Multi-core PC Research Articles

Real-Time USB Networking and Device I/O

Multicore PC-class embedded systems present an opportunity to consolidate separate microcontrollers as software-defined functions. For instance, an automotive system with more than 100 electronic control units (ECUs) could be replaced with one or, at most, several multicore PCs running software tasks for chassis, body, powertrain, infotainment, and advanced driver assistance system (ADAS) services. However, a key challenge is how to handle real-time device input and output (I/O) and host-level networking as part of sensor data processing and control. A traditional microcontroller would commonly feature one or more Controller Area Network (CAN) buses for real-time I/O. CAN buses are usually absent in PCs, which instead feature higher bandwidth Universal Serial Bus (USB) interfaces. This article shows how to achieve real-time device I/O and host-to-host communication over USB, using suitably written device drivers and a time-aware POSIX-like “tuned pipe” abstraction. This allows developers to establish task pipelines spanning one or more hosts, with end-to-end latency and throughput guarantees for sensor data processing, control, and actuation.

The Direction of Development Parallel Programming of Software Tools Software Complexes Military Purpose

The article discusses the issues of classification selection of preferred parallelization algorithms (with minimal execution time) implemented in parallel software development tools for multi-core (multiprocessor) computing systems with shared memory, based on the collected training statistical information of the parameters of the execution of cycles, previous program launches. The classification (decisive) rule for selecting the preferred algorithm in the learning process can be built only based on information from the training sample of previous runs, this information should not just be remembered, but should be generalized and transformed into an image most similar to the new recognizable image characterizing an unknown program cycle. To this end, the article presents three methods that allow, based on the mathematical apparatus of probability theory, to sequentially collect and transform training samples for each cyclic section of the program to the form of an image providing a functional relationship between the number of iterations of the current cycle and the preferred parallelization algorithm. The obtained dependence allows automatic selection (in real time) of the preferred parallelization algorithm based on classification selection by the input parameter (number of iterations) of the cycle in the generated project profile consisting of a set of tuples of preferred parallelization algorithms. The purpose of this article is to assess the impact of the execution time of parallelized cyclic sections of the target program, using the proposed method of automated selection of preferred algorithms, with multithreaded parallel execution of the program in multi-core (multiprocessor) PCs on the results of simulation of combat operations

On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization

In this paper, two different acceleration techniques for a deterministic DIRECT (DIviding RECTangles)-type global optimization algorithm, DIRECT-GLce, are considered. We adopt dynamic data structures for better memory usage in MATLAB implementation. We also study shared and distributed parallel implementations of the original DIRECT-GLce algorithm, and a distributed parallel version for the aggressive counterpart. The efficiency of DIRECT-type parallel versions is evaluated solving box- and generally constrained global optimizations problems with varying complexity, including a practical NASA speed reducer design problem. Numerical results show a good efficiency, especially for the distributed parallel version of the original DIRECT-GLce on a multi-core PC.

Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data.

DNA methylation data-based precision cancer diagnostics is emerging as the state of the art for molecular tumor classification. Standards for choosing statistical methods with regard to well-calibrated probability estimates for these typically highly multiclass classification tasks are still lacking. To support this choice, we evaluated well-established machine learning (ML) classifiers including random forests (RFs), elastic net (ELNET), support vector machines (SVMs) and boosted trees in combination with post-processing algorithms and developed ML workflows that allow for unbiased class probability (CP) estimation. Calibrators included ridge-penalized multinomial logistic regression (MR) and Platt scaling by fitting logistic regression (LR) and Firth's penalized LR. We compared these workflows on a recently published brain tumor 450k DNA methylation cohort of 2,801 samples with 91 diagnostic categories using a 5 × 5-fold nested cross-validation scheme and demonstrated their generalizability on external data from The Cancer Genome Atlas. ELNET was the top stand-alone classifier with the best calibration profiles. The best overall two-stage workflow was MR-calibrated SVM with linear kernels closely followed by ridge-calibrated tuned RF. For calibration, MR was the most effective regardless of the primary classifier. The protocols developed as a result of these comparisons provide valuable guidance on choosing ML workflows and their tuning to generate well-calibrated CP estimates for precision diagnostics using DNA methylation data. Computation times vary depending on the ML algorithm from <15 min to 5 d using multi-core desktop PCs. Detailed scripts in the open-source R language are freely available on GitHub, targeting users with intermediate experience in bioinformatics and statistics and using R with Bioconductor extensions.

