High-performance Programming Research Articles

Two-set inequalities for the binary knapsack polyhedra

This paper presents two-set inequalities, a class of valid inequalities for knapsack and multiple knapsack problems. Two-set inequalities are generated from two arbitrary sets of variables from a knapsack constraint. This class of cutting planes is not a traditional type of lifting since a valid inequality over a restricted space is not required to start. Furthermore, they cannot be derived using any existing lifting technique. The paper presents a quadratic algorithm to efficiently generate many two-set inequalities. Conditions for facet-defining two-set inequalities are also derived. Computational experiments tested these inequalities as pre-processing cuts versus CPLEX, a high-performance mathematical programming solver, at default settings. Overall, two-set inequalities reduced the time to solve some benchmark multiple knapsack instances to up to 80%. Computational results also showed the potential of this new class of cutting planes to solve computationally challenging binary integer programs.

Highly efficient modeling and optimization of neural fiber responses to electrical stimulation

Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. By leveraging advances in high-performance computing and parallel programming, we present a surrogate fiber model that generates spatiotemporal responses to a wide variety of cuff-based electrical peripheral nerve stimulation protocols. We used our surrogate fiber model to design stimulation parameters for selective stimulation of pig and human vagus nerves. Our approach yields a several-orders-of-magnitude improvement in computational efficiency while retaining generality and high predictive accuracy, demonstrating its robustness and potential to enhance the design and optimization of peripheral neuromodulation therapies.

Parallel implementation for real-time visual SLAM systems based on heterogeneous computing

Simultaneous localization and mapping has become rapidly developed and plays an indispensable role in intelligent vehicles. However, many state-of-the-art visual simultaneous localization and mapping (VSLAM) frameworks are very time-consuming both in front-end and back-end, especially for large-scale scenes. Nowadays, the increasingly popular use of graphics processors for general-purpose computing, and the progressively mature high-performance programming theory based on compute unified device architecture (CUDA) have given the possibility for large-scale VSLAM to solve the conflict between limited computing power and excessive computing tasks. The paper proposes a full-flow optimal parallelization scheme based on heterogeneous computing to speed up the time-consuming modules in VSLAM. Firstly, a parallel strategy for feature extraction and matching is designed to reduce the time consumption arising from multiple data transfers between devices. Secondly, a bundle adjustment method based solely on CUDA is developed. By fully optimizing memory scheduling and task allocation, a large increase in speed is achieved while maintaining accuracy. Besides, CUDA heterogeneous acceleration is fully utilized for tasks such as error computation and linear system construction in the VSLAM back-end to enhance the operation speed. Our proposed method is tested on numerous public datasets on both computer and embedded sides, respectively. A number of qualitative and quantitative experiments are performed to verify its superiority in terms of speed compared to other states-of-the-art.

Next-generation bioinformatics: An ultrafast and user-friendly tool for phylogenomic data exploration.

With increasingly large genomic datasets, even routine bioinformatic tasks can be arduous, computationally demanding, and time-consuming. Additionally, most bioinformatic programs do not have a graphical user interface (GUI) and thus, require users to be minimally competent in command-line. These impediments present significant economic and technological barriers for practitioners who do not have access to advanced computational resources and support. In this issue of Molecular Ecology Resources, Handika and Esselstyn (2024) developed an ultrafast and memory-efficient bioinformatic tool, SEGUL, that performs common manipulations and calculations of summary statistics on phylogenomic datasets. SEGUL has two main features that distinguish it from other bioinformatic programs: (1) it is based on the recently emergent, high-performance programming language Rust, and (2) it has a GUI written using Flutter, a cross-platform programming framework that also supports mobile operating systems (mobile iOS, iPadOS and Android). By leveraging and combining the power of Rust and Flutter, SEGUL achieves significantly faster computation times and lower memory usage across different platforms and CPU architectures compared to similar programs. The unique and innovative features of SEGUL pave the way for a new era of bioinformatics that can be more energy-efficient, inclusive, and available to a broader swathe of users.

Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia.

Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in "Big Data" sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.

