Array Data Structure Research Articles

A parallel and performance portable implementation of a full-field crystal plasticity model

We have developed a parallel implementation of an Elasto-Viscoplastic Fast Fourier Transform-based (EVPFFT) micromechanical solver to enable computationally efficient crystal plasticity modeling for polycrystalline materials. Our primary focus lies in achieving performance portability, allowing a single EVPFFT implementation to run optimally on various homogeneous architectures, including multi-core Central Processing Units (CPUs), as well as on heterogeneous computer architectures comprising multi-core CPUs and Graphics Processing Units (GPUs) from different vendors. To accomplish this goal, we have leveraged MATAR, a C++ software library that simplifies the creation and utilization of multidimensional dense or sparse matrix and array data structures. These data structures are designed to be portable across diverse architectures through the use of Kokkos, a performance-portable library. Additionally, we have employed the Message Passing Interface (MPI) to efficiently distribute the computational workload among processors. The heFFTe (Highly Efficient FFT for Exascale) library is used to facilitate the performance portability of the fast Fourier transforms (FFTs) computation. The computational performance of EVPFFT is evaluated and presented in terms of parallel scalability and simulation runtime on different high-performance computing (HPC) architectures. The utility of the developed framework to efficiently simulate the micro-mechanical fields in polycrystalline microstructures in engineering applications is discussed. Program summaryProgram Title: EVPFFTCPC Library link to program files:https://doi.org/10.17632/2k8579fyyv.1Developer's repository link:https://github.com/lanl/FierroLicensing provisions: BSD 3-Clause LicenseProgramming language: C++External routines/libraries: MPI, Kokkos, MATAR, HeFFTe, HDF5Nature of problem: EVPFFT is a crystal plasticity code designed to compute micro-mechanical fields within a polycrystalline representative volume element (RVE) and predict the macroscale response of the RVE.Solution method: EVPFFT uses the periodic Green's function method in Fourier space to solve the field equations of static stress equilibrium in a periodic spatial domain.

A Python package for the local multiscale analysis of spatial point processes (LomPy)

The consideration of multiscale characteristics has emerged as a popular component of geospatial data analysis and modeling. However, the practical implementation of such analysis tasks involves time-consuming and computationally intensive processes that require the integration of knowledge and methods from different disciplines (e.g., quantitative geography, signal processing, and natural sciences) and in which large amounts of data have to be processed. Yet, to date, there is few open-source software that enables an efficient and transparent computational workflow. This paper introduces a Python package for the local multiscale analysis of spatial point processes (LomPy). LomPy is specifically designed for processing and analyzing data that is either irregularly spaced or has large data holes over the spatial territory: a common and methodologically challenging property of geoprocessing operations. The first main function of the package computes the multiresolution quantities, while the second applies them to the extraction of fractal and multifractal features at arbitrarily “local” spatial scales. The third function extends the univariate analysis option to multivariate settings. Powerful tools for regression modeling, density estimation, and visualization are also provided. It should be emphasized that the package is efficient on both vector and raster data structures, ensuring a wide range of applicability in urban data science and beyond.

A novel PV array reconfiguration technique based on circular array data structure

Partial shading condition (PSC) poses a significant challenge to solar power generation as it has a severe impact on the performance of photovoltaic (PV) systems, leading to a decrease in energy yield and reliability. Reconfiguration of PV arrays is a potential technique to deal with this challenge, however, reconfiguration techniques proposed in the past are restricted in their suitability for practical implementations in real-world installations as they are only optimal for specific shading patterns. To overcome this limitation, the article introduces a novel reconfiguration approach inspired by the circular array data structure, a commonly utilized tool in the realm of computer science. The proposed circular array transformation (CAT) technique shows great potential in mitigating the impact of partial shading (PS) and maximizing the overall power generation across a wider range of PSCs. The effectiveness of the proposed technique is validated through simulations and experimental studies conducted on a 5 × 5 PV array. Furthermore, the scalability of the proposed reconfiguration technique is verified through extensive simulations on a 9 × 9 PV array. Comparative analysis reveals that the proposed CAT rearrangement approach exhibits clear dominance and outperforms existing and recently published state-of-the-art reconfiguration methods.

