Parallel Query Research Articles

Impact of Design Decisions on Performance of Embarrassingly Parallel .NET Database Application

The implementation of parallel applications is always a challenge. It embraces many distinctive design decisions that are to be taken. The paper presents issues of parallel processing with use of .NET applications and popular Database Management Systems (DBMSes). In the paper, four design dilemmas are addressed: how efficient is the auto-parallelism implemented in the .NET TPL library, how do popular DBMSes differ in serving parallel requests, what is the optimal size of data chunks in the data parallelism scheme, and how the TPL auto-parallelism behaves in the public clouds. They are analyzed in the context of the typical and practical business case originated from IT solutions which are dedicated for the energy market participants. The paper presents the results of experiments conducted in a controlled, on-premises and cloud environments. The experiments allowed to compare the performance of the TPL auto-parallelism with a wide range of manually set numbers of worker threads. They also helped to evaluate four DBMSes: Oracle, MySQL, PostgreSQL, and MSSQL in the scenario of serving parallel queries. Finally, they showed the impact of data chunk sizes on the overall performance.

CMOS-Integrated Ternary Content Addressable Memory using Nanocavity CBRAMs for High Sensing Margin.

The development of data-intensive computing methods imposes a significant load on the hardware, requiring progress toward a memory-centric paradigm. Within this context, ternary content-addressable memory (TCAM) can become an essential platform for high-speed in-memory matching applications of large data vectors. Compared to traditional static random-access memory (SRAM) designs, TCAM technology using non-volatile resistive memories (RRAMs) in two-transistor-two-resistor (2T2R) configurations presents a cost-efficient alternative. However, the limited sensing margin between the match and mismatch states in RRAM structures hinders the potential of using memory-based TCAMs for large-scale architectures. Therefore, this study proposes a practical device engineering method to improve the switching response of conductive-bridge memories (CBRAMs) integrated with existing complementary metal-oxide-semiconductor (CMOS) transistor technology. Importantly, this work demonstrates a significant improvement in memory window reaching 1.87 × 107 by incorporating nanocavity arrays and modifying electrode geometry. Consequently, TCAM cells using nanocavity-enhanced CBRAM devices can exhibit a considerable increase in resistance ratio up to 6.17 × 105, thereby closely approximating the sensing metrics observed in SRAM-based TCAMs. The improved sensing capability facilitates the parallel querying of extensive data sets. TCAM array simulations using experimentally verified device models indicate a substantial sensing margin of 65× enabling a parallel search of 2048 bits.

Optimizing Performance in Distributed Cloud Architectures: A Review of Optimization Techniques and Tools

This research paper presents a groundbreaking hybrid transactional/analytical processing (HTAP) architecture designed to revolutionize real-time point cloud data processing, particularly in autonomous driving environments. Integrating elements from both columnar and row-based tables within a spatial database, the proposed architecture offers unparalleled efficiency in managing and updating point cloud data in real-time. The architecture's distributed nature operates through a seamless synergy of Edge and Cloud components. The Edge segment operates within the Robot Operating System (ROS) environment of the vehicle, while the Cloud counterpart functions within the PostgreSQL environment of cloud services. The communication between these components is facilitated by Kafka, ensuring rapid and reliable data transmission. A pivotal aspect of the proposed system lies in its ability to autonomously detect changes in point cloud data over time. This is achieved through a sophisticated algorithm that analyzes dissimilarities in the data, triggering real-time updates in areas where high dissimilarity is detected. The system ensures the maintenance of the latest state of point cloud data, contributing significantly to the generation of safe and optimized routes for autonomous vehicles. In terms of optimization, the paper demonstrates how the HTAP architecture achieves real-time online analytical processing through query parallelization in a distributed database cluster. The system's efficacy is evaluated through simulations conducted in the CloudSim framework, showcasing its scalability, adaptability, and robustness in handling point cloud data processing for a single vehicle. While acknowledging the achievement of the proposed architecture, certain limitations are recognized. The study highlights the need for further investigation into the system's performance under simultaneous analysis and updates from multiple vehicles. Additionally, ensuring seamless scalability and robustness for uninterrupted operation and expansion during runtime is identified as an area requiring further development.

