=Θ (s/p) . We then show how T can be used to answer a given set Q of m=O(n) range queries in time O((s log m)/p + T c (s,p)) and O((s log m)/p + T c (s,p) + k/p) , where k is the number of results to be reported. These parallel construction and search algorithms are both highly efficient, in that their running times are the sequential time divided by the number of processors, plus a constant number of parallel communication rounds.