Vector-deductive Memory-based Transactions for Fault-as-address Simulation

The main idea is to create logic-free vector computing, using only read-write transactions on address memory. The strategic goal is to create a deterministic vector-quantum computing using photons for read-write transactions on stable subatomic memory elements. The main task is to implement new vector computing models and methods based on primitive read-write transactions in vector flexible interpretive fault modeling and simulation technology, where data is used as addresses for processing the data itself. The essence of vector computing is read-write transactions on vector data structures in address memory. Vector computing is a computational process based on elementary read-write transactions over cells of binary vectors that are stored in address memory and form a functionality where the input data to be processed is the addresses of these cells. The advantages of a vector universal model for a compact description of ordered processes, phenomena, functions, and structures are defined for the purpose of their parallel analysis. Analytical expressions of logic, which require algorithmically complex calculators, are replaced by output state vectors of elements and digital circuits, focused on the parallelism of register logical procedures on regular data structures. A vector-deductive method for formula synthesis for propagating input lists (data) of faults is proposed, which has a quadratic computational complexity of register operations. A new matrix of deductive vectors has been synthesized, which is characterized by the following properties: compactness, parallel data processing based on a single read-write transaction in memory, elimination of traditional logic from fault simulation procedures, full automation of its synthesis process, and focus on technological solving all problems of technical diagnosis. In the work, the transition to vector logic in the organization of computing and the elimination of traditional logic presented in the form of tables and analytical expressions were carried out. The use of read-write transactions on memory in the absence of a command system focuses the new vector-logic computing towards deterministic quantum architectures based on stable subatomic memory particles.

Tidy Data Neatly Resolves Mass-Spectrometry's Ragged Arrays

Mass spectrometry (MS) is a powerful tool for measuring biomolecules, but the data produced is often difficult to handle computationally because it is stored as a ragged array. In R, this format is typically encoded in complex S4 objects built around environments, requiring an extensive background in R to perform even simple tasks. However, the adoption of tidy data [@wickham2014] provides an alternate data structure that is highly intuitive and works neatly with base R functions and common packages, as well as other programming languages. Here, we discuss the current state of R-based MS data processing, the convenience and challenges of integrating tidy data techniques into MS data processing, and present [RaMS](https://CRAN.R-project.org/package=RaMS), a package that produces tidy representations of MS data.

QUANTUM DIGITAL-ANALOGUE COMPUTING

Context. Nature is the relation among processes and phenomena. Nothing exists in the universe without relations. Computer is transactions of relations between data with the help of control and execution mechanisms. Quantum relations are a superposition of particles and their states. Superposition and entanglement are equivalent concepts. Entanglement is a non-local superposition of deterministic states. A quantum computer is unconditional transactions of relations between qubit data. Quantum computer is an analog device for parallel solution of combinatorial problems. Practically oriented definitions of the quantum computer concepts are the path to development of scalable quantum parallel algorithms for combinatorial problems solving. Any algorithm can be reduced to a sequence of operations without conditions, because any truth table is a collection of a complete system of conditions-states. Any sequence of actions can always be reduced to one parallel operation. Conditions and sequences arise only when the developer wants to use previously created primitive constructs to build an always non-optimal computing unit. The paradigm of quantum computer creation is determined through the use of photonic transactions on the electrons of an atom may exclude the use of quantum logic. The evolutionary path of a quantum computer from the classical one: “memory-address-transaction” (MAT) → “electron-addresstransaction” → “electron-address-quantaction” (EAQ) → state-superposition-logic. The meeting point of classical and quantum computers is photon transactions on the structure of electrons. Everything that is calculated on a quantum computer can be calculated in parallel on a classical one on account of memory redundancy. The given example is a memory-driven algorithm for modeling digital products based on qubit-vector forms of functionality description for significant performance boost of computing processes by parallel execution of logical operations.
 Objective. Simulation of the correct SoC-component behavior based on vector representation of the logic. Formation of the triggering development of a computing based on the superposition of the classical, quantum and analog computing process, which in its development should be based on technological qubit, tabular and vector data structures for the parallel solution of combinatorial problems.
 Method. MAT-computing implements any algorithms on account of transactions (read-write) in memory. Qubit-vector models for describing functionalities, which differ from known truth tables in compactness of description and manufacturability for the implementation of parallel algorithms of the synthesis and analysis of digital devices and SoC-components.
 Results. 1) The metric of the technological data structures, focused on parallel troubleshooting in digital systems based on the usage of two logical vector operations, was proposed for the first time. 2) The metric of relations between the individual components of QC, allowing organizing a quantum deterministic computer, has been further developed. 3) Quantum architectural solutions, that allow solving coverage problems in a quasi-parallel mode, were proposed for the first time. 4) Architectural solutions based on an analog-to-digital computing, which can be used to solve the problems of the digital systems parallel analysis, have been further developed. 5) Vector-qubit structures of the logic data, that allow a quasi-parallel simulation of digital circuits, were proposed.
 Conclusions. Qubit models, quantum methods and combinatorial algorithms for technical diagnostics of digital devices have been implemented, which can significantly (up to 25%) reduce the time of test synthesis, deductive modeling of faulty and correct behavior, search for defective states by introducing an innovative idea of using qubit-vector data structures for describing logical components. Comparative assessments of qubit models and methods usage show an increase in the efficiency of algorithms for modeling digital devices compared to tabular ones. The superposition of a classical, quantum and analog computer is integrally represented, which allows to find the best solutions for recognition and decision making.