Accelerating range minimum queries with ray tracing cores

Over the past decade, GPU technology has undergone a notable transformation, evolving from pure general-purpose computation to the integration of application-specific integrated circuits (ASICs), including Tensor Cores and Ray Tracing (RT) cores. While these specialized GPU cores were initially developed to enhance specific domains like AI and real-time rendering, recent research has successfully harnessed their capabilities to expedite other tasks traditionally reliant on conventional GPU computing. One GPU task that is still yet to find its way into RT cores is the processing of range minimum queries (RMQs) in parallel, which is fundamental in fields such as information retrieval or pattern matching, among others. In this context, accelerating RMQs with RT cores would impact many of the applications that heavily rely on this task. In this work we present RTXRMQ, a new approach that can compute RMQs with RT cores. The main contribution is the proposal of a geometric solution for RMQ, where elements become triangles that are placed and shaped according to the element’s value and position in the array, respectively, such that the closest hit of a ray launched from a point given by the query parameters corresponds to the result of that query. Experimental results show that RTXRMQ is currently best suited for small query ranges relative to the input size, achieving up to 5× and 2.3× of speedup over parallel state of the art CPU and GPU approaches, respectively. For medium and large query ranges RTXRMQ is still slower than the state of the art GPU approach, but still competitive by being 2.5× and 4× faster than a state of the art CPU method running in parallel as well. Furthermore, performance scaling experiments across the latest RTX GPU architectures show that if the current RT core scaling trend continues, then RTXRMQ’s performance would scale at a higher rate than the other compared approaches, making it an attractive tool for future high performance applications that employ many batches of RMQs.

The Distributed HTAP Architecture for Real-Time Analysis and Updating of Point Cloud Data

Updating the most recent set of point cloud data is critical in autonomous driving environments. However, existing systems for point cloud data management often fail to ensure real-time updates or encounter situations in which data cannot be effectively refreshed. To address these challenges, this study proposes a distributed hybrid transactional/analytical processing architecture designed for the efficient management and real-time processing of point cloud data. The proposed architecture leverages both columnar and row-based tables, enabling it to handle the substantial workloads associated with its hybrid architecture. The construction of this architecture as a distributed database cluster ensures real-time online analytical process query performance through query parallelization. A dissimilarity analysis algorithm for point cloud data, built by utilizing the capabilities of the spatial database, updates the point cloud data for the relevant area whenever the online analytical process query results indicate high dissimilarity. This research contributes to ensuring real-time hybrid transactional/analytical processing workload processing in dynamic road environments, helping autonomous vehicles generate safe, optimized routes.

Toward Performant and Energy-Efficient Network Queries: A Parallel and Stateless Approach

In many edge computing applications (e.g., wireless sensor networks, WSNs) where nodes are mostly battery-powered, queries’ energy consumption, and response time are two of the most important metrics, representing the network’s sustainability and performance, respectively. Conventional techniques used to focus on only one of the two metrics and did not attempt to optimize both in a coordinated manner. This work aims to achieve both high sustainability and high performance of these queries at the same time. To that end, a new mechanism is proposed to construct the topology of a three-tier WSN. The proposed mechanism eliminates the conventional stateful routing tables and employs a stateless and efficient addressing scheme inspired by the Chinese remainder theorem (CRT). The CRT-based topology allows for query parallelism, an unprecedented feature in the WSNs. On top of the new topology encoded by CRT, a new protocol is designed to parallelly preprocess collected data on sensor nodes by effectively aggregating and deduplicating data in a cluster of neighborhood nodes. At the same time, the hibernating mechanism is proposed to prolong the network life cycle. Moreover, a new algorithm is devised to allow the queries and results to be transmitted through low-power and fault-tolerant paths using recursive elections over a subset of the entire power range. With all these new techniques taken together, the system presented in this work outperforms approximate algorithms from various perspectives: (i) the query response is improved by up to 21.6%; (ii) the energy consumption is reduced by up to 16.8%; and (iii) the reliability is increased by up to 18.3%.

Multiwinner Voting with Possibly Unavailable Candidates

Selecting a committee that meets diversity and proportionality criteria is a challenging endeavor that has been studied extensively in recent years. This task becomes even more challenging when some of the selected candidates decline the invitation to join the committee. Since the unavailability of one candidate may impact the rest of the selection, inviting all candidates at the same time may lead to a suboptimal committee. Instead, invitations should be sequential and conditional on which candidates invited so far accepted the invitation: the solution to the committee selection problem is a query policy. If invitation queries are binding, they should be safe: one should not query a candidate without being sure that whatever the set of available candidates possible at that stage, her inclusion will not jeopardize committee optimality. Assuming approval-based inputs, we characterize the set of rules for which a safe query exists at every stage. In order to parallelize the invitation process, we investigate the computation of safe parallel queries, and show that it is often hard. We also study the existence of safe parallel queries with respect to proportionality axioms such as extended justified representation.

Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In this work, we propose an efficient method, in the fog-cloud computing architecture, to index continuous and heterogeneous data streams in metric space. This method divides the fog layer into three levels: clustering, clusters processing and indexing. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to group the data from each stream into homogeneous clusters at the clustering fog level. Each cluster in the first data stream is stored in the clusters processing fog level and indexed directly in the indexing fog level in a Binary tree with Hyperplane (BH tree). The indexing of clusters in the subsequent data stream is determined by the coefficient of variation (CV) value of the union of the new cluster with the existing clusters in the cluster processing fog layer. An analysis and comparison of our experimental results with other results in the literature demonstrated the effectiveness of the CV method in reducing energy consumption during BH tree construction, as well as reducing the search time and energy consumption during a k Nearest Neighbor (kNN) parallel query search.

The complexity of bicriteria tree-depth

The tree-depth problem can be seen as finding an elimination tree of minimum height for a given input graph G. We introduce a bicriteria generalization in which additionally the width of the elimination tree needs to be bounded by some input integer b. We are interested in the case when G is the line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive 2b-approximation algorithm. This particular class of graphs received significant attention in the past, mainly due to a number of potential applications, e.g. in parallel assembly of modular products, or parallel query processing in relational databases, as well as purely combinatorial applications including searching in tree-like partial orders (which in turn generalizes binary search on sorted data).

Person Search by a Bi-Directional Task-Consistent Learning Model

Two-stage person search methods achieve the state-of-the-art performance by separate detection and re-ID stages, but neglect the consistency needs between these two stages. The re-ID stage needs more accurate query bounding boxes and fewer boxes of distractors; The detection stage needs the re-ID stage to have robustness against unavailable detection errors. In this paper, we introduce a novel Bi-directional Task-Consistent Learning (BTCL) person search framework, including a Target-Specific Detector (TSD) and a re-ID model with Dynamic Adaptive Learning Structure (DALS). For the former consistency need, we add a verification head for predicting the similarity scores between query and proposals in parallel with the existing heads for bounding box recognition. Thus, TSD generates accurate boxes for the query-like pedestrians, which are suitable for the re-ID stage. For the re-ID robustness need, DALS dynamically generates a large number of possible detection results in line with the real distribution. By training the re-ID model on data with different types of detection errors, DLAS improves the model robustness to detection inputs. Experimental results show our framework achieves state-of-the-art performance on two widely-used person search datasets.

Design of Internet of Things Information Interactive Perception System Based on 5G Mobile Communication Technology in a Hospital Network

Abstract In the communication process of 5G big data Internet of Things, because of the difference of communication data, the data interaction process needs information mutual recognition scheduling, which greatly reduces the effectiveness of the interactive perception system. In order to improve the real-time performance of platform interaction in the Internet of Things environment, an Internet of Things information interaction perception system based on 5G mobile communication technology is designed. This is mainly important in a hospital network. The system perception layer completes the accurate acquisition of Internet of Things information through data acquisition module and transmits it to the application layer through 5G mobile communication module in the network layer. The intelligent interaction module based on context awareness in the application layer will be heterogeneous and unstructured. The Internet of Things perceived data is encapsulated in context as a unified data processing object of the Internet of Things. The context rule-matching algorithm based on Storm is used to achieve accurate matching between heterogeneous and unstructured perceptual data and rules, and an interactive perceptual parallel query scheduling strategy is used to achieve the optimal scheduling of information mutual perception in the Internet of Things. The results show that there is a positive correlation between the validity of sensing temperature information and the amount of sensing data. The average data processing delay of the system is stable at 50 ms, which meets the real-time requirement of data processing.

