Parallel Sorting Algorithms Research Articles

ThSORT: an efficient parallel sorting algorithm on multi-core DSPs

thSORT: an efficient parallel sorting algorithm on multi-core DSPs

Advanced Model of Parallel Sorting Algorithm with Ranking

Advanced Model of Parallel Sorting Algorithm with Ranking

Parallel Multi-Deque Partition Dual-Deque Merge sorting algorithm using OpenMP

Quicksort is an important algorithm that uses the divide and conquer concept, and it can be run to solve any problem. The performance of the algorithm can be improved by implementing this algorithm in parallel. In this paper, the parallel sorting algorithm named the Multi-Deque Partition Dual-Deque Merge Sorting algorithm (MPDMSort) is proposed and run on a shared memory system. This algorithm contains the Multi-Deque Partitioning phase, which is a block-based parallel partitioning algorithm, and the Dual-Deque Merging phase, which is a merging algorithm without compare-and-swap operations and sorts the small data with the sorting function of the standard template library. The OpenMP library, which is an application programming interface used to develop the parallel implementation of this algorithm, is implemented in MPDMSort. Two computers (one with an Intel Xeon Gold 6142 CPU and the other with an Intel Core i7-11700 CPU) running Ubuntu Linux are used in this experiment. The results show that MPDMSort is faster than parallel balanced quicksort and multiway merge sort on the large random distribution data. A speedup of 13.81times and speedup per thread of 0.86 can be obtained. Thus, developers can use these parallel partitioning and merging algorithms to improve the performance of related algorithms.

Systematic Literature Review on Parallel Sorting and Searching Algorithms for Graph Analytics Problems

This research paper aims to systematically review the literature published recently on the topic of parallel processing algorithms for sorting and searching methods. The paper emphasizesfinding the major gaps in the existing methods of using parallel sorting and searching methods for graph analytics problems and their use in scalable data science. Many peer-reviewed research papers and articles from international and national journals, conferences and other sources are searched using appropriate keywords and they are analyzed to understand the concept, current trend in the area and to find the research gaps, by employing a systematic review method of literature. The outcomes of this literature review haveresulted in the need to undertakea research work on "Design and Implementation of Parallel Sorting and Searching Algorithms for Graph Analytics Problems in Scalable Data Science". Also, we have identified the required research objectives and its plan of execution for the research proposal. Systematic literature review, Topic of research proposal along with research objectives, Listing of SWOT analysis of the research agendas, Listing of ABCD analysis of the research proposal

Reversing an imperative concurrent programming language

We introduce a method of reversing the execution of imperative concurrent programs. Given an irreversible program, we describe the process of producing two versions. The first performs forward execution and saves information necessary for reversal. The second uses this saved information to simulate reversal. We propose using identifiers to capture statement execution order, and to use this to correctly undo data races via backtracking reversibility. We prove this reversibility to be correct, showing that the initial program state is restored and that all saved information is used (garbage-clean). • Reversal of a concurrent imperative programming language that stores minimal data to undo irreversible steps • Identifiers assigned to statements as they execute that determines reverse control flow and ensures correct reversibility • Proof of correctness confirms the method of reversal restores program states and does not produce garbage • Reversibility illustrated on an implementation of a parallel sorting algorithm, with forward and reverse execution described

Analysis and optimization of Dual Parallel Partition Sorting with OpenMP

PurposeSorting is a very important algorithm to solve problems in computer science. The most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the data into subarrays and finally sorting them.Design/methodology/approachIn this paper, the algorithm named Dual Parallel Partition Sorting (DPPSort) is analyzed and optimized. It consists of a partitioning algorithm named Dual Parallel Partition (DPPartition). The DPPartition is analyzed and optimized in this paper and sorted with standard sorting functions named qsort and STLSort which are quicksort, and introsort algorithms, respectively. This algorithm is run on any shared memory/multicore systems. OpenMP library which supports multiprocessing programming is developed to be compatible with C/C++ standard library function. The authors’ algorithm recursively divides an unsorted array into two halves equally in parallel with Lomuto's partitioning and merge without compare-and-swap instructions. Then, qsort/STLSort is executed in parallel while the subarray is smaller than the sorting cutoff.FindingsIn the authors’ experiments, the 4-core Intel i7-6770 with Ubuntu Linux system is implemented. DPPSort is faster than qsort and STLSort up to 6.82× and 5.88× on Uint64 random distributions, respectively.Originality/valueThe authors can improve the performance of the parallel sorting algorithm by reducing the compare-and-swap instructions in the algorithm. This concept can be used to develop related problems to increase speedup of algorithms.