A vector method for finding sequences in big data

A technological software solution is proposed for metric search and identification of logical-temporal patterns of a business data flow by creating additional vector data structures and a parallel method for their processing. The subject of research is the methods of searching and identifying logical-temporal patterns in big data. The purpose of the study is to increase the efficiency of searching and recognizing logical-temporal patterns that semantically form business functionality in an 8-hour frame of screenshots with "garbage" data. Applied methods: apparatus of set theory and Boolean algebra, metric models for determining parameters for sets of binary vectors, elements of probability theory, theory of algorithms, software modeling. The results obtained: a method for searching and recognizing patterns based on a vector problem of character sequences that identify patterns in big data streams using unitary coding of information primitives and data; vector models are unitary-encoded data structures for describing a big data flow as Cartesian products of a set of primitive-string-markers and a discrete sequence of implementation of a given time frame. The practical significance of the work: the implementation of the vector method, which made it possible to create a pattern recognition program in a big data stream with a probability of 0.77%.

MOQUI: an open-source GPU-based Monte Carlo code for proton dose calculation with efficient data structure

Objective. Monte Carlo (MC) codes are increasingly used for accurate radiotherapy dose calculation. In proton therapy, the accuracy of the dose calculation algorithm is expected to have a more significant impact than in photon therapy due to the depth-dose characteristics of proton beams. However, MC simulations come at a considerable computational cost to achieve statistically sufficient accuracy. There have been efforts to improve computational efficiency while maintaining sufficient accuracy. Among those, parallelizing particle transportation using graphic processing units (GPU) achieved significant improvements. Contrary to the central processing unit, a GPU has limited memory capacity and is not expandable. It is therefore challenging to score quantities with large dimensions requiring extensive memory. The objective of this study is to develop an open-source GPU-based MC package capable of scoring those quantities. Approach. We employed a hash-table, one of the key-value pair data structures, to efficiently utilize the limited memory of the GPU and score the quantities requiring a large amount of memory. With the hash table, only voxels interacting with particles will occupy memory, and we can search the data efficiently to determine their address. The hash-table was integrated with a novel GPU-based MC code, moqui. Main results. The developed code was validated against an MC code widely used in proton therapy, TOPAS, with homogeneous and heterogeneous phantoms. We also compared the dose calculation results of clinical treatment plans. The developed code agreed with TOPAS within 2%, except for the fall-off and regions, and the gamma pass rates of the results were >99% for all cases with a 2 mm/2% criteria. Significance. We can score dose-influence matrix and dose-rate on a GPU for a 3-field H&N case with 10 GB of memory using moqui, which would require more than 100 GB of memory with the conventionally used array data structure.

GenASiSBasics: Object-oriented utilitarian functionality for large-scale physics simulations (Version 4)

GenASiSBasics: Object-oriented utilitarian functionality for large-scale physics simulations (Version 4)

Developing method of vector synthesis deductive logic forcomputer systems fault analysis

The article is devoted to the development of models and methods for fault analysisfor examinate testpatterns.Deductive fault simulationof digital devices is the most advanced technology that serves the field of design and testing of modern computer systems. Atthe same time, fault simulation solves the problem of assessing the quality of the test in the class of single constant defects. However, the computational complexity of obtaining deductive formulas, estimated as n3, is a rather difficult task for high-dimensional RTL-level functional circuits, so the deductive method is usually used only for digital circuits represented at the gate level. Next, we propose a vector method for synthesis deductive formulas for digital schemes represented by RTL elements. This method became possible due to the element description of any complexity in the form ofoutput states vector forcombinational device.The model of xor-relationships between the wonderful logical functions (or, xor, and) of digital objects is improved, which is convolutedinto zero-space. Itmakes possible to solve the problems of design and test, machine learning, search for similarities-differences, and destructive components in processes and phenomena. The advantages of the vector model for a compact description of objects, functions and structures are determined. It is proposed to replace analytical expressions thatrequire algorithmically complex calculating, with vector data structures for describing functional logic. Vector-deductive method for synthesisformulas fortransporting input fault lists is proposed.Ithas a quadratic computational complexity of register operations. The coordinate-vector model of defects is considered, not tied to input variables, which can be used for efficient processing of complex logic circuits when assessing the quality of synthesized tests. An algorithm for the synthesis of deductive vectors is presented, which differs from the known ones in the technological parallel processing simplicity of truth tables and makes it possible to create structural and logical conditions for simulating faults in digital projects of the gate, register and system description levels. An efficient method for the synthesis of a deductive truth table according to the rule L=T⊕F is proposed.Itdiffers from the known ones by using vector-coordinate parallel xor-operation.Itprovidesthe transportation of faults through a functional element of arbitrary complexity.

Optimizing Time Complexity of Searching Operation: A Noble Algorithm

By the passage of time , there are many searching algorithm and data structure evolved to make basic operations more efficient with respect to time and space complexity like storing, searching, deleting the data and they play very important role in real-world applications. Based on mechanism of searching, Searching algorithm can be classified into three types of algorithm: linear, binary, hashing, but excluding these, there are many searching techniques proposed by many researchers. The aim of all these proposed searching technique is to optimize the time complexity of searching operation generally compared to Binary Search, which have average and worst case time complexity of log(n), where n is number of elements. This paper proposes a noble searching algorithm that works for sorted array data structure and performs better in most of the cases than that of binary search. The proposed searching Technique will either find index of target element or find a small space within array space by using statistical operation, and then binary search will be applied to that small space, thus it will reduce the overall time complexity. This paper briefly discusses, proposed algorithm and it’s time complexity and comparing the efficiency of the algorithm with some of the well-known other algorithms, using graphs and tables and its practical applications. We will analyze the behavior of algorithm in different parameters like:-size of input data, Nature of elements in list, Different Case Scenario (worst, best, and avg. case) etc.

A Vector Data Model for Efficiently Rendering Large Vector Maps on Global 3D Terrain Surfaces

Visualizing vector data on 3D terrain surfaces is a basic and essential function in 3D GIS. However, due to the complexity of vector data structures, efficient and effective organization of the vector data is a key issue for the efficient display of vector data in 3D. In this paper, we present a new Vector Tiled Pyramid Model to organize and manage vector data so that they can be visualized on 3D terrain surfaces more effectively. In the Vector Tiled Pyramid Model, vector data at different scales within the same geographical extent are stored as separate levels. Each vector level in our proposed model is divided into vector tiles of fixed sizes organized in a grid index. This improves the efficiency of visualizing vector data on 3D terrain surfaces. Preliminary experimental results suggest that the proposed Vector tiled Pyramid Model, compared with the traditional vector database scheme, can help us to visualize vector data on 3D terrain surfaces more efficiently. In addition, this advantage is more evident when a vector tile at a lower level (large-scale) is requested and visualized on 3D terrain surfaces.

Performance Optimization of Sorting Algorithm using Processor Affinity in Java

Background: The processor affinity library in Java can switch ON all the processing cores available in a multi-processing environment. Using this feature will enable concurrent programmers to fully utilize the benefits of processing power available in multi-core processors. Therefore, this study aims to use processor affinity to develop four different frameworks that could optimize the efficiency of quick sorting algorithms on multi-core platforms. Methods: Benchmarking is the method used to carry out all the experiments and test the developed algorithm's efficiencies using all four frameworks. An Octa-core machine with eight (8) processing cores was used to develop and run the algorithms to measure their running times and compare their performances. JDK 12.0 is the version of Java used for development. An array data structure containing One Million Elements (1,000,000) was used as the preferred data structure. Results: The results obtained show that processor affinity can improve the performance of quick sorting algorithms by ensuring no processing core is idled during the computation of results. The results also show that processor affinity and Work-Stealing-Action perform similar functions by ensuring that available cores in a machine are fully utilized to handle tasks and improve performance. It was further found that the algorithm developed using the Executor Services framework outperformed all the three other frameworks implemented using Naïve, Fork-Join, and Sequential implementations. Conclusion: It was concluded that processor affinity improves the performances of all the four different implementations of quick sorts. It was also concluded that in the fork-join framework, Work-Stealing-Action performed by the worker-threads has an effect similar to that of affinity by ensuring that all the processing cores are fully utilized to improve performance. Further investigations can be carried out using machines with more processing cores in their CPU using a different data structure such as a Link List Array of the same or different data size to see whether the same or different conclusions can be drawn.

A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.

Supercomputer model of dynamical dusty gas with intense momentum transfer between phases based on OpenFPM library

The paper considers the solution of model gas-dynamic problems (propagation of plane sound wave, one-dimensional shock tube problem, three-dimensional problem of a point explosion in a continuous medium) in the case of a gas-dust medium. The interaction of dust and gas was taken into account using the IDIC method within the SPH method used to solve gas-dynamic equations. An important feature of the work is the use of the open computational package OpenFPM, which makes it easy to carry out parallel computations. The main advantage of this package is the ready-made (implemented by the authors of the package) and intuitive, automatically parallelizable vector data structures, the use of which is identical both in the case of calculations on a personal computer and in the case of using supercomputer resources. The paper analyzes the efficiency of parallelization of numerical solutions of the considered problems.

VECTOR-DRIVEN LOGIC AND STRUCTURE FOR TESTING AND DEDUCTIVE FAULT SIMULATION

Context. It is known that data structures are decisive for the creation of efficient parallel algorithms and high-performance computing devices. Therefore, the development of mathematically perfect and technologically simple data structures takes about 80 percent of the design time, when about 20 percent of time and material resources are spent on algorithms and their hardware-software coding. This lead to search for such primitives of data structures that will significantly simplify the parallel high-performance algorithms which are working on them. Models and methods for testing and simulation of digital systems are proposed, which containing certain advantages of quantum computing in terms of implementation of vector qubit data structures in technology of classical computational processes.
 Objective. The goal of the work is development of an innovative technology for qubit-vector synthesis and deductive analysis of tests for their verification based on vector data structures that greatly simplify algorithms that can be embedded as BIST components in digital systems on chips.
 Method. The deductive faults simulation is used to obtain analytical expressions focused on transporting fault lists through a functional or logical element based on the xor-operation, which serves as a measure of similarity-difference between a test, a function and faults which is specified in the same way in one of the formats − a table, graph, equation. A binary vector is proposed as the most technologically advanced primitive of data structures for setting logical functionality for the purpose of parallel synthesis and analysis of digital systems. The parallelism of solving combinatorial problems is a physical property of quantum computing, which in classical computing, for parallel simulation and faults diagnostics, is provided by unitary-coded data structures due to excess memory.
 Results. 1) A method of analytical synthesis of deductive logic for functional elements on the gate level and register transfer level has been developed. 2) A deductive processor for faults simulation based on transporting input lists or faults vectors to external outputs of digital circuits was proposed. 3) The qubit-vector form of logic setting and methods of qubit synthesis of deductive equations for faults simulation were described. 4) A qubit-vector method for the tests’ synthesis which is using derivatives calculated by vector coverage of logic has been developed. 5) Models and methods verification is performed on test examples in the software implementation of structures and algorithms.
 Conclusions. The scientific novelty lies in the new paradigm of the technology for the synthesis of deductive RTL logic based on metric test equation, which forms the. A vector form for structures description is introduced, which makes it possible to apply wellknown technologies for the synthesis and analysis of logical circuits tests to effectively solve the problems of graph structures testing and state machine models of digital devices. The practical significance is reflected in the examples of analytical synthesis of deductive logic for functional elements on gate level and register transfer level. A deductive processor for faults simulation which is focused on implementation as a BIST tool, which is used in online testing, simulation and fault diagnosis for digital systems on chips is proposed. A qubit-vector form of the digital systems description is proposed, which surpasses the existing methods of computing devices development in terms of the metric: manufacturability, compactness, speed and quality. A software application has been developed that implements the main testing, simulation and diagnostics services which are used in the educational process to study the advantages of qubit-vector data structures and algorithms. The computational complexity of synthesis processes and deductive formulas for logic and their usage in fault simulation are given.

