Single-core Computer Research Articles

Accelerating electrostatic particle-in-cell simulation: A novel FPGA-based approach for efficient plasma investigations.

Particle-in-cell (PIC) simulation serves as a widely employed method for investigating plasma, a prevalent state of matter in the universe. This simulation approach is instrumental in exploring characteristics such as particle acceleration by turbulence and fluid, as well as delving into the properties of plasma at both the kinetic scale and macroscopic processes. However, the simulation itself imposes a significant computational burden. This research proposes a novel implementation approach to address the computationally intensive phase of the electrostatic PIC simulation, specifically the Particle-to-Interpolation phase. This is achieved by utilizing a high-speed Field Programmable Gate Array (FPGA) computation platform. The suggested approach incorporates various optimization techniques and diminishes memory access latency by leveraging the flexibility and performance attributes of the Intel FPGA device. The results obtained from our study highlight the effectiveness of the proposed design, showcasing the capability to execute hundreds of functional operations in each clock cycle. This stands in contrast to the limited operations performed in a general-purpose single-core computation platform (CPU). The suggested hardware approach is also scalable and can be deployed on more advanced FPGAs with higher capabilities, resulting in a significant improvement in performance.

High-performance computation toward large-scale underwater acoustics modelling

The complex nature of the ocean has long presented a challenge for researchers in the field of oceanography, including in the area of underwater acoustics. As a result, significant efforts have been made to develop accurate numerical models to better understand and study the ocean. Established models such as Acoustic Toolbox and Range-dependent Acoustic Models have proven to be effective for modelling sound propagation. However, these models were designed to run on single-core computers, and there is potential to optimise their performance on modern systems. The use of General-purpose graphics processing units (GPGPUs) and Single Instruction/Multiple Data (SIMD) computing units is one way to achieve this optimization. In addition to improving the computational speed of established models, these optimized models can also contribute to implementing more sophisticated modelling approaches, such as broad-band modelling. In this study, we will discuss the optimized performance of established codes, particularly those based on parabolic equation methods (PE), and also discuss the results of advanced modelling techniques.

Improved prediction of conopeptide superfamilies with ConoDictor 2.0.

Cone snails are among the richest sources of natural peptides with promising pharmacological and therapeutic applications. With the reduced costs of RNAseq, scientists now heavily rely on venom gland transcriptomes for the mining of novel bioactive conopeptides, but the bioinformatic analyses often hamper the discovery process. Here, we present ConoDictor 2.0 as a standalone and user-friendly command-line program. We have updated the program originally published as a web server 10 years ago using novel and updated tools and algorithms and improved our classification models with new and higher quality sequences. ConoDictor 2.0 is now more accurate, faster, multiplatform and able to deal with a whole cone snail venom gland transcriptome (raw reads or contigs) in a very short time. The new version of Conodictor also improves the identification and subsequent classification for entirely novel or relatively distant conopeptides. We conducted various tests on known conopeptides from public databases and on the published venom duct transcriptome of Conus geographus, and compared previous results with the output of ConoDictor 2.0, ConoSorter and BLAST. Overall, ConoDictor 2.0 is 4 to 8 times faster for the analysis of a whole transcriptome on a single core computer and performed better at predicting gene superfamily. ConoDictor 2.0 is available as a python 3 git folder at https://github.com/koualab/conodictor. Supplementary data are available at Bioinformatics Advances online.

Optimizing regular computations based on neural networks and Graph Traversal

In recent days we can see that multicore computers have the ability to easily manipulate digit numbers however as numbers get bigger the computation becomes more complex, the reason is that the size of both CPU registers and buses is limited. As a result, the arithmetic operations such as addition, subtraction, multiplication, and division for CPU become more complex to perform. For solving the problem of how to do computation on big digit numbers, a number of algorithms have been developed. However, the existing algorithms are noticeably slow because they operate on bits individually and are designed to run over single-core computers only. In this paper, an AI model is presented that performs a computation on tokens of 8-digit numbers to assist boost the CPU computation performance.

Distributed IoT monitoring model using multi-core sensing function integration

Agricultural IoT technology realizes the technology of precise, intelligent, and scientific management of agricultural production. Accurate perception and efficient transmission of farmland data is the basis for precision and smart agriculture. Based on the consideration of WSN distributed monitoring sensor nodes, this paper designs a multi-core sensing agricultural Internet of Things monitoring system based on the low efficiency of existing single-core computing and the inability to adapt to massive sensing data node operations. Multi-core data fusion was simulated and analyzed. Firstly, a method for constructing key value subspaces based on logical landmarks is proposed. The node set maintained by the subspace adds local physical location features to coordinate node discovery and routing. Compared with the traditional key value space, the subspace has a higher system priority, which makes the route local priority, thus realizing traffic localization. The simulation results show that the distributed agricultural network data aggregation algorithm based on multi-core perception can significantly reduce the energy consumption of sensor nodes in WSN, prolong the service life of WSN, and greatly improve the computational efficiency and data accuracy.