Application Analysis of the Ecological Economics Model of Parallel Accumulation Sorting and Dynamic Internet of Things in the Construction of Ecological Smart City

In order to improve the effect of ecological smart city construction, this paper analyzes the application of the ecological economic model based on parallel accumulation sorting and dynamic Internet of Things in the construction of ecological smart city. Moreover, this paper studies the core technology algorithm of information retrieval, proposes a parallel accumulation sorting algorithm, and combines the dynamic Internet of Things and ecological economics to construct an ecological smart city construction model. In addition, on the basis of constructing an ecological smart city, this paper combines the parallel accumulation sorting algorithm and the dynamic Internet of Things technology to analyze the construction effect of the ecological smart city. The experimental research results show that the ecological economic model based on parallel accumulation sorting and dynamic Internet of Things has a good application effect in the construction of ecological smart city.

Load Balancing Optimization of In-Memory Database for Massive Information Processing of Internet of Things (IoTs)

Based on the analysis of the key technologies of the Internet of Things service platform architecture, a load balancing optimization scheme of in-memory database based on the massive information processing of the Internet of Things service platform is proposed. This scheme firstly proposes a system model that can satisfy the mass sensor information processing under the open platform environment and designs several functional unit modules of the system. By combining these functional units, the service can be configured for thousands of services and tenants. This paper presents an adaptive strategy selection method, which can automatically select the optimization strategy by dividing the position and querying the selection rate to improve the efficiency of the adaptive index algorithm. The index structure is initialized by parallel sorting algorithm, and the query statement is executed and the index structure is optimized by thread level parallel and radix sort methods. An elastic pipeline technique is proposed, which includes an elastic iterator model and a dynamic scheduler. The elastic iterator model is an upgrade of the traditional iterator model, adding the characteristics of dynamic multicore execution. In the process of query processing, dynamic scheduler monitors the load of each node in real time and dynamically adjusts the parallelism, so as to realize the load balance of in-memory database and maximize the utilization of hardware resources. The elastic pipeline realizes the isolation of parallelism from query compilation to avoid inappropriate parallelism allocation caused by missing and insufficient information during query compilation.

Engineering In-place (Shared-memory) Sorting Algorithms

We present new sequential and parallel sorting algorithms that now represent the fastest known techniques for a wide range of input sizes, input distributions, data types, and machines. Somewhat surprisingly, part of the speed advantage is due to the additional feature of the algorithms to work in-place, i.e., they do not need a significant amount of space beyond the input array. Previously, the in-place feature often implied performance penalties. Our main algorithmic contribution is a blockwise approach to in-place data distribution that is provably cache-efficient. We also parallelize this approach taking dynamic load balancing and memory locality into account. Our new comparison-based algorithm In-place Parallel Super Scalar Samplesort ( IPS 4 o ) , combines this technique with branchless decision trees. By taking cases with many equal elements into account and by adapting the distribution degree dynamically, we obtain a highly robust algorithm that outperforms the best previous in-place parallel comparison-based sorting algorithms by almost a factor of three. That algorithm also outperforms the best comparison-based competitors regardless of whether we consider in-place or not in-place, parallel or sequential settings. Another surprising result is that IPS 4 o even outperforms the best (in-place or not in-place) integer sorting algorithms in a wide range of situations. In many of the remaining cases (often involving near-uniform input distributions, small keys, or a sequential setting), our new In-place Parallel Super Scalar Radix Sort ( IPS 2 Ra ) turns out to be the best algorithm. Claims to have the – in some sense – “best” sorting algorithm can be found in many papers which cannot all be true. Therefore, we base our conclusions on an extensive experimental study involving a large part of the cross product of 21 state-of-the-art sorting codes, 6 data types, 10 input distributions, 4 machines, 4 memory allocation strategies, and input sizes varying over 7 orders of magnitude. This confirms the claims made about the robust performance of our algorithms while revealing major performance problems in many competitors outside the concrete set of measurements reported in the associated publications. This is particularly true for integer sorting algorithms giving one reason to prefer comparison-based algorithms for robust general-purpose sorting.

