Current Desktop Computers Research Articles

An Inhomogeneous Plane-Wave Based Single-Level Fast Direct Solver for the Scattering Analysis of Extremely Large Antenna Arrays

A semianalytic single-level fast direct solver (FDS) is presented for the method-of-moments (MoM) analysis of large planar antenna arrays with up to few thousands of complex elements on current desktop computers. Assuming a partition of the impedance matrix, the proposed scheme factorizes the near-field blocks via a singular value decomposition (SVD)-based low-rank approximation and the intermediate-field blocks via an analytical inhomogeneous plane-wave (IPW) expansion. The near- and intermediate-field bases are then unified to leverage the Woodbury formula for the inversion. The solutions for the excitation of each array ports are obtained at once with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N^{15/7} \ln ^{3} N)$ </tex-math></inline-formula> computational cost and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N^{11/7} \ln N)$ </tex-math></inline-formula> memory for array problems with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> basis functions. The complexities are traced precisely and the error is controlled by means of analytical formulas for the required number of IPWs. The FDS is also combined with a macrobasis function (MBF) approach to deal with antenna geometries containing many subwavelength details. This article includes simulations of regular arrays of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$169^{2}~3$ </tex-math></inline-formula> -D dipoles and an irregular array composed of seven stations of the low-frequency square kilometer array (SKA) telescope comprising each 256 log-periodic (LP) antennas. The proposed FDS allows the analysis of the coupling effect both within any one station but also between the stations of the telescope, a size and complexity of problem which has not been possible with existing published techniques.

Fast Spectral-Domain Computation of Radiation Integrals Over Tilted Planes Using Unequally-Spaced Fast Laplace Transforms

A plane-wave spectrum method is presented for the fast computation of radiation integrals for source and observation plates, which can be arbitrarily tilted and separated by an intermediate-to-far-field distance. The method expedites the direct and inverse transforms between spatial and spectral domains using unequally spaced fast Laplace transforms. These fast algorithms allow one to combine fast Fourier transforms (FFTs) and contour deformation in the complex plane with controllable accuracy and computation times close to that of a single 2-D FFT. Hence, scattering interactions between plates of sizes up to thousands of wavelengths can now be computed within seconds using current desktop computers. The fast method is validated here for the case of radio propagation in a canyon street modeled with electrically large facets using the physical optics approximation.

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Dynamic programming (DP) is currently the reference optimal energy management approach for hybrid electric vehicles (HEVs). However, several research concerns arise regarding the effective application of DP for optimal HEV control problems which involve a significant number of control variables, state variables and optimization constraints. This paper deals with an optimal control problem for a full parallel P2 HEV with constraints on battery state-of-charge (SOC), battery lifetime in terms of state-of-health (SOH), and smooth driving in terms of the frequencies of internal combustion engine (ICE) activations and gear shifts over time. The DP formulation for the considered HEV control problem is outlined, yet its practical application is demonstrated as unfeasible due to a lack of computational power and memory in current desktop computers. To overcome this drawback, a computationally efficient version of DP is proposed which is named Slope-weighted Rapid Dynamic Programming (SRDP). Computational advantage is achieved by SRDP in considering only the most efficient HEV powertrain operating points rather than the full set of control variable values at each time instant of the drive cycle. A benchmark study simulating various drive cycles demonstrates that the introduced SRDP can achieve compliance with imposed control constraints on battery SOC, battery SOH and smooth driving. At the same time, SRDP can achieve up to 78% computational time saving compared with a baseline DP approach considering the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). On the other hand, the increase in the fuel consumption estimated by SRDP is limited within 3.3% compared with the baseline DP approach if the US06 Supplemental Federal Test Procedure is considered. SRDP could thus be exploited to efficiently explore the large design space associated to HEV powertrains.

Characterization of Weak Protein Domain Structure by Spin-Label Distance Distributions.

Function of intrinsically disordered proteins may depend on deviation of their conformational ensemble from that of a random coil. Such deviation may be hard to characterize and quantify, if it is weak. We explored the potential of distance distributions between spin labels, as they can be measured by electron paramagnetic resonance techniques, for aiding such characterization. On the example of the intrinsically disordered N-terminal domain 1–267 of fused in sarcoma (FUS) we examined what such distance distributions can and cannot reveal on the random-coil reference state. On the example of the glycine-rich domain 188–320 of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) we studied whether deviation from a random-coil ensemble can be robustly detected with 19 distance distribution restraints. We discuss limitations imposed by ill-posedness of the conversion of primary data to distance distributions and propose overlap of distance distributions as a fit criterion that can tackle this problem. For testing consistency and size sufficiency of the restraint set, we propose jack-knife resampling. At current desktop computers, our approach is expected to be viable for domains up to 150 residues and for between 10 and 50 distance distribution restraints.

Improving the Lomb–Scargle Periodogram with the Thomson Multitaper

Abstract A common approach for characterizing the properties of time-series data that are evenly sampled in time is to estimate the power spectrum of the data using the periodogram. The periodogram as an estimator of the spectrum is (1) statistically inconsistent (i.e., its variance does not go to zero as infinite data are collected), (2) biased for finite samples, and (3) suffers from spectral leakage. In astronomy, time-series data are often unevenly sampled in time, and it is popular to use the Lomb–Scargle (LS) periodogram to estimate the spectrum. Unfortunately, from a statistical standpoint, the LS periodogram suffers from the same issues as the classical periodogram and has even worse spectral leakage. Here, we present an improvement on the LS periodogram by combining it with the Thomson multitaper approach. The multitaper spectral estimator is well established in the statistics and engineering literature for evenly sampled time series. It is attractive because it directly trades off bias and variance for frequency resolution, and is fast to compute: compared to an untapered spectral estimator, the multitaper adds no more than a couple of seconds for a time series with a million data points on a current desktop computer. Here, we describe an estimator that combines the multitaper with the LS periodogram. We show examples in which this new approach has improved properties compared to traditional approaches in the case of unevenly sampled time series. Finally, we demonstrate an application of the method to astronomy with an application to Kepler data.

Possibilities and Limitations of Automatic Feature Extraction shown by the Example of Crack Detection in 3D-CT Images of Concrete Specimen

To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too.

Improving co-running program’s performance on CMP

Chip multi-processor (CMP) has become the most common processor in the current cluster and desktop computer, and it is also the current development direction. On CMP, programs usually co-running with each other. However, programs commonly interfere with each other. Some time the interference takes big effect, which cause serious drop down of performance. In order to avoid the serious performance interference, programs should be scheduled reasonably to different socket to improve the program’s performance and system’s utilization. In this paper we propose a new scheduling method to realize a more reasonable scheduling. We do not only consider the LLC miss rate, but also consider the LLC reference of the programs. By the information of LLC reference and LLC miss rate, we schedule programs to different sockets, which realize a reasonable scheduling. The experiment result show that making use of the scheduling method proposed by our paper, program’s performance can improve 4%, because the performance improve is realized by on-chip resource, which is a big contribution.

Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls

Data mining tools may be computationally demanding, which leads to an increasing interest on parallel computing strategies in order to improve their performance. While multi-core processors and Graphics Processing Units (GPUs) accelerators increased the computing power of current desktop computers, we observe that desktop-based data mining tools do not take full advantage of these architectures yet. This paper investigates strategies to improve the performance of Weka, a popular data mining tool, through multi-core and GPU acceleration. Using performance profiling of Weka, we identify operations that could improve the data mining performance when parallelized. We selected two of these operations, and analyze the impact of their parallel execution on Weka’s performance. These experiments demonstrate that while significant speedups can be achieved, all operations are not prone to be parallelized, which reinforces the need for a careful and well-studied selection of the candidates.

Combining structures on different length scales in ray tracing: analysis of optical losses in solar cell modules

Solar cell modules consist of optically relevant geometric structures on very different length scales. While the whole module and the solar cells are on a scale of meters and centimeters, the pyramids etched on mono-crystalline Si cells (for enhancing light-trapping) have sizes in the micrometer range. The simulation domain cannot be reduced substantially to still capture module specific effects. Hence, these large differences in length scale have so far prohibited a detailed ray tracing analysis of entire modules. In this work, we developed a ray tracing approach that separates large and small scale geometries into different simulation domains; the simulated photon automatically switches between the different domains as needed. With this approach, it is possible to simulate whole modules on current desktop computers within reasonable time. We demonstrate the capabilities of this method by analyzing the optical losses in solar cell modules from mass production, as well as in modules under development that have no encapsulant. Our ray tracing method can be applied to any geometric structures containing different length scales.