Spread Option Pricing on Single-Core and Parallel Computing Architectures

This paper introduces parallel computation for spread options using two-dimensional Fourier transform. Spread options are multi-asset options whose payoffs depend on the difference of two underlying financial securities. Pricing these securities, however, cannot be done using closed-form methods; as such, we propose an algorithm which employs the fast Fourier Transform (FFT) method to numerically solve spread option prices in a reasonable amount of short time while preserving the pricing accuracy. Our results indicate a significant increase in computational performance when the algorithm is performed on multiple CPU cores and GPU. Moreover, the literature on spread option pricing using FFT methods documents that the pricing accuracy increases with FFT grid size while the computational speed has opposite effect. By using the multi-core/GPU implementation, the trade-off between pricing accuracy and speed is taken into account effectively.

Efficient reduced-order modeling of unsteady aerodynamics under light dynamic stall conditions

In this research, a reduced-order modeling is developed to predict the unsteady aerodynamic forces under light dynamic stall conditions at low-speed regimes. The filtered white Gaussian noise is selected as input signals for computational fluid dynamics solver in order to generate training data, containing the information of reduced frequency and amplitude. Because of the time history influences, the reduced-order modeling combines the Kriging function and recurrence framework together in this approach. An airfoil NACA0012 undergoing pitching motions with different reduced frequency, amplitude, and mean angle of attack is designed to illustrate the methodology. The developed model can predict the lift, drag, and moment coefficients in seconds on a single-core computer processor. To reduce the prediction errors between reduced-order modeling predictions and computational fluid dynamics simulations, the aerodynamic loads in static conditions are applied as initial inputs. The predictions via the proposed approach are in agreement with the results using a high precision computational fluid dynamics solver over the designed ranges of amplitude and reduced frequency, which is suitable for engineering applications, such as fluid-structure interaction, and aircraft design optimizations.

Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter

One of the widespread image processing applications is image filtering with two dimensional convolution. Determining the weights of image filters are of importance for the success of filtering operation. Heuristic algorithms such as genetic algorithms provide an efficient way of training these types of filters. Due to the high computational cost of repetitive image filtering operations, this process may take hours to implement using single core computing. OpenMP (Open Multi Processing) provides an efficient library for utilizing the computing power of multicore processors. In this study, OpenMP accelerated training of separable filters that are a subclass of convolution filters has been implemented based on genetic algorithms. Comparative speed-up results for various sizes of images using various sizes of filtering kernels were presented. Also the effect of population size of genetic algorithm and the number of working cores have been investigated.

Parameter Identification of Lithium-Ion Batteries Model to Predict Discharge Behaviors Using Heuristic Algorithm

A complete set of parameters that describes lithium-ion batteries is required in order to better control the sophisticated battery management system used during practical application of lithium-ion batteries. Previous work in the literature requires long times and a computer cluster to identify the full set of parameters while using a pseudo-two-dimensional (P2D) model and a genetic algorithm (GA). This work proposes a more efficient parameter identification method based on the heuristic algorithm. The heuristic algorithm includes features such as the requirement of fewer identified parameters and the effective interval spacing of parameters to save searching time during the GA. This algorithm also uses a divide-and-conquer strategy that divides the full set of P2D parameters into two groups and identifies each group, respectively. With this method, the complete identification of P2D model parameters can be performed within 10 h using a single core computer. Simulated data using the above identified parameters are consistent with the reference results from the literature. And the identified paramters could be used for estimation of the state of charge and the state of available power capability of lithium-ion batteries.

A Parallel Simulating System for Calculating Illumination Distribution of Plant Canopy

Illumination is an important factor to the growth and reproduce of plants. In the process of the plant canopy illumination distribution simulation, it is too complex and time-consuming for single-core computer because of the huge data. This paper has carried on the parallel processing for the Z-buffer sorting algorithm which used for the calculation of the plant canopy illumination distribution in order to speeding up the rate of calculation. The algorithm is transplanted to multiprocessor computers as server and controls the simulation calculation by designing the client-side on the common computers. This design has completed a simulation system based on the C-S mode. It adopts the illumination simulation for the rice in different period using this system and draws the effect picture. At last, the paper has drawn a conclusion that this algorithm is very useful to analyzing plant growth trends and simulating the change of the plant canopy illumination distribution.

Hierarchical Architecture for HEVC Parallel Encoder

The Emerging High Efficiency Video Coding (HEVC) standard offers a significantly better compression rate and higher video quality. HEVC encoders create very high CPU demand, and it is hard for a single core computer to deal with such complex coding computations. To process the high workload of HEVC coding for large-scale video data, the current HEVC draft contains several parallelizing approaches: tile-based parallelization and WPP-level parallelization. In this paper, we adopt additional data parallelism, GOP partitioning, to implement a parallel encoder. We propose the scalable cluster architecture of the HEVC encoder to achieve scalability and a high encoding speed by combining two levels of parallelism, GOP-level parallelism and the HEVC parallel method. The proposed scheme can reduce the large encoding time and significantly improve the coding efficiency. The proposed scalable cluster system is very suitable for high-resolution video such as 4K or 8K containing large amounts of video data.

SAWdoubler: A program for counting self-avoiding walks

This article presents SAWdoubler, a package for counting the total number ZN of self-avoiding walks (SAWs) on a regular lattice by the length-doubling method, of which the basic concept has been published previously by us. We discuss an algorithm for the creation of all SAWs of length N, efficient storage of these SAWs in a tree data structure, and an algorithm for the computation of correction terms to the count Z2N for SAWs of double length, removing all combinations of two intersecting single-length SAWs.We present an efficient numbering of the lattice sites that enables exploitation of symmetry and leads to a smaller tree data structure; this numbering is by increasing Euclidean distance from the origin of the lattice. Furthermore, we show how the computation can be parallelised by distributing the iterations of the main loop of the algorithm over the cores of a multicore architecture. Experimental results on the 3D cubic lattice demonstrate that Z28 can be computed on a dual-core PC in only 1 h and 40 min, with a speedup of 1.56 compared to the single-core computation and with a gain by using symmetry of a factor of 26. We present results for memory use and show how the computation is made to fit in 4 GB RAM. It is easy to extend the SAWdoubler software to other lattices; it is publicly available under the GNU LGPL license. Program summaryProgram title: SAWdoublerCatalogue identifier: AEOB_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOB_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: GNU Lesser General Public LicenceNo. of lines in distributed program, including test data, etc.: 2101No. of bytes in distributed program, including test data, etc.: 19816Distribution format: tar.gzProgramming language: C.Computer: Any computer with a UNIX-like operating system and a C compiler. For large problems, use is made of specific 128-bit integer arithmetic provided by the gcc compiler.Operating system: Any UNIX-like system; developed under Linux and Mac OS 10.Has the code been vectorised or parallelised?: Yes. A parallel version of the code is available in the “Extras” directory of the distribution file.RAM: Problem dependent (2 GB for counting SAWs of length 28 on the 3D cubic lattice)Classification: 16.11.Nature of problem: Computing the number of self-avoiding walks of a given length on a given lattice.Solution method: Length-doubling.Restrictions: The length of the walk must be even. Lattice is 3D simple cubic.Additional comments: The lattice can be replaced by other lattices, such as BCC, FCC, or a 2D square lattice.Running time: Problem dependent (2.5 h using one processor core for length 28 on the 3D cubic lattice).

Fast image analysis in polarization SHG microscopy

Pixel resolution polarization-sensitive second harmonic generation (PSHG) imaging has been recently shown as a promising imaging modality, by largely enhancing the capabilities of conventional intensity-based SHG microscopy. PSHG is able to obtain structural information from the elementary SHG active structures, which play an important role in many biological processes. Although the technique is of major interest, acquiring such information requires long offline processing, even with current computers. In this paper, we present an approach based on Fourier analysis of the anisotropy signature that allows processing the PSHG images in less than a second in standard single core computers. This represents a temporal improvement of several orders of magnitude compared to conventional fitting algorithms. This opens up the possibility for fast PSHG information with the subsequent benefit of potential use in medical applications.

R-Gada: a fast and flexible pipeline for copy number analysis in association studies

BackgroundGenome-wide association studies (GWAS) using Copy Number Variation (CNV) are becoming a central focus of genetic research. CNVs have successfully provided target genome regions for some disease conditions where simple genetic variation (i.e., SNPs) has previously failed to provide a clear association.ResultsHere we present a new R package, that integrates: (i) data import from most common formats of Affymetrix, Illumina and aCGH arrays; (ii) a fast and accurate segmentation algorithm to call CNVs based on Genome Alteration Detection Analysis (GADA); and (iii) functions for displaying and exporting the Copy Number calls, identification of recurrent CNVs, multivariate analysis of population structure, and tools for performing association studies. Using a large dataset containing 270 HapMap individuals (Affymetrix Human SNP Array 6.0 Sample Dataset) we demonstrate a flexible pipeline implemented with the package. It requires less than one minute per sample (3 million probe arrays) on a single core computer, and provides a flexible parallelization for very large datasets. Case-control data were generated from the HapMap dataset to demonstrate a GWAS analysis.ConclusionsThe package provides the tools for creating a complete integrated pipeline from data normalization to statistical association. It can effciently handle a massive volume of data consisting of millions of genetic markers and hundreds or thousands of samples with very accurate results.

Simple and parallel proximity algorithms for general polygonal models

AbstractWe present simple and fast parallel proximity algorithms for rigid polygonal models. Given two polygon‐soup models in space, if they overlap, our algorithm can find all the intersected primitives between them; otherwise, it reports their Euclidean minimum distance. Our algorithm is performed in a parallel fashion and shows scalable performance in terms of the number of available computing cores. The key ingredient of our algorithm is a simple load‐balancing metric based on the penetration depth (PD) (for collision detection) and approximate Euclidean distance (for Euclidean distance computation) between bounding volumes. To compute the PD between oriented bounding boxes (OBBs), we present a novel algorithm based on the well‐known separating axis theorem (SAT) and also shows that the PD can be trivially obtained as a byproduct of SAT. We have implemented these algorithms on a commodity PC with eight cores and benchmarked their performance on complicated geometric models. In practice, the performance of our algorithm shows up to 5 and 9.7 times improvement for collision and distance queries, respectively, compared to single core computation. Copyright © 2010 John Wiley & Sons, Ltd.

Towards high performance computing for molecular structure prediction using IBM Cell Broadband Engine - an implementation perspective

BackgroundRNA structure prediction problem is a computationally complex task, especially with pseudo-knots. The problem is well-studied in existing literature and predominantly uses highly coupled Dynamic Programming (DP) solutions. The problem scale and complexity become embarrassingly humungous to handle as sequence size increases. This makes the case for parallelization. Parallelization can be achieved by way of networked platforms (clusters, grids, etc) as well as using modern day multi-core chips.MethodsIn this paper, we exploit the parallelism capabilities of the IBM Cell Broadband Engine to parallelize an existing Dynamic Programming (DP) algorithm for RNA secondary structure prediction. We design three different implementation strategies that exploit the inherent data, code and/or hybrid parallelism, referred to as C-Par, D-Par and H-Par, and analyze their performances. Our approach attempts to introduce parallelism in critical sections of the algorithm. We ran our experiments on SONY Play Station 3 (PS3), which is based on the IBM Cell chip.ResultsOur results suggest that introducing parallelism in DP algorithm allows it to easily handle longer sequences which otherwise would consume a large amount of time in single core computers. The results further demonstrate the speed-up gain achieved in exploiting the inherent parallelism in the problem and also elicits the advantages of using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA.ConclusionThe speed-up performance reported here is promising, especially when sequence length is long. To the best of our literature survey, the work reported in this paper is probably the first-of-its-kind to utilize the IBM Cell Broadband Engine (a heterogeneous multi-core chip) to implement a DP. The results also encourage using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA to predict its secondary structure.

Linear-scaling atomic orbital-based second-order Møller–Plesset perturbation theory by rigorous integral screening criteria

A Laplace-transformed second-order Moller-Plesset perturbation theory (MP2) method is presented, which allows to achieve linear scaling of the computational effort with molecular size for electronically local structures. Also for systems with a delocalized electronic structure, a cubic or even quadratic scaling behavior is achieved. Numerically significant contributions to the atomic orbital (AO)-MP2 energy are preselected using the so-called multipole-based integral estimates (MBIE) introduced earlier by us [J. Chem. Phys. 123, 184102 (2005)]. Since MBIE provides rigorous upper bounds, numerical accuracy is fully controlled and the exact MP2 result is attained. While the choice of thresholds for a specific accuracy is only weakly dependent upon the molecular system, our AO-MP2 scheme offers the possibility for incremental thresholding: for only little additional computational expense, the numerical accuracy can be systematically converged. We illustrate this dependence upon numerical thresholds for the calculation of intermolecular interaction energies for the S22 test set. The efficiency and accuracy of our AO-MP2 method is demonstrated for linear alkanes, stacked DNA base pairs, and carbon nanotubes: e.g., for DNA systems the crossover toward conventional MP2 schemes occurs between one and two base pairs. In this way, it is for the first time possible to compute wave function-based correlation energies for systems containing more than 1000 atoms with 10 000 basis functions as illustrated for a 16 base pair DNA system on a single-core computer, where no empirical restrictions are introduced and numerical accuracy is fully preserved.