This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master-Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results' implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware.

• Keywords
• Coarse-Grained, Fine-Grained, Master–Slave, SCOOP, parallelized genetic algorithms,
• MeSH
• Algorithms * MeSH
• Computers * MeSH
• Publication type
• Journal Article MeSH

A novel algorithm for implementing a general type of multireference coupled-cluster (MRCC) theory based on the Jeziorski-Monkhorst exponential ansatz [Jeziorski, B.; Monkhorst, H. J. Phys. Rev. A1981, 24, 1668] is introduced. The proposed algorithm utilizes processor groups to calculate the equations for the MRCC amplitudes. In the basic formulation, each processor group constructs the equations related to a specific subset of references. By flexible choice of processor groups and subset of reference-specific sufficiency conditions designated to a given group, one can ensure optimum utilization of available computing resources. The performance of this algorithm is illustrated on the examples of the Brillouin-Wigner and Mukherjee MRCC methods with singles and doubles (BW-MRCCSD and Mk-MRCCSD). A significant improvement in scalability and in reduction of time to solution is reported with respect to recently reported parallel implementation of the BW-MRCCSD formalism [Brabec, J.; van Dam, H. J. J.; Kowalski, K.; Pittner, J. Chem. Phys. Lett.2011, 514, 347].

• Publication type
• Journal Article MeSH

Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity-time curves, acquired by a rubber process analyser for styrene-butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.

• Keywords
• curing process, generalised regression neural network, intelligent modelling, parallel computing, rubber blends,
• Publication type
• Journal Article MeSH

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

• Keywords
• GPU, MRI, parallel processing, review,
• MeSH
• Algorithms * MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Brain MeSH
• Computer Graphics * MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Software MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

The most common way to calculate charge distribution in a molecule is ab initio quantum mechanics (QM). Some faster alternatives to QM have also been developed, the so-called "equalization methods" EEM and ABEEM, which are based on DFT. We have implemented and optimized the EEM and ABEEM methods and created the EEM SOLVER and ABEEM SOLVER programs. It has been found that the most time-consuming part of equalization methods is the reduction of the matrix belonging to the equation system generated by the method. Therefore, for both methods this part was replaced by the parallel algorithm WIRS and implemented within the PVM environment. The parallelized versions of the programs EEM SOLVER and ABEEM SOLVER showed promising results, especially on a single computer with several processors (compact PVM). The implemented programs are available through the Web page http://ncbr.chemi.muni.cz/~n19n/eem_abeem.

• Publication type
• Journal Article MeSH

This paper presents a parallel implementation of a non-local transform-domain filter (BM4D). The effectiveness of the parallel implementation is demonstrated by denoising image series from computed tomography (CT) and magnetic resonance imaging (MRI). The basic idea of the filter is based on grouping and filtering similar data within the image. Due to the high level of similarity and data redundancy, the filter can provide even better denoising quality than current extensively used approaches based on deep learning (DL). In BM4D, cubes of voxels named patches are the essential image elements for filtering. Using voxels instead of pixels means that the area for searching similar patches is large. Because of this and the application of multi-dimensional transformations, the computation time of the filter is exceptionally long. The original implementation of BM4D is only single-threaded. We provide a parallel version of the filter that supports multi-core and many-core processors and scales on such versatile hardware resources, typical for high-performance computing clusters, even if they are concurrently used for the task. Our algorithm uses hybrid parallelisation that combines open multi-processing (OpenMP) and message passing interface (MPI) technologies and provides up to 283× speedup, which is a 99.65% reduction in processing time compared to the sequential version of the algorithm. In denoising quality, the method performs considerably better than recent DL methods on the data type that these methods have yet to be trained on.

• Keywords
• high-performance computing, image denoising, medical imaging, parallel implementation, volumetric data,
• Publication type
• Journal Article MeSH

A new reconstruction method for parallel MRI called PROBER is proposed. The method PROBER works in an image domain similar to methods based on Sensitivity Encoding (SENSE). However, unlike SENSE, which first estimates the spatial sensitivity maps, PROBER approximates the reconstruction coefficients directly by B-splines. Also, B-spline coefficients are estimated at once in order to minimize the reconstruction error instead of estimating the reconstruction in each pixel independently (as in SENSE). This makes the method robust to noise in reference images. No presmoothing of reference images is necessary. The number of estimated parameters is reduced, which speeds up the estimation process. PROBER was tested on simulated, phantom, and in vivo data. The results are compared with commercial implementations of the algorithms SENSE and GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) in terms of elapsed time and reconstruction quality. The experiments showed that PROBER is faster than GRAPPA and SENSE for images wider than 150x150 pixels for comparable reconstruction quality. With more basis functions, PROBER outperforms both SENSE and GRAPPA in reconstruction quality at the cost of slightly increased computational time.