Two-dimensional magnetotelluric inversion using unstructured triangular mesh implemented in Julia

This study presents a 2-D magnetotelluric inversion code tailored for unstructured triangular meshes, developed using Julia, a high-level, high-performance programming language designed for scientific and numerical computation. The forward modeling engine utilizes a node-based finite element method to solve electromagnetic fields throughout the modeling domain. The inversion process employs the Gauss-Newton optimization algorithm, iteratively updating the model via the minimization of a regularized least-squares objective function. We verified the accuracy of the forward modeling using two reference models and performed a synthetic inversion experiment to validate our inversion method and stress the necessity of accurately handling topography-influenced data. Through its application to constructing an electrical resistivity model beneath the northwestern end of Sumatra Island, Indonesia, we demonstrate the practicality of our code for inverting field datasets.

Development of software for neuromarketing based on artificial intelligence and data science using high-performance computing and parallel programming technologies

This paper deals with the scientific research in a new area of human activity – neuromarketing. The development of new software for neuromarketing research based on modern methods and tools of artificial intelligence and data science (neural network technologies) using technologies of high-performance computing and parallel programming are considered in this paper. The results of experimental measurements of electrical activity of the consumer's brain based on electroencephalographic signals depending on various types of marketing stimuli are presented in this paper. The quality of the developed software for neuromarketing is evaluated on the basis of various quality metrics.

Design of efficient Programming Language with Lexer using ’$’-prefixed identifier

An identifier which starts with ‘$‘is known as ‘$‘-prefixed identifier and this type of identifiers are used in our research paper to improve the lexical analysis phase. This paper talks about a new programming language with ‘$‘-prefixed identifier that features a novel approach for optimizing the lexer for efficient lexical analysis which can be applied to any existing language. This approach is used to classify identifiers and keywords using ‘$‘-prefixed variables, which significantly reduces the time taken and number of iterations required during the tokenization process, leading to improved overall performance. This type of language structure allows for fast lookup and matching of tokens. We conducted a series of experiments to evaluate the performance of our lexer and compared it with a regular lexer. Our results show that our approach leads to significant improvements in time complexity and number of iterations for identifying whether the token is an identifier or a keyword, resulting in faster compilation times and improved overall performance. Our language has reduced the amount of time taken by 7-10% and 45-50% in terms of iterations. Our language and lexer represent a significant step forward in the design and implementation of high-performance programming languages by reducing the number of iterations and time taken to identify whether a token is a keyword or an identifier.

Memory-friendly fixed-point iteration method for nonlinear surface mode oscillations of acoustically driven bubbles: from the perspective of high-performance GPU programming

A fixed-point iteration technique is presented to handle the implicit nature of the governing equations of nonlinear surface mode oscillations of acoustically excited microbubbles. The model is adopted from the theoretical work of Shaw [1], where the dynamics of the mean bubble radius and the surface modes are bi-directionally coupled via nonlinear terms. The model comprises a set of second-order ordinary differential equations. It extends the classic Keller–Miksis equation and the linearized dynamical equations for each surface mode. Only the implicit parts (containing the second derivatives) are reevaluated during the iteration process. The performance of the technique is tested at various parameter combinations. The majority of the test cases needs only a single reevaluation to achieve 10-9 error. Although the arithmetic operation count is higher than the Gauss elimination, due to its memory-friendly matrix-free nature, it is a viable alternative for high-performance GPU computations of massive parameter studies.

Analisis Watermarking Menggunakan Metode Discrete Cosine Transform (DCT) dan Discrete Fourier Transform (DFT)

Digital image watermarking is the insertion of watermarks into digital image media. Several types of watermarking methods used are Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT). Both of these watermarking methods work in the frequency domain (transform). Digital image watermarking using the frequency domain is carried out on the frequency coefficient. This study used 30 digital image data as material for digital image watermaking analysis with 10 data each in binary, grayscale and color digital images in jpg, png and bmp formats. Digital images in the binary and grayscale domains are conversions from digital images in the true color (RGB) domain. Digital image watermarking includes three main processes, namely embedding the watermarked image on the original digital image, extracting the watermarked image and measuring the correlation between the two digital images. Correlation aims to measure two variables that have the same relationship. The technology used in this research work is MATLAB (Matrix Laboratory) as a high-performance programming language for computing in solving problems with solutions expressed in mathematical notation. The results of the discussion prove that the watermarking process in terms of color, for DCT, RGB is better and binary is better for DFT. And the watermaking process, in terms of the type of watermark inserted, for both DCT and DFT, a good watermark is an invisible watermark.