Achieving new SQL query performance levels through parallel execution in SQL Server

This article provides an in-depth look at implementing parallel SQL query processing using the Microsoft SQL Server database management system. It examines how parallelism can significantly accelerate query execution by leveraging multi-core processors and clustered environments. The article explores SQL Server's sophisticated parallel processing capabilities including automatic query parallelization, intra-query parallelism techniques like parallel joins and parallel data aggregation, as well as inter-query parallelism for concurrent query execution. It covers key considerations around effective parallelization such as managing concurrency and locks, handling data skew, resource governance, and monitoring. Challenges like debugging parallel plans and potential bottlenecks from excessive parallelism are also discussed along with mitigation strategies. Real-world examples demonstrate how judicious application of parallel processing helps optimize complex analytics workloads involving massive datasets. The insights presented provide guidance to database developers and administrators looking to enable parallel SQL query execution in SQL Server environments for substantial performance gains and scalability.

Multilingual parallel corpus: An institutional resource for terminology development at the University of South Africa (Unisa)

The indigenous African languages of South Africa are not fully developed to provide for specialised terminology and were considered unsuitable for use as languages of tuition and research. This was used as a scapegoat for not utilising these languages in the South African education system. Since 1994, however, terminology development has been one of the key priorities of democratic South Africa. The institutions of Higher Learning have been mandated to develop and intellectualise the indigenous languages for teaching, learning and research. In line with this, this article aims to address the problem of unavailability of scientific or technical terms by illustrating how a multilingual corpus ––from which multilingual glossaries as resources for tuition and research–– can be compiled. Adopting a qualitative descriptive approach, suitable source texts in English and their translations in various African indigenous languages, namely, IsiZulu, IsiXhosa, IsiNdebele, SiSwati, Tshivenda, and Xitsonga were selected from the University study material for inclusion in the multilingual parallel corpus. ParaConc, a software that is suitable to query parallel texts, was used to align and extract terms from the corpus. The study demonstrates how parallel texts can be useful in developing scientific and technical terms. The University of South Africa can become the centre of corpus compilation for the intellectualisation of the official indigenous South African languages, since it is the only university in the country that caters for all these languages.

Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations

Systems performing large data-parallel computations, including online analytical processing (OLAP) systems like Druid and search engines like Elasticsearch, are increasingly being used for business-critical real-time applications where providing low query latency is paramount. In this paper, we investigate an underexplored factor in the performance of data-parallel queries: their parallelism. We find that to minimize the tail latency of data-parallel queries, it is critical to place data such that the data items accessed by each individual query are spread across as many machines as possible so that each query can leverage the computational resources of as many machines as possible. To optimize parallelism and minimize tail latency in real systems, we develop a novel parallelism-optimizing data placement algorithm that defines a linearly-computable measure of query parallelism, uses it to frame data placement as an optimization problem, and leverages a new optimization problem partitioning technique to scale to large cluster sizes. We apply this algorithm to popular systems such as Solr and MongoDB and show that it reduces p99 latency by 7-64% on data-parallel workloads.

Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark

Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.

Round-Competitive Algorithms for Uncertainty Problems with Parallel Queries

In computing with explorable uncertainty, one considers problems where the values of some input elements are uncertain, typically represented as intervals, but can be obtained using queries. Previous work has considered query minimization in the settings where queries are asked sequentially (adaptive model) or all at once (non-adaptive model). We introduce a new model where k queries can be made in parallel in each round, and the goal is to minimize the number of query rounds. Using competitive analysis, we present upper and lower bounds on the number of query rounds required by any algorithm in comparison with the optimal number of query rounds for the given instance. Given a set of uncertain elements and a family of m subsets of that set, we study the problems of sorting all m subsets and of determining the minimum value (or the minimum element(s)) of each subset. We also study the selection problem, i.e., the problem of determining the i-th smallest value and identifying all elements with that value in a given set of uncertain elements. Our results include 2-round-competitive algorithms for sorting and selection and an algorithm for the minimum value problem that uses at most (2+varepsilon ) cdot mathrm {opt}_k+mathrm {O}left( frac{1}{varepsilon } cdot lg mright) query rounds for every 0<varepsilon <1, where mathrm {opt}_k is the optimal number of query rounds.