• MeSH
• Algorithms MeSH
• Artifacts MeSH
• Time Factors MeSH
• Gadolinium DTPA MeSH
• Adult MeSH
• Phantoms, Imaging MeSH
• Head anatomy & histology   MeSH
• Thorax anatomy & histology   MeSH
• Calibration MeSH
• Contrast Media MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Computer Simulation MeSH
• Image Processing, Computer-Assisted methods   statistics & numerical data   MeSH
• Image Enhancement methods   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH
• Names of Substances
• Gadolinium DTPA MeSH
• Contrast Media MeSH

The article deals with a method of calculation of off-axis light propagation between parallel planes using discretization of the Rayleigh-Sommerfeld integral and its implementation by fast convolution. It analyses zero-padding in case of different plane sizes. In case of memory restrictions, it suggests splitting the calculation into tiles and shows that splitting leads to a faster calculation when plane sizes are a lot different. Next, it suggests how to calculate propagation in case of different sampling rates by splitting planes into interleaved tiles and shows this to be faster than zero-padding and direct calculation. Neither the speedup nor memory-saving method decreases accuracy; the aim of the proposed method is to provide reference data that can be compared to the results of faster and less precise methods.

• Publication type
• Journal Article MeSH

AIMS: Amyloidosis is caused by deposition of abnormal protein fibrils, leading to damage of organ function. Hereditary amyloidosis represents a monogenic disease caused by germline mutations in 11 amyloidogenic precursor protein genes. One of the important but non-specific symptoms of amyloidosis is hypertrophic cardiomyopathy. Diagnostics of hereditary amyloidosis is complicated and the real cause can remain overlooked. We aimed to design hereditary amyloidosis gene panel and to introduce new next-generation sequencing (NGS) approach to investigate hereditary amyloidosis in a cohort of patients with hypertrophic cardiomyopathy of unknown significance. METHODS: Design of target enrichment DNA library preparation using Haloplex Custom Kit containing 11 amyloidogenic genes was followed by MiSeq Illumina sequencing and bioinformatics identification of germline variants using tool VarScan in a cohort of 40 patients. RESULTS: We present design of NGS panel for 11 genes (TTR, FGA, APOA1, APOA2, LYZ, GSN, CST3, PRNP, APP, B2M, ITM2B) connected to various forms of amyloidosis. We detected one mutation, which is responsible for hereditary amyloidosis. Some other single nucleotide variants are so far undescribed or rare variants or represent common polymorphisms in European population. CONCLUSIONS: We report one positive case of hereditary amyloidosis in a cohort of patients with hypertrophic cardiomyopathy of unknown significance and set up first panel for NGS in hereditary amyloidosis. This work may facilitate successful implementation of the NGS method by other researchers or clinicians and may improve the diagnostic process after validation.

• Keywords
• DNA, amyloid, haematology, molecular genetics, peripheral blood,
• MeSH
• Adult MeSH
• Amyloidosis, Familial diagnosis   genetics   MeSH
• Phenotype MeSH
• Gene Frequency MeSH
• Genetic Predisposition to Disease MeSH
• Genetic Markers MeSH
• Cardiomyopathy, Hypertrophic diagnosis   genetics   MeSH
• Polymorphism, Single Nucleotide * MeSH
• Middle Aged MeSH
• Humans MeSH
• Mutation * MeSH
• DNA Mutational Analysis methods   MeSH
• Pilot Projects MeSH
• Predictive Value of Tests MeSH
• Reproducibility of Results MeSH
• Risk Factors MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Gene Expression Profiling methods   MeSH
• Transcriptome * MeSH
• Computational Biology MeSH
• High-Throughput Nucleotide Sequencing * MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic MeSH
• Names of Substances
• Genetic Markers MeSH

In surgical practice, small metallic instruments are frequently used to perform various tasks inside the human body. We address the problem of their accurate localization in the tissue. Recent experiments using medical ultrasound have shown that this modality is suitable for real-time visualization of anatomical structures as well as the position of surgical instruments. We propose an image-processing algorithm that permits automatic estimation of the position of a line-segment-shaped object. This method was applied to the localization of a thin metallic electrode in biological tissue. We show that the electrode axis can be found through maximizing the parallel integral projection transform that is a form of the Radon transform. To accelerate this step, hierarchical mesh-grid algorithm is implemented. Once the axis position is known, localization of the electrode tip is performed. The method was tested on simulated images, on ultrasound images of a tissue mimicking phantom containing a metallic electrode, and on real ultrasound images from breast biopsy. The results indicate that the algorithm is robust with respect to variations in electrode position and speckle noise. Localization accuracy is of the order of hundreds of micrometers and is comparable to the ultrasound system axial resolution.

• MeSH
• Phantoms, Imaging MeSH
• Prosthesis Implantation methods   MeSH
• Electrodes, Implanted * MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Ultrasonography, Interventional instrumentation   methods   MeSH
• Humans MeSH
• Reproducibility of Results MeSH
• Sensitivity and Specificity MeSH
• Image Enhancement methods   MeSH
• Imaging, Three-Dimensional methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH