Parallel Tool Research Articles

An assessment of large language models for OpenMP-based code parallelization: a user perspective

Large language models have sparked a lot of attention in the research community in recent days, especially with the introduction of practical tools such as ChatGPT and Github Copilot. Their ability to solve complex programming tasks was also shown in several studies and commercial solutions increasing the interest in using them for software development in different fields. High performance computing is one of such fields, where parallel programming techniques have been extensively used to utilize raw computing power available in contemporary multicore and manycore processors. In this paper, we perform an evaluation of the ChatGPT and Github Copilot tools for OpenMP-based code parallelization using a proposed methodology. We used nine different benchmark applications which represent typical parallel programming workloads and compared their OpenMP-based parallel solutions produced manually and using ChatGPT and Github Copilot in terms of obtained speedup, applied optimizations, and quality of the solution. ChatGPT 3.5 and Github Copilot installed with Visual Studio Code 1.88 were used. We concluded that both tools can produce correct parallel code in most cases. However, performance-wise, ChatGPT can match manually produced and optimized parallel code only in simpler cases, as it lacks a deeper understanding of the code and the context. The results are much better with Github Copilot, where much less effort is needed to obtain correct and performant parallel solution.

Entropy-Driven Catalytic G-Quadruple Cycle Amplification Integrated with Ligases for Label-Free Detection of Single Nucleotide Polymorphisms.

G-Quadruplex/thioflavin (G4/THT) has become a very promising label-free fluorescent luminescent element for nucleic acid detection due to its good programmability and compatibility. However, the weak fluorescence efficiency of single-molecule G4/THT limits its potential applications. Here, we developed an entropy-driven catalytic (EDC) G4 (EDC-G4) cycle amplification technology as a universal label-free signal amplification and output system by properly programming classical EDC and G4 backbone sequences, preintegrated ligase chain reaction (LCR) for label-free sensitive detection of single nucleotide polymorphisms (SNPs). First, the positive strand LCR enabled specific transduction and preliminary signal amplification from single-base mutation information to single-strand information. Subsequently, the EDC-G4 cycle amplification reaction was activated, accompanied by the production of a large number of G4/THT luminophores to output fluorescent signals. The EDC-G4 system was proposed to address the weak fluorescence of G4/THT and obtain a label-free fluorescence signal amplification. The dual-signal amplification effect enabled the LCR-EDC-G4 detection system to accurately detect mutant target (MT) at concentrations as low as 22.39 fM and specifically identify 0.01% MT in a mixed detection pool. Moreover, the LCR-EDC-G4 system was further demonstrated for its potential application in real biological samples. Therefore, this study not only contributes ideas for the development of label-free fluorescent biosensing strategies but also provides a high-performance and practical SNP detection tool in parallel.

Benchmarking kinship estimation tools for ancient genomes using pedigree simulations.

There is growing interest in uncovering genetic kinship patterns in past societies using low-coverage palaeogenomes. Here, we benchmark four tools for kinship estimation with such data: lcMLkin, NgsRelate, KIN, and READ, which differ in their input, IBD estimation methods, and statistical approaches. We used pedigree and ancient genome sequence simulations to evaluate these tools when only a limited number (1 to 50 K, with minor allele frequency ≥0.01) of shared SNPs are available. The performance of all four tools was comparable using ≥20 K SNPs. We found that first-degree related pairs can be accurately classified even with 1 K SNPs, with 85% F1 scores using READ and 96% using NgsRelate or lcMLkin. Distinguishing third-degree relatives from unrelated pairs or second-degree relatives was also possible with high accuracy (F1 > 90%) with 5 K SNPs using NgsRelate and lcMLkin, while READ and KIN showed lower success (69 and 79% respectively). Meanwhile, noise in population allele frequencies and inbreeding (first-cousin mating) led to deviations in kinship coefficients, with different sensitivities across tools. We conclude that using multiple tools in parallel might be an effective approach to achieve robust estimates on ultra-low-coverage genomes.

PARamrfinder: detecting allele-specific DNA methylation on multicore clusters

The discovery of Allele-Specific Methylation (ASM) is an important research field in biology as it regulates genomic imprinting, which has been identified as the cause of some genetic diseases. Nevertheless, the high computational cost of the bioinformatic tools developed for this purpose prevents their application to large-scale datasets. Hence, much faster tools are required to further progress in this research field. In this work we present PARamrfinder, a parallel tool that applies a statistical model to identify ASM in data from high-throughput short-read bisulfite sequencing. It is based on the state-of-the-art sequential tool amrfinder, which is able to detect ASM at regional level from Bisulfite Sequencing (BS-Seq) experiments in the absence of Single Nucleotide Polymorphism information. PARamrfinder provides the same Allelically Methylated Regions as amrfinder but at significantly reduced runtime thanks to exploiting the compute capabilities of common multicore CPU clusters and MPI RMA operations to attain an efficient dynamic workload balance. As an example, our tool is up to 567 times faster for real data experiments on a cluster with 8 nodes, each one containing two 16-core processors. The source code of PARamrfinder, as well as a reference manual, is available at https://github.com/UDC-GAC/PARamrfinder.

Adaptive patch grid strategy for parallel protein folding using atomic burials with NAMD

The definition of protein structures is an important research topic in molecular biology currently, since there is a direct relationship between the function of the protein in the organism and the 3D geometric configuration it adopts. The transformations that occur in the protein structure from the 1D configuration to the 3D form are called protein folding. Ab initio protein folding methods use physical forces to model the interactions among the atoms that compose the protein. In order to accelerate those methods, parallel tools such as NAMD were proposed. In this paper, we propose two contributions for parallel protein folding simulations: (a) adaptive patch grid (APG) and (b) the addition of atomic burials (AB) to the traditional forces used in the simulation. With APG, we are able to adapt the simulation box (patch grid) to the current shape of the protein during the folding process. AB forces relate the 3D protein structure to its geometric center and are adequate for modeling globular proteins. Thus, adding AB to the forces used in parallel protein folding potentially increases the quality of the result for this class of proteins. APG and AB were implemented in NAMD and tested in supercomputer environments. Our results show that, with APG, we are able to reduce the execution time of the folding simulation of protein 4LNZ (5,714 atoms, 15 million time steps) from 12 hours and 36 minutes to 11 hours and 8 minutes, using 16 nodes (256 CPU cores). We also show that our APG+AB strategy was successfully used in a realistic protein folding simulation (1.7 billion time steps).

On the Influence of Wave-Shaped Tool Path Strategies on Geometric Accuracy in Incremental Sheet Forming

In incremental sheet forming (ISF), the geometrical accuracy is still a challenge that is only solved for specific applications. The underlying mechanisms of geometrical defects in ISF are very complex and still not fully understood. Nevertheless, the process understanding is constantly evolving. Recent work has shown, for example, how bending moments resulting from residual stresses affect geometric accuracy. It has become clear that resulting bending moments with an axis parallel to the main tool path direction are dominant. Based on that, the current paper investigates the hypothesis that linear and parallel tool paths lead to an unfavourable accumulation of residual bending moments along a common axis, and whether this accumulation effect can be reduced by wave-shaped tool paths. Thus, the described research investigates the influence of novel path strategies on the residual bending moments and the resulting geometrical deviations. The path strategies are based on wave-shaped path lines, whereas the curvature is within the sheet plane. The investigations focussed on a rectangular sheet that is clamped at its shortest edges and a part geometry-sensitive to springback. Experimental and numerical investigations show a significantly positive influence of some investigated path strategies on the geometric deviation, compared to a conventional path strategy.

Assessing the Human Health Benefits of Climate Mitigation, Pollution Prevention, and Biodiversity Preservation.

Since the Industrial Revolution, humanity has amassed great wealth and achieved unprecedented material prosperity. These advances have come, however, at great cost to the planet. They are guided by an economic model that focuses almost exclusively on short-term gain, while ignoring natural capital and human capital. They have relied on the combustion of vast quantities of fossil fuels, massive consumption of the earth's resources, and production and environmental release of enormous quantities of chemicals, pesticides, fertilizers, and plastics. They have caused climate change, pollution, and biodiversity loss, the "Triple Planetary Crisis". They are responsible for more than 9 million premature deaths per year and for widespread disease - impacts that fall disproportionately upon the poor and the vulnerable. To map the human health impacts of climate change, pollution, and biodiversity loss. To outline a framework for assessing the health benefits of interventions against these threats. Actions taken by national governments and international agencies to mitigate climate change, pollution, and biodiversity loss can improve health, prevent disease, save lives, and enhance human well-being. Yet assessment of health benefits is largely absent from evaluations of environmental remediation programs. This represents a lost opportunity to quantify the full benefits of environmental remediation and to educate policy makers and the public. We recommend that national governments and international agencies implementing interventions against climate change, pollution, and biodiversity loss develop metrics and strategies for quantifying the health benefits of these interventions. We recommend that they deploy these tools in parallel with assessments of ecologic and economic benefits. Health metrics developed by the Global Burden of Disease (GBD) study may provide a useful starting point.Incorporation of health metrics into assessments of environmental restoration will require building transdisciplinary collaborations. Environmental scientists and engineers will need to work with health scientists to establish evaluation systems that link environmental and economic data with health data. Such systems will assist international agencies as well as national and local governments in prioritizing environmental interventions.