Computer Simulation of the Seismic Wave Propagation in Poroelastic Medium

This article presents an algorithm for the numerical solution of an initial-boundary value problem for a symmetric t-hyperbolic system of partial differential equations. This problem is based on continual filtration model, which describes the propagation of seismic waves in a poroelastic medium saturated with a fluid characterized by such physical parameters as the propagation velocities of longitudinal P- (fast and slow) and transverse S-waves, the density of the medium materials, and porosity. The system of linearized equations of saturated porous media is formulated in terms of physical variables of the velocity–stress tensor of the porous matrix and the velocity–pressure of the saturating fluid in the absence of energy dissipation. The solution is implemented numerically using an explicit finite difference upwind scheme built on a staggered grid to avoid the appearance of oscillations in the solution functions. The program code implementing parallel computing is developed in the high-performance Julia programming language. The possibility of using the approach is demonstrated by the example of solving the problem of propagation of seismic waves from a source located in the formation. Computational experiments based on real data from oil reservoirs have been implemented, and dynamic visualization of solutions consistent with the first waves arrival times has been obtained.

BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations.

MotivationHigh-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language.ResultsHere, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets.Availability and implementationThe Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2.Supplementary information Supplementary data are available at Bioinformatics online.

A Flexible Water Quality Modelling Simulator Based on Matrix Algebra

For the sustainable management of aquatic ecosystems, an integrated approach is required. This is why watershed-based management is becoming an increasingly popular instrument for the improvement of water quality. Water quality models serve as a central part of the watershed management. Predictive water quality models are valuable tools, but they are usually complex infrastructures in terms of both operation and software development. The aim of this study is to develop the water quality simulator of a larger hydro-ecological modelling framework. Since the water quality problems are diverse, development of one water quality kinetics sub-model that would fit to all water quality problems would be an impossible task. This is the reason why; the water quality simulator software code was developed following the open source philosophy, implemented on a high level (yet high performance) programming language, and documented intensively in-line to enhance the code readability. The water quality simulator software, which is designed as a component of HIDROTURK integrated modelling platform, consists of a general transport sub-model, three water quality kinetics sub-models and utilities.

Towards Quantum 3D Imaging Devices

We review the advancement of the research toward the design and implementation of quantum plenoptic cameras, radically novel 3D imaging devices that exploit both momentum–position entanglement and photon–number correlations to provide the typical refocusing and ultra-fast, scanning-free, 3D imaging capability of plenoptic devices, along with dramatically enhanced performances, unattainable in standard plenoptic cameras: diffraction-limited resolution, large depth of focus, and ultra-low noise. To further increase the volumetric resolution beyond the Rayleigh diffraction limit, and achieve the quantum limit, we are also developing dedicated protocols based on quantum Fisher information. However, for the quantum advantages of the proposed devices to be effective and appealing to end-users, two main challenges need to be tackled. First, due to the large number of frames required for correlation measurements to provide an acceptable signal-to-noise ratio, quantum plenoptic imaging (QPI) would require, if implemented with commercially available high-resolution cameras, acquisition times ranging from tens of seconds to a few minutes. Second, the elaboration of this large amount of data, in order to retrieve 3D images or refocusing 2D images, requires high-performance and time-consuming computation. To address these challenges, we are developing high-resolution single-photon avalanche photodiode (SPAD) arrays and high-performance low-level programming of ultra-fast electronics, combined with compressive sensing and quantum tomography algorithms, with the aim to reduce both the acquisition and the elaboration time by two orders of magnitude. Routes toward exploitation of the QPI devices will also be discussed.

Stencil Solvers for PDEs on GPUs: An Example From Cosmology

The increasingly diverse ecosystem of high-performance architectures and programming models presents a mounting challenge for programmers seeking to accelerate scientific computing applications. Code generation offers a promising solution, transforming a simple and general representation of computations into lower level, hardware-specialized and -optimized code. We describe the philosophy set forth by the Python package Pystella, a high-performance framework built upon such tools to solve partial differential equations on structured grids. A hierarchy of abstractions provides increasingly expressive interfaces for specifying physical systems and algorithms. We present an example application from cosmology, using finite-difference methods to solve coupled hyperbolic partial differential equations on (multiple) GPUs. Such an approach may enable a broad range of domain scientists to make efficient use of increasingly heterogeneous computational resources while mitigating the drastic effort and expertise nominally required to do so.

WITHDRAWN: Malware patterns detection and prediction using cloud based deep learning for secured network environment

Abstract The today’s network environment is highly susceptible to different types of traffic from multiple sources whereby the need to analyze the packets arises. The vulnerabilities and loopholes exist in these network traffic, which are being exploited by the threat actors. Considering the ever increasing high volumes of network packets, emerging new trends and zero day attacks, there is every need to enhance the approaches for malware analysis and detection in terms of accuracy and effectiveness. The need of the hour is to optimize the malware analysis and detection techniques using metahueristic approach over a wide range of datasets from different cyber forensic sources. The work based in the existing malware detection includes signature analysis from historical datasets based on the heuristic functions or frequency analysis. In this research manuscript, the evaluation, training and prediction of the network traffic is presented using the ML (machine learning) and DL (deep learning) based implementations so that the overall security in the network can be elevated. In addition, the results are evaluated using high performance programming platforms so that the further prediction of any new type of malware can be done.

Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm

In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online.

Объектно-ориентированный подход к разработке моделей данных

The modern understanding of the object-oriented approach focuses on one of the main problems of programming – its automation. Many important and practically useful results have been achieved in this direction. Methods for creating object libraries and programming technologies have been developed, which greatly facilitate software development. This is especially evident in the development of WEB-applications. MVC technology has improved the efficiency of developing programs that link remote databases to client applications. However, this and similar technologies have one significant drawback, which is manifested in the fact that the time of obtaining data from their database often does not meet the requirements of both the developer and the user. For a long time, there have been ongoing discussions on this topic on the Internet. Various methods are proposed to speed up database calls, which, as a rule, complicate programming and are artificial methods that go beyond the basic technology, which are applicable to solving particular problems and require significant processing in each specific case. The article assumes that the reason for these problems lies in the fact that in the programming community there is a solidified opinion, the essence of which is that the relational data model, which serves as the basis for most modern DBMS, has nothing to do with the object-oriented paradigm. Therefore, developers of programming technologies use an intermediate technology called object-relational mapping (ORM), which, in their opinion, should connect the non-object database and the object program that provides the client application with the database. In fact, any way you organize your database systems is in the object-oriented paradigm. Based on these premises, the article discusses the object method of developing data models. For this purpose, the properties of data models are formulated, which are necessary for them to be object and to ensure high performance of programs that implement data processing on their basis. The concept of an abstract algebraic machine is introduced, which is a two-base algebraic system. Examples are given that illustrate the use of an abstract algebraic machine for solving various problems. The possibility is considered and a method is proposed for constructing algebraic systems for complex data structures, namely, tuples, which can be considered as elements of such data aggregates as multidimensional matrices, relations, files. In conclusion, it is concluded that the proposed approach allows one to prove the correspondence of various data models, which, in turn, makes it possible to effectively implement parallel processing of various databases on software and hardware complexes, the architecture of which is based on a multidimensional matrix computation model.

GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets.

BackgroundThe amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena.ResultsWe present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study.ConclusionsGigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies.

Numerical modeling of the convective Kelvin-Helmholtz instabilities of astropauses

In this paper we present the numerical modeling results of the problem of the hypersonic stellar wind interaction with fully ionized interstellar medium that moves with respect to the star with supersonic speed. This is the classical problem that has been firstly studied in 1970 by Baranov et al. [1] under the thin layer approximation.In this paper we present results of numerical solutions obtained with high spatial resolution of the numerical grid. The computations were performed by using GPU processors with high-performance parallel programming technology CUDA from NVIDIA. It is shown that the stationary solution depends only on two dimensionless parameters - Mach number in the interstellar medium, M∞, and adiabatic index, γ. The stationary solutions were obtained for different values of M∞ for low and medium resolution of the numerical grids. For the numerical grids of high spatial resolution the Kelvin-Helmholtz instability appears at the tangential discontinuities (both primary and secondary) in the tail part of interaction region. The instability is convective as it should be according to the linear analyses by Ruderman et al. [6]. We explore the instability evolution with increasing resolution of the numerical grid. Effects of the numerical scheme are also studied.