FlexCWM: A Flexible Framework for Cluster-Weighted Models

Cluster-weighted models (CWMs) are mixtures of regression models with random covariates. However, besides having recently become rather popular in statistics and data mining, there is still a lack of support for CWMs within the most popular statistical suites. In this paper, we introduce flexCWM, an R package specifically conceived for fitting CWMs. The package supports modeling the conditioned response variable by means of the most common distributions of the exponential family and by the t distribution. Covariates are allowed to be of mixed-type and parsimonious modeling of multivariate normal covariates, based on the eigenvalue decomposition of the component covariance matrices, is supported. Furthermore, either the response or the covariates distributions can be omitted, yielding to mixtures of distributions and mixtures of regression models with fixed covariates, respectively. The expectation-maximization (EM) algorithm is used to obtain maximum-likelihood estimates of the parameters and likelihood-based information criteria are adopted to select the number of groups and/or a parsimonious model. For the component regression coefficients, standard errors and significance tests are also provided. Parallel computation can be used on multicore PCs and computer clusters, when several models have to be fitted. To exemplify the use of the package, applications to artificial and real datasets, included in the package, are presented.

ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions

We introduce the R package ContaminatedMixt, conceived to disseminate the use of mixtures of multivariate contaminated normal distributions as a tool for robust clustering and classification under the common assumption of elliptically contoured groups. Thirteen variants of the model are also implemented to introduce parsimony. The expectationconditional maximization algorithm is adopted to obtain maximum likelihood parameter estimates, and likelihood-based model selection criteria are used to select the model and the number of groups. Parallel computation can be used on multicore PCs and computer clusters, when several models have to be fitted. Differently from the more popular mixtures of multivariate normal and t distributions, this approach also allows for automatic detection of mild outliers via the maximum a posteriori probabilities procedure. To exemplify the use of the package, applications to artificial and real data are presented.

Interactive sound propagation and rendering for large multi-source scenes

We present an approach to generate plausible acoustic effects at interactive rates in large dynamic environments containing many sound sources. Our formulation combines listener-based backward ray tracing with sound source clustering and hybrid audio rendering to handle complex scenes. We present a new algorithm for dynamic late reverberation that performs high-order ray tracing from the listener against spherical sound sources. We achieve sublinear scaling with the number of sources by clustering distant sound sources and taking relative visibility into account. We also describe a hybrid convolution-based audio rendering technique that can process hundreds of thousands of sound paths at interactive rates. We demonstrate the performance on many indoor and outdoor scenes with up to 200 sound sources. In practice, our algorithm can compute more than 50 reflection orders at interactive rates on a multicore PC, and we observe a 5x speedup over prior geometric sound propagation algorithms.

Interactive Sound Propagation and Rendering for Large Multi-Source Scenes

We present an approach to generate plausible acoustic effects at interactive rates in large dynamic environments containing many sound sources. Our formulation combines listener-based backward ray tracing with sound source clustering and hybrid audio rendering to handle complex scenes. We present a new algorithm for dynamic late reverberation that performs high-order ray tracing from the listener against spherical sound sources. We achieve sublinear scaling with the number of sources by clustering distant sound sources and taking relative visibility into account. We also describe a hybrid convolution-based audio rendering technique that can process hundreds of thousands of sound paths at interactive rates. We demonstrate the performance on many indoor and outdoor scenes with up to 200 sound sources. In practice, our algorithm can compute more than 50 reflection orders at interactive rates on a multicore PC, and we observe a 5x speedup over prior geometric sound propagation algorithms.

THE PARALLEL IMPLEMENTATION OF ALGORITHMS FOR FINDING THE REFLECTION SYMMETRY OF THE BINARY IMAGES

Abstract. In this paper, we investigate the exact method of searching an axis of binary image symmetry, based on brute-force search among all potential symmetry axes. As a measure of symmetry, we use the set-theoretic Jaccard similarity applied to two subsets of pixels of the image which is divided by some axis. Brute-force search algorithm definitely finds the axis of approximate symmetry which could be considered as ground-truth, but it requires quite a lot of time to process each image. As a first step of our contribution we develop the parallel version of the brute-force algorithm. It allows us to process large image databases and obtain the desired axis of approximate symmetry for each shape in database. Experimental studies implemented on “Butterflies” and “Flavia” datasets have shown that the proposed algorithm takes several minutes per image to find a symmetry axis. However, in case of real-world applications we need computational efficiency which allows solving the task of symmetry axis search in real or quasi-real time. So, for the task of fast shape symmetry calculation on the common multicore PC we elaborated another parallel program, which based on the procedure suggested before in (Fedotova, 2016). That method takes as an initial axis the axis obtained by superfast comparison of two skeleton primitive sub-chains. This process takes about 0.5 sec on the common PC, it is considerably faster than any of the optimized brute-force methods including ones implemented in supercomputer. In our experiments for 70 percent of cases the found axis coincides with the ground-truth one absolutely, and for the rest of cases it is very close to the ground-truth.