Estimating the response time of a data-intensive computing environment

Introduction: The amount of digital data is constantly growing as well as the need for its storage and processing for various purposes. To conduct data analysis, high-performance computing environments associated with parallelization methods, and, accordingly, data-intensive applications are used. The lack of quality tools for evaluating the effectiveness of the process of parallel data processing or tasks leads to excessive allocation of resources. Purpose: To develop mathematical models of data-intensive computing environments and methods for their performance analysis, i.e., for estimating the average system response time based on the data on system performance at the level of subtask solving. Results: We present a mathematical model of a parallel computing system in the form of a queueing system with parallel query processing on various architectures, including non-Poisson input flow and non-exponential service times. As a method for analyzing the average response time, we use a combination of simulation modeling with one of the machine learning methods (artificial neural networks). The effectiveness of the method is confirmed by numerical experiments and depends neither on the type of input flow, nor on the type of distribution of query service times, nor on the number of servers in the nodes of the system. The approximation error of the average response time does not exceed 10%, which makes it possible to optimize the generally accepted resource allocation, significantly reducing the amount of the resources. Practical relevance: The presented models and the method of their analysis can be used for efficient planning and allocation of resources for data-intensive systems.

Accelerated materials design using batch Bayesian optimization: A case study for solving the inverse problem from materials microstructure to process specification

Microstructure-based process design is one of the main ingredients for materials design, under the integrated computational materials engineering paradigm, which relies on inverting process-structure–property linkages. The specific inverse problem connecting microstructure to processing conditions is exceedingly difficult to solve, even in a computational setting. The difficulty arises from the challenges associated with properly representing the microstructure space as well as the computational cost of the simulations used to connect process conditions to microstructure evolution. In this work, we attempt to invert a process-microstructure problem by implementing and deploying a search scheme based on multi-scale batch Bayesian optimization. We employ this framework to efficiently navigate the microstructure manifold in two examples involving phase-field simulations. In these examples, the volume fraction and characteristic length scale of phases resulting from spinodal decompositions are considered in different objective functions to find synthetic target microstructures. We show how this batch Bayesian optimization can be used to efficiently uncover process-microstructure connections through optimal parallel querying of the process space, providing a new pathway for solving inverse problems in materials design.

An NVM SSD-based High Performance Query Processing Framework for Search Engines

Commercial search engines generally maintain hundreds of thousands of machines equipped with large sized DRAM which incurs high hardware cost since DRAM is expensive. Recently, NVM Optane SSD has been considered as a promising underlying storage device due to its price advantage and speed advantage. However, to achieve a comparable efficiency performance with in-memory index, applying NVM to both latency and I/O bandwidth critical applications still face non-trivial challenges, because NVM has much lower I/O speed and bandwidth compared to DRAM. In this paper, we propose an NVM SSD-optimized query processing framework, aiming to address both the latency and bandwidth issues of using NVM in search engines. First, we propose a pipelined query processing methodology which significantly reduces the I/O waiting time. Second, we propose a cache-aware query reordering algorithm which enables queries sharing more data to be processed adjacently. Third, we propose a data prefetching mechanism which reduces the extra thread waiting time and improves bandwidth utilization. Moreover, we propose intra-query parallel mechanisms for long-tail queries, including query subtask scheduling, heap concurrent access strategy, query parallelism prediction and adaptive pipelining. Extensive experimental studies show that our framework significantly outperforms the state-of-the-art baselines, which obtains comparable processing latency and throughput with DRAM in both inter-query and intra-query parallel scenarios.

PARP: A Parallel Traffic Condition Driven Route Planning Model on Dynamic Road Networks

The problem of route planning on road network is essential to many Location-Based Services (LBSs). Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Thus, a practical route planning strategy is required to supply the continuous route optimization considering the historic, current, and future traffic condition. However, few existing works comprehensively take into account these various traffic conditions during the route planning. Moreover, the LBSs usually suffer from extensive concurrent route planning requests in rush hours, which imposes a pressing need to handle numerous queries in parallel for reducing the response time of each query. However, this issue is also not involved by most existing solutions. We therefore investigate a parallel traffic condition driven route planning model on a cluster of processors. To embed the future traffic condition into the route planning, we employ a GCN model to periodically predict the travel costs of roads within a specified time period, which facilitates the robustness of the route planning model against the varying traffic condition. To reduce the response time, a Dual-Level Path (DLP) index is proposed to support a parallel route planning algorithm with the filter-and-refine principle. The bottom level of DLP partitions the entire graph into different subgraphs, and the top level is a skeleton graph that consists of all border vertices in all subgraphs. The filter step identifies a global directional path for a given query based on the skeleton graph. In the refine step, the overall route planning for this query is decomposed into multiple sub-optimizations in the subgraphs passed through by the directional path. Since the subgraphs are independently maintained by different processors, the sub-optimizations of extensive queries can be operated in parallel. Finally, extensive evaluations are conducted to confirm the effectiveness and superiority of the proposal.