Prediction of the 3D conformation of a small peptide vaccine targeting Aβ42 oligomers.

The original etiology of Alzheimer's disease (AD) is the deposition of amyloid-beta (Aβ) proteins, which starts from the aggregation of the Aβ oligomers. The optimal therapeutic strategy targeting Aβ oligomer aggregation is the development of AD vaccines. Despite the fact that positive progress has been made for experimental attempts at AD vaccines, the physicochemical and even structural properties of these AD vaccines remain unclear. In this study, through immunoinformatic and molecular dynamics (MD) simulations, we first designed and simulated an alternative of vaccine TAPAS and found that the structure of the alternative can reproduce the 3D conformation of TAPAS determined experimentally. Meanwhile, immunoinformatic methods were used to analyze the physicochemical properties of TAPAS, including immunogenicity, antigenicity, thermal stability, and solubility, which confirm well the efficacy and safety of the vaccine, and validate the scheme reliability of immunoinformatic and MD simulations in designing and simulating the TAPAS vaccine. Using the same scheme, we predicted the 3D conformation of the optimized ACI-24 peptide vaccine, an Aβ peptide with the first 15 residues of Aβ42 (Aβ1-15). The vaccine was verified once to be effective against both full-length Aβ1-42 and truncated Aβ4-42 aggregates, but an experimental 3D structure was absent. We have also explored the immune mechanism of the vaccine at the molecular level and found that the optimized ACI-24 and its analogues can block the growth of either full-length Aβ1-42 or truncated Aβ4-42 pentamer by contacting the hydrophobic residues within the N-terminus and β1 region on the contact surface of either pentamer. Additionally, residues (D1, D7, S8, H13, and Q15) were identified as the key residues of the vaccine to contact either of the two Aβ oligomers. This work provides a feasible implementation scheme of immunoinformatic and MD simulations for the development of AD small peptide vaccines, validating the power of the scheme as a parallel tool to the experimental approaches and injecting molecular-level information into the understanding and design of anti-AD vaccines.

SeQual-Stream: approaching stream processing to quality control of NGS datasets

BackgroundQuality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing.ResultsIn this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations on genomic datasets in a fast, distributed and scalable way. To do so, our approach relies on the Apache Spark framework and the Hadoop Distributed File System (HDFS) to fully exploit the stream paradigm and accelerate the preprocessing of large datasets as they are being downloaded and/or copied to HDFS. The experimental results have shown significant improvements in the execution times of SeQual-Stream when compared to a batch processing tool with similar quality control features, providing a maximum speedup of 2.7times when processing a dataset with more than 250 million DNA sequences, while also demonstrating good scalability features.ConclusionOur solution provides a more scalable and higher performance way to carry out quality control of large genomic datasets by taking advantage of stream processing features. The tool is distributed as free open-source software released under the GNU AGPLv3 license and is publicly available to download at https://github.com/UDC-GAC/SeQual-Stream.

Optimization Techniques for Model Checking Leads-to Properties in a Stratified Way

We devised the L +1-layer divide & conquer approach to leads-to model checking ( L +1-DCA2L2MC) and its parallel version, and developed sequential and parallel tools for L +1-DCA2L2MC. In a temporal logic called UNITY , designed by Chandy and Misra, the leads-to temporal connective plays an important role and many case studies have been conducted in UNITY, demonstrating that many systems requirements can be expressed as leads-to properties. Hence, it is worth dedicating to these properties. Counterexample generation is one of the main tasks in the L +1-DCA2L2MC technique that can be optimized to improve its running performance. This article proposes a technique to find all counterexamples at once in model checking with a new model checker. Furthermore, layer configuration selection is essential to make the best use of the L +1-DCA2L2MC technique. This work also proposes an approach to finding a good layer configuration for the technique with an analysis tool. Some experiments are conducted to demonstrate the power and usefulness of the two optimization techniques, respectively. Moreover, our sequential and parallel tools are compared with SPIN and LTSmin model checkers, showing a promising way to mitigate the state space explosion and improve the running performance of model checking when dealing with large state spaces.

TaxonWorks in its 10th Year: What’s new, what’s next?

TaxonWorks in its 10th Year: What’s new, what’s next?

Nanopore-Enabled Dark-Field Digital Sensing of Nanoparticles.