Parallel SystemC Simulation for ESL Design

Virtual platforms have become essential tools for the design of embedded systems. Developers rely on them for design space exploration and software debugging. However, with rising HW/SW complexity and the need to simulate more and more processors simultaneously, the performance of virtual platforms degrades rapidly. Parallel simulation techniques can help to counter this by leveraging multicore PCs, which are widely available today. This work presents a novel parallel simulation approach that is targeted toward acceleration of virtual platforms from the ESL domain. By trading some timing accuracy, multiprocessor virtual platforms can be accelerated by up to 3.4× on regular quad-core workstations.

A Low-cost Multicomputer for solving the RCPSP

&lt;p&gt;In the paper it is shown that time necessary to solve the NP-hard Resource-Constrained Project Scheduling Problem (RCPSP) could be considerably reduced using a low-cost multicomputer. We consider an extension of the problem when resources are only partially available and a deadline is given but the cost of the project should be minimized. In such a case nding an acceptable solution (optimal or even semi-optimal) is computationally very hard. To reduce this complexity a distributed processing model of a metaheuristic algorithm, previously adapted by us for working with human resources and the CCPM method, was developed. Then, a new implementation of the model on a low-cost multicomputer built from PCs connected through a local network was designed and compared with regular implementation of the model on a cluster. Furthermore, to examine communication costs, an implementation of the model on a single multi-core PC was tested, too.&lt;br /&gt;The comparative studies proved that the implementation is as ecient as on more expensive cluster. Moreover, it has balanced load and scales well.&lt;/p&gt;

Interactive Sound Propagation and Rendering for Large Multi-Source Scenes

We present an approach to generate plausible acoustic effects at interactive rates in large dynamic environments containing many sound sources. Our formulation combines listener-based backward ray tracing with sound source clustering and hybrid audio rendering to handle complex scenes. We present a new algorithm for dynamic late reverberation that performs high-order ray tracing from the listener against spherical sound sources. We achieve sublinear scaling with the number of sources by clustering distant sound sources and taking relative visibility into account. We also describe a hybrid convolution-based audio rendering technique that can process hundreds of thousands of sound paths at interactive rates. We demonstrate the performance on many indoor and outdoor scenes with up to 200 sound sources. In practice, our algorithm can compute more than 50 reflection orders at interactive rates on a multicore PC, and we observe a 5x speedup over prior geometric sound propagation algorithms.

A Fast PC Algorithm for High Dimensional Causal Discovery with Multi-Core PCs.

Discovering causal relationships from observational data is a crucial problem and it has applications in many research areas. The PC algorithm is the state-of-the-art constraint based method for causal discovery. However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e.g., gene expression datasets. On another note, the advancement of computer hardware in the last decade has resulted in the widespread availability of multi-core personal computers. There is a significant motivation for designing a parallelized PC algorithm that is suitable for personal computers and does not require end users' parallel computing knowledge beyond their competency in using the PC algorithm. In this paper, we develop parallel-PC, a fast and memory efficient PC algorithm using the parallel computing technique. We apply our method to a range of synthetic and real-world high dimensional datasets. Experimental results on a dataset from the DREAM 5 challenge show that the original PC algorithm could not produce any results after running more than 24 hours; meanwhile, our parallel-PC algorithm managed to finish within around 12 hours with a 4-core CPU computer, and less than six hours with a 8-core CPU computer. Furthermore, we integrate parallel-PC into a causal inference method for inferring miRNA-mRNA regulatory relationships. The experimental results show that parallel-PC helps improve both the efficiency and accuracy of the causal inference algorithm.

OpenMP and CUDA simulations of Sella Zerbino Dam break on unstructured grids

This paper presents two 2D dam break parallelized models based on shallow water equations (SWE) written in conservative form. The models were implemented exploiting multicore PC systems and graphics processor unit (GPU) architectures under the OpenMP and the NVIDIA™’s compute unified device architecture (CUDA) frameworks. The mathematical model is solved using a finite-volume technique on an unstructured grid, with Roe’s approximate Riemann solver, a first-order upwind scheme. The upwind treatment of the source terms is implemented. A technique to cope with a wetting-drying advance front is adopted, together with the inclusion of the influence of source terms in the stability constraint in order to prevent negative water depths at the dry fronts. The proposed model is first applied to a laboratory test and then to a real dam break that occurred in Italy in 1935. Results on different grid sizes are compared to show the computing efficiency between the original sequential model and the parallelized models.