Peculiarities of the Parallel Sorting Algorithm with Rank Formation

Peculiarities of the Parallel Sorting Algorithm with Rank Formation

A design implementation of the Central Process Module for an In-Beam PET in HICTD

To monitor the irradiation dose and position accurately delivered to patient during particle therapy in Heavy-Ion Cancer Therapy Device (HICTD) in Lanzhou, China, an In-Beam Positron Emission Tomography (PET) has been designed as a detection device to obtain time, energy and position information of gamma ray generated from the annihilation event, which is equipped at the beam line of HICTD. Sixteen detection blocks are adopted to construct a Dual-Head Planar-Type detector array in the In-Beam PET, and each detection block is composed of pixel detection elements. Data Acquisition Unit (DAQU) is employed as the processing module to process and digitize signals from detection blocks. Each DAQU faces a pair of detection blocks, there are eight DAQUs in total in this PET at present. A Central Processor Module (CPM) developed for the detector array is presented in this paper. CPM has two crucial functions: first it is as a hub to connect with all DAQUs through the optical links for data transfer and state control, second it is as a data process engine which collects data from different DAQUs and sorts the events into the chronological order for coincidence event discrimination, and then interfaces both control signals and event data to a host server through PCIe interface for image reconstruction. A novel parallel sorting algorithm has been explored and adopted in CPM, which can effectively sort the valid events detected by detection blocks. The key performance of the CPM has been evaluated in experiments. It can handle the events up to 16 million per second at least, which is capable of sorting and discriminating coincidence events efficiently.

High-performance genome sorting program

This paper is devoted to the practical application of parallel sorting algorithms and parallel input-output methods for the problem of genome alignment. The paper considers different approaches to the implementation of such algorithms, taking into account the capabilities of high-performance systems. Main purpose of the work is to develop a genome sorting program, the efficiency of which significantly exceeds the efficiency of free software analogues. The genome sorting program is implemented for a supercomputer using the C++ language and the OpenMP and OpenMPI. The developed program demonstrates a significant increase in the speed of operation (up to 10 times) compared to free software analogues due to massive parallel data input and output. Different approaches for data input/output parallelization and data processing considered in the paper can be applied in other subject areas.

A MultiStack Parallel (MSP) Partition Algorithm Applied to Sorting

The CPUs of smartphones are becoming multicore with huge RAM and storage to support a variety of multimedia applications in the near future. A MultiStack Parallel (MSP) sorting algorithm is proposed and named MSPSort to support manycore systems. It can be regarded as many threads of single-pivot interleaving block-based Hoare’s algorithm. Each thread performs compare-swap operations between left and right (stacked and interleaved) data blocks. A number of multithreading features of OpenMP and our own optimization strategies have been utilized. To simulate those smartphones, MSPSort is fine tuned and tested on four Linux systems, e.g. Intel i7-2600, Xeon X5670, AMD R7-1700 and R9-2920. Their memory configurations can be classified as either uniform or non-uniform memory access. The statistical results are satisfied compared to parallel-mode sorting algorithms of Standard Template Library, namely Balanced QuickSort and MultiWay MergeSort. Moreover, MSPSort looks promising to be developed further to improve both run time and stability.

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.

A Sorting Network on Trees

Sorting networks are a class of parallel oblivious sorting algorithms. Not only do they have interesting theoretical properties but they can be fabricated. A sorting network is a sequence of parallel compare-exchange operations using comparators which are grouped into stages. This underlying graph defines the topology of the network. The majority of results on sorting networks concern the unrestricted case where the underlying graph is the complete graph. Prior results are also known for paths, hypercubes, and meshes. In this paper we introduce a sorting network whose underlying topology is a tree and formalize the concept of sorting networks on a restricted graph topology by introducing a new parameter for graphs called its sorting number. The main result of the paper is a description of an [Formula: see text] depth sorting network on a tree with maximum degree [Formula: see text].

Energy performance of parallel sort algorithms

This article shows the energy consumption analysis by parallel sorting algorithms. Sort algorithms are used in information systems and databases, to select and organize the information. The tests carried out confirm the reduction of energy consumption by using parallel sorting algorithms.

Engineering Algorithms for Scalability through Continuous Validation of Performance Expectations

Many libraries in the HPC field use sophisticated algorithms with clear theoretical scalability expectations. However, hardware constraints or programming bugs may sometimes render these expectations inaccurate or even plainly wrong. While algorithm and performance engineers have already been advocating the systematic combination of analytical performance models with practical measurements for a very long time, we go one step further and show how this comparison can become part of automated testing procedures. The most important applications of our method include initial validation, regression testing, and benchmarking to compare implementation and platform alternatives. Advancing the concept of performance assertions, we verify asymptotic scaling trends rather than precise analytical expressions, relieving the developer from the burden of having to specify and maintain very fine-grained and potentially non-portable expectations. In this way, scalability validation can be continuously applied throughout the whole development cycle with very little effort. Using MPI and parallel sorting algorithms as examples, we show how our method can help uncover non-obvious limitations of both libraries and underlying platforms.

Performance Evaluation of Parallel Bubble Sort Algorithm on Supercomputer IMAN1

Parallel sorting algorithms order a set of elements USING MULTIPLE processors in order to enhance the performance of sequential sorting algorithms. In general, the performance of sorting algorithms are EVALUATED IN term of algorithm growth rate according to the input size. In this paper, the running time, parallel speedup and parallel efficiency OF PARALLEL bubble sort is evaluated and measured. Message Passing Interface (MPI) IS USED for implementing the parallel version of bubble sort and IMAN1 supercomputer is used to conduct the results. The evaluation results show that parallel bubble sort has better running time as the number of processors increases. On other hand, regarding parallel efficiency, parallel bubble sort algorithm is more efficient to be applied OVER SMALL number of processors.

Performance Evaluation of Parallel Bubble Sort Algorithm on Supercomputer IMAN1

Parallel sorting algorithms order a set of elements USING MULTIPLE processors in order to enhance the performance of sequential sorting algorithms. In general, the performance of sorting algorithms are EVALUATED IN term of algorithm growth rate according to the input size. In this paper, the running time, parallel speedup and parallel efficiency OF PARALLEL bubble sort is evaluated and measured. Message Passing Interface (MPI) IS USED for implementing the parallel version of bubble sort and IMAN1 supercomputer is used to conduct the results. The evaluation results show that parallel bubble sort has better running time as the number of processors increases. On other hand, regarding parallel efficiency, parallel bubble sort algorithm is more efficient to be applied OVER SMALL number of processors

ОБЧИСЛЮВАЛЬНА СКЛАДНІСТЬ МЕРЕЖЕВОЇ МОДЕЛІ СОРТУВАННЯ ЛІНІЙНОГО МАСИВУ ЧИСЕЛ

In the development of advanced software and hardware for modern computing, the interest is the improvement of methods of associative processing of information such as procedures of sorting and selection. That ensures the realization of effective search for the required information in the data arrays. The need for parallel non-processing of large amounts of information entails the appropriate organization of associative memory, as well as the development and using of perspective technical devices. The sorting is important procedure in such application areas as solving economic problems, managing databases, sorting of IP addresses in computer networks, processing signals and images (for example, in nonlinear median image filtering). The analysis of known sorting methods have shown that the most effective method of parallel sorting, taking into account its hardware implementation by the sorting network, is the pairwise exchange. At the same time, the degree of parallelism of any sorting method for its hardware implementation directly depends on the number of comparison schemes that work in parallel in each view. For a pairwise exchange method, the degree of parallelism is determined by the value ]n/2[, where n is the number of input numerical values ​​or the dimension of the input linear number array. In this article methods of implementing of the sorting algorithm by the method of pairwise exchange with the link topology between elements of the number array in the form of tape and ring are analyzed. For example, the parallel sorting algorithm using the pairwise exchange method is described. The simulation at a high-level C ++ language is done. The obtained statistical and graphic results of modeling are analyzed. The analysis of graphical modeling results shows the dependence of the form O(n) between the number of sort cycles and the dimensionality n of the input array. That confirms the effectiveness of the hardware implementation of sorting by pairwise exchange on the sorting network due to the regularity of the structure and connections in the sorting process. The ability to statistically determine not only the number of sorting cycles with a given dimension of the number array, but also the corresponding number of comparisons and transposition greatly extends the possibilities of improving the known and creating new ways of synchronous sorting of elements of a linear array by hardware in the form of a sorting network.