In this study, we use nanopore arrays as a platform for detecting and characterizing individual nanoparticles (NPs) in real time. Dark-field imaging of nanopores with dimensions smaller than the wavelength of light occurs under conditions where trans-illumination is blocked, while the scattered light propagates to the far-field, making it possible to identify nanopores. The intensity of scattering increases dramatically during insertion of AgNPs into empty nanopores, owing to their plasmonic properties. Thus, momentary occupation of a nanopore by a AgNP produces intensity transients that can be analyzed to reveal the following characteristics: (1) NP scattering intensity, which scales with the sixth power of the AgNP radius, shows a normal distribution arising from the heterogeneity in NP size, (2) the nanopore residence time of NPs, which was observed to be stochastic with no permselective effects, and (3) the frequency of AgNP capture events on a 21 × 21 nanopore array, which varies linearly with the concentration of the NPs, agreeing with the frequency calculated from theory. The lower limit of detection (LOD) for NPs was 130 fM, indicating that the measurement can be used in applications in which ultrasensitive detection is required. The results presented here provide valuable insights into the dynamics of NP transport into and out of nanopores and highlight the potential of nanopore arrays as powerful, massively parallel tools for nanoparticle characterization and detection.

A systematic review of the literature on methods and technologies for teaching parallel and distributed computing in universities

There is a growing demand for software developers who have experience writing parallel programs rather than just "parallelizing" sequential systems as computer hardware gets more and more parallel. In order to develop the skills of future software engineers, it is crucial to teach pupils parallelism in elementary computer science courses. We searched the Scopus database for articles on "teaching parallel and distributed computing" and "parallel programming," published in English between 2008 and 2019. 26 papers were included in the study after quality review. As a result, a lab course using the C++ programming language and MPI library serves as the primary teaching tool for parallel and distributed computing.

TOOLS FOR MONITORING AND QUALITY ASSURANCE OF EDUCATION IN THE CONDITIONS OF AUTONOMY OF KAZAKHSTAN’S UNIVERSITIES

The purpose of the research is to analyze the implementation and perception by the academic community of the main tools for control and quality assurance of higher education as preventive control and accreditation.Methodology. The method of analysis of secondary data obtained from open sources of the Committee for Quality Assurance in Education and Science, the Center for the Bologna Process and Academic Mobility, the Information and Analytical Center under the Ministry of Education and Science of the Republic of Kazakhstan was used. Content analysis of legislation, strategic and program documents, national reports, speeches was also carried out. In addition, a mixed research method was used as an expert survey (758 respondents) and an expert interview (14 people).The originality / value of the research lies in conducting a study based on the opinions of employees and teachers of Kazakhstani universities to determine the level of their perception of preventive control and accreditation.Findings. As a result of the analysis of the assessment of preventive control and accreditation and the perception of universities, more positive assessment of accreditation than of preventive control was revealed. The survey results proved that despite the rejection of state certification, the elements and approach in preventive control remain «bureaucratic» and «limiting the autonomy of universities».Considering the positive perception of independent accreditation by the academic community, today the question is brewing as to whether it is already relevant to use these two tools in parallel with duplicate goals (achieving quality, improving quality) in quality assurance.

ParRADMeth: Identification of Differentially Methylated Regions on Multicore Clusters.

The discovery of Differentially Methylated (DM) regions is an important research field in biology, as it can help to anticipate the risk of suffering from specific diseases. Nevertheless, the high computational cost of the bioinformatic tools developed for this purpose prevents their application to large-scale datasets. Hence, much faster tools are required to further progress in this research field. In this work we present ParRADMeth, a parallel tool that applies beta-binomial regression for the identification of these DM regions. It is based on the state-of-the-art sequential tool RADMeth, which proved superior biological accuracy compared to counterparts in previous experimental evaluations. ParRADMeth provides the same DM regions as RADMeth but at significantly reduced runtime thanks to exploiting the compute capabilities of common multicore CPU clusters. For example, our tool is up to 189 times faster for real data experiments on a cluster with 16 nodes, each one containing two eight-core processors. The source code of ParRADMeth, as well as a reference manual, are available at https://github.com/UDC-GAC/ParRADMeth.

Technology Implementation for Mental Health End Users: A Model to Guide Digital Transformation for Inpatient Mental Health Professionals.