Dijkstra’s Algorithm-A Case Study to Understand How Algorithms are improved

In Graph Theory, Dijkstra’s Algorithm is one of the most well-known algorithms to calculate the Shortest Path. Many researchers have used its basic algorithm and attempted to improve its time complexity. This research is targeting the most famous works of such researchers. Few commonly known simple data structures like an array, link list, priority queues, and few advanced level data structures like binomial, Fibonacci are revisited and surveyed. The main target of this research/study is the evolutionary improvements in the time complexity of these algorithms. These improvements make it a suitable candidate to see how algorithms can run efficiently and the contributions of different researchers.

Itemset Representation and Mining the Rules for Huntington’s Dataset

Association rule mining does not restrict to market basket application but it is also employed in many applications such as health, industrial, network domain and etc. In this paper, an association mining algorithm is applied to the health management domain. It helps in the decision making by producing the rules for the early detection of the disease. By checking the personal details and symptoms of the patient, association rule mining will help in prediction and diagnosing the disease at an early stage. The dataset used in this experiment is the Huntington Disease (HD) dataset, which is one of the rare diseases. The dataset needs to be stored in the memory for the computation and generation of rules. Storing the items in the memory will take 4 bytes if the array data structure is used. Furthermore, if the dataset is very large, storing each and every detail in the memory becomes speculative. It is also not cost-effective and consumes a lot of resources. One of the solutions is to present the itemset in such a way that the memory consumed is concise. The items are represented using the set representation that takes less time and memory as compared to the traditional methods. The dataset is mine using the Apriori Algorithm which produces only those itemsets which are more frequent or have a high probability of occurrence. The algorithm gives a prior knowledge of the frequent itemsets. Then, the rules will be generated from these frequent itemsets. The memory and time consumption using the set representation is compared with the array representation of itemsets. Doi: 10.28991/esj-2021-01284 Full Text: PDF

On the non‐recursive implementation of multistage sampling without replacement

Variance estimation in multistage sampling without replacement usually requires considerable computational effort. One option is to implement explicit formulas on a computer, at least for some specific sampling designs. This approach becomes quite cumbersome to handle beyond two stages, both from the formulation and computer implementation points of view. Another option is to provide a general method to compute variance estimates for any number of stages. Such an approach may involve data structures and estimators which are recursively defined.•The solution we present in this article is intended to be both general and computationally efficient by relying on a full-iterative implementation.•The definition of the estimators remains implicit as in the recursive approach, but is expressed in terms of recurrence relations translated into iterative algorithms.•These algorithms rely only on (dense) array data structures. Moreover, most of the necessary computer memory is only used during preliminary steps and is not required when performing the statistical calculations.

Gsufsort: constructing suffix arrays, LCP arrays and BWTs for string collections

BackgroundThe construction of a suffix array for a collection of strings is a fundamental task in Bioinformatics and in many other applications that process strings. Related data structures, as the Longest Common Prefix array, the Burrows–Wheeler transform, and the document array, are often needed to accompany the suffix array to efficiently solve a wide variety of problems. While several algorithms have been proposed to construct the suffix array for a single string, less emphasis has been put on algorithms to construct suffix arrays for string collections.ResultIn this paper we introduce gsufsort, an open source software for constructing the suffix array and related data indexing structures for a string collection with N symbols in O(N) time. Our tool is written in ANSI/C and is based on the algorithm gSACA-K (Louza et al. in Theor Comput Sci 678:22–39, 2017), the fastest algorithm to construct suffix arrays for string collections. The tool supports large fasta, fastq and text files with multiple strings as input. Experiments have shown very good performance on different types of strings.Conclusionsgsufsort is a fast, portable, and lightweight tool for constructing the suffix array and additional data structures for string collections.