A Novel Method for Multiplexed Nanometric Bead Tracking

Single-molecule force spectroscopy is a powerful tool to resolve dynamics and interactions between large bio-molecules. Magnetic Tweezers, for example, probe the mechanics of a biopolymer by tethering it to a micrometer sized bead and tracking the bead using video microscopy and image processing. This technique strongly relies on accurate bead tracking in three dimensions with nanometer resolution. However, it traditionally suffers from extremely low throughput. When parallelizing bead-tracking experiments, the need arises for fast algorithms without compromising accuracy and robustness. Here, we introduce a novel tracking method based on a 3D cross correlation of the bead image with a set of computer-generated reference images. We show that the phase shift of the cross correlation peak is proportional to the bead height. For testing the accuracy, we used Lorentz-Mie scattering theory to generate test data. We found that non-circular effects such as interlacing, small image artifacts and non-uniform illumination do not affect the tracking result. Our algorithm tracks over 100 beads in a 100x100 pixel region-of-interest on a multicore PC in real time. Its speed, accuracy and robustness may improve other techniques that rely on bead tracking such as optical tweezers and acoustic force spectroscopy.

Research on Multi-Core PC Parallel Computation Based on OpenMP

Along with quad-core PC coming into the market and invention of 80-core processor in the laboratory, Software development will go through a foundational innovation due to multicore technology. The technique of parallel programming on multi-core computers is explored in this thesis. First, the OpenMP standard which is an application programming interface (API) on parallel programming model of shared-memory is introduced and an overview of a set of compiler directives and a library of support functions are given. The OpenMP programs requires an OpenMP-compatible compiler and thread-safe libraries，therefore, both Intel C++ compiler 9.1 and Microsoft Visual Studio 2005 are perfect choices. Then, two-dimensional discrete fast Fourier transform (FFT) is studied by focusing on parallel program design，realization and optimization technology. Finally，the thesis predicts that high performance parallel computing software component library must be a perfect exploitation field in the further future.

Improving the efficiency of generalized 3D Hough transform

In this paper we continue the study for improving the efficiency of the generalized Hough transform to search for multiple spherical surfaces for a given three-dimensional array of points in space. Proposed parallelization of the algorithm to speed up its work on multicore PCs. Greatest efficiency this campaign shows together with a cascading Hough transform. Figs.: 1. Tabl.: 1. Refs.: 10 titles.Keywords: 3D Hough transform; cascaded Hough transform; parallelization of the algorithm.

Distributed Circuit Simulation by Using Backward-Traversing Waveform Relaxation

This paper describes methods to utilize the multi-core PC as well as computers connected by internet to perform MOSFET circuit simulations. The Combining Simulation Method (CSM) is the used parallel algorithm. This paper also presents an automatic method to partition the simulation jobs in CSM. All proposed methods have been implemented and tested. Experimental results justify pleasing effects of proposed methods.

GPUs for real-time processing in HEP trigger systems

We describe a pilot project for the use of Graphics Processing Units (GPUs) for online triggering applications in High Energy Physics (HEP) experiments. Two major trends can be identified in the development of trigger and DAQ systems for HEP experiments: the massive use of general-purpose commodity systems such as commercial multicore PC farms for data acquisition, and the reduction of trigger levels implemented in hardware, towards a pure software selection system (trigger-less). The very innovative approach presented here aims at exploiting the parallel computing power of commercial GPUs to perform fast computations in software both at low- and high-level trigger stages. General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerator in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughputs, the use of such devices for real-time applications in high-energy physics data acquisition and trigger systems is becoming very attractive. We discuss in details the use of online parallel computing on GPUs for synchronous low-level trigger with fixed latency. In particular we show preliminary results on a first test in the NA62 experiment at CERN. The use of GPUs in high-level triggers is also considered, the ATLAS experiment (and in particular the muon trigger) at CERN will be taken as a study case of possible applications.

GPUs for real-time processing in HEP trigger systems

We describe a pilot project (GAP – GPU Application Project) for the use of GPUs (Graphics processing units) for online triggering applications in High Energy Physics experiments. Two major trends can be identified in the development of trigger and DAQ systems for particle physics experiments: the massive use of general-purpose commodity systems such as commercial multicore PC farms for data acquisition, and the reduction of trigger levels implemented in hardware, towards a fully software data selection system ("trigger-less"). The innovative approach presented here aims at exploiting the parallel computing power of commercial GPUs to perform fast computations in software not only in high level trigger levels but also in early trigger stages. General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerators in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughputs, the use of such devices for real-time applications in high energy physics data acquisition and trigger systems is becoming relevant. We discuss in detail the use of online parallel computing on GPUs for synchronous low-level triggers with fixed latency. In particular we show preliminary results on a first test in the CERN NA62 experiment. The use of GPUs in high level triggers is also considered, the CERN ATLAS experiment being taken as a case study of possible applications.