Digital transformation is the adoption of digital technologies by an entity in an effort to increase operational efficiency. In mental health care, digital transformation entails technology implementation to improve the quality of care and mental health outcomes. Most psychiatric hospitals rely heavily on "high-touch" interventions or those that require in-person, face-to-face interaction with the patient. Those that are exploring digital mental health care interventions, particularly for outpatient care, often copiously commit to the "high-tech" model, losing the crucial human element. The process of digital transformation, especially within acute psychiatric treatment settings, is in its infancy. Existing implementation models outline the development of patient-facing treatment interventions within the primary care system; however, to our knowledge, there is no proposed or established model for implementing a new provider-facing ministration tool within an acute inpatient psychiatric setting. Solving the complex challenges within mental health care demands that new mental health technology is developed in concert with a use protocol by and for the inpatient mental health professional (IMHP; the end user), allowing the "high-touch" to inform the "high-tech" and vice versa. Therefore, in this viewpoint article, we propose the Technology Implementation for Mental-Health End-Users framework, which outlines the process for developing a prototype of an IMHP-facing digital intervention tool in parallel with a protocol for the IMHP end user to deliver the intervention. By balancing the design of the digital mental health care intervention tool with IMHP end user resource development, we can significantly improve mental health outcomes and pioneer digital transformation nationwide.

Novel Tool Path Generation Method for Pocket Machining Using Sound Field Synthesis Theory

Contour parallel tool paths have been proved to be a preferred machining strategy for their advantage of less tool retractions and less sharp turns. The traditional geometrical algorithm-based tool path generation method often makes it hard to simply and simultaneously solve the problems of self-intersection, no residual, and smoothness at the same time due to their contradictions. To address this issue, a contoured parallel tool path generation method for pocket machining is developed in this study. It is based on sound field synthesis theory inspired by the phenomenon of sound wave propagation. Firstly, the simplified medial axis (SMA) tree of the pocket is extracted and the propagation direction of each SMA segment is calculated on account of the geometric characteristics of the pocket boundary. Secondly, the final tool path is obtained through the synthesis of the sound field. Finally, the novel method is verified on five different pockets to generate a contoured parallel milling tool path. After machining these pockets and measuring the machining time, roughness, and cutting force, the experimental results demonstrate that the tool path obtained by the novel method has advantages in improving machining quality and efficiency.

PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing.

De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel peptide identification, but also a precedent task for many downstream tasks, such as vaccine and antibody studies. In recent years, neural network models for de novo peptide sequencing have manifested a remarkable ability to accommodate various data sources and outperformed conventional peptide identification tools. However, the excellent model is computationally expensive, taking up to 1 week to process about 400000 spectrums. This article presents PGPointNovo, a novel neural network-based tool for parallel de novo peptide sequencing. PGPointNovo uses data parallelization technology to accelerate training and inference and optimizes the training obstacles caused by large batch sizes. The results of extensive experiments conducted on multiple datasets of different sizes demonstrate that compared with PointNovo the excellent neural network-based de novo peptide sequencing tool, PGPointNovo, accelerates de novo peptide sequencing by up to 7.35× without precision or recall compromises. The source code and the parameter settings are available at https://github.com/shallFun4Learning/PGPointNovo. Supplementary data are available at Bioinformatics Advances online.

A simple but powerful simulated certainty equivalent approximation method for dynamic stochastic problems

We introduce a novel simulated certainty equivalent approximation (SCEQ) method for solving dynamic stochastic problems. Our examples show that SCEQ can quickly solve high‐dimensional finite‐ or infinite‐horizon, stationary or nonstationary dynamic stochastic problems with hundreds of state variables, a wide state space, and occasionally binding constraints. With the SCEQ method, a desktop computer will suffice for large problems, but it can also use parallel tools efficiently. The SCEQ method is simple, stable, and can utilize any solver, making it suitable for solving complex economic problems that cannot be solved by other algorithms.

Lessons-learnt on articulating and evaluating I4.0 developments at SME manufacturing companies

This paper describes an attempt to systematically develop a Smart Industry (SI) toolkit in conjunction with a digitization scan that identifies the digital maturity and potential improvement steps for SME manufacturing companies. Existing scan methods have been compared and based on that a new maturity measurement instrument has been proposed. In parallel tools relevant for digitization of SME manufacturing companies were selected, such that this allowed for in depth discussion with SMEs, by indicating how they could grow in maturity by implementing toolkit elements. The approach also lets SMEs contribute to toolkit development (and as such help fellow manufacturing SMEs in their development) by means of valuable lessons-learned or relevant topics for the toolkit and refinement of the scan with examples and references. The maturity scan thus becomes the representation of a shared development agenda. The results also shed some light on how to help SME companies in the Netherlands in their transition towards Smart Manufacturing by means of a jointly developed SI toolkit.