Performance Improvement of Data Mining in Weka through GPU Acceleration

Data mining tools may be computationally demanding, so there is an increasing interest on parallel computing strategies to improve their performance. The popularization of Graphics Processing Units (GPUs) increased the computing power of current desktop computers, but desktop-based data mining tools do not usually take full advantage of these architectures. This paper exploits an approach to improve the performance of Weka, a popular data mining tool, through parallelization on GPU-accelerated machines. From the profiling of Weka object-oriented code, we chose to parallelize a matrix multiplication method using state-of-the-art tools. The implementation was merged into Weka so that we could analyze the impact of parallel execution on its performance. The results show a significant speedup on the target parallel architectures, compared to the original, sequential Weka code.

Deterministic clustering of the available chemical space

Clustering of compound libraries using 2D binary fingerprints is a fundamental task in chemoinformatics and various methods have been described to solve it [1]. These methods can roughly be grouped into deterministic and non-deterministic approaches with two key-characteristics distinguishing them. First, the algorithmic complexity of deterministic approaches is more demanding whereas the non-deterministic methods often try to overcome this drawback by using heuristics to save time and memory. Second, deterministic clustering algorithms, especially agglomerative hierarchical techniques have been shown to yield good results and often perform better than non-deterministic approaches [2]. As a consequence, clustering of small to medium sized libraries with up to 1 million compounds is regularly performed using deterministic techniques whereas libraries comprising millions of compounds are mostly clustered using heuristics like k-means [3]. Here, we present a deterministic approach for clustering huge compound libraries based on all pairwise compound similarities. For this purpose, we use an extremely fast and flexible algorithm for similarity calculations, which we have developed to be purely CPU-based thus having no need for any specialized hardware. Using this similarity method, we implemented a workflow with the following steps. First, we create a set of unique input fingerprints by filtering duplicates that are then stored and finally remapped onto their representative clusters. Second, we calculate all pairwise similarities to construct a similarity network by applying a fixed Tanimoto threshold to select the edges to be inserted into the network. From this similarity network the connected subgraphs are extracted and forwarded to the last step. Finally, connected subgraphs exceeding a predefined size are hierarchically clustered. As a result, we show that our algorithm for similarity calculation is competitive to recently published CPU-based methods and can perform up to 380 million Tanimoto calculations per second on a current desktop computer. This efficient method allows our workflow to process medium to large libraries on current desktop computers within minutes. To finally demonstrate the power of our clustering workflow, we processed the commercially available chemical space comprising about 17 million compounds [4]. The entire clustering workflow took 63 hours on a compute server using 64 cores and 100 GB main memory to complete.

An Integrated Pipeline for de Novo Assembly of Microbial Genomes

Remarkable advances in DNA sequencing technology have created a need for de novo genome assembly methods tailored to work with the new sequencing data types. Many such methods have been published in recent years, but assembling raw sequence data to obtain a draft genome has remained a complex, multi-step process, involving several stages of sequence data cleaning, error correction, assembly, and quality control. Successful application of these steps usually requires intimate knowledge of a diverse set of algorithms and software. We present an assembly pipeline called A5 (Andrew And Aaron's Awesome Assembly pipeline) that simplifies the entire genome assembly process by automating these stages, by integrating several previously published algorithms with new algorithms for quality control and automated assembly parameter selection. We demonstrate that A5 can produce assemblies of quality comparable to a leading assembly algorithm, SOAPdenovo, without any prior knowledge of the particular genome being assembled and without the extensive parameter tuning required by the other assembly algorithm. In particular, the assemblies produced by A5 exhibit 50% or more reduction in broken protein coding sequences relative to SOAPdenovo assemblies. The A5 pipeline can also assemble Illumina sequence data from libraries constructed by the Nextera (transposon-catalyzed) protocol, which have markedly different characteristics to mechanically sheared libraries. Finally, A5 has modest compute requirements, and can assemble a typical bacterial genome on current desktop or laptop computer hardware in under two hours, depending on depth of coverage.

Microsimulating parcel-level land use and activity-based travel: Development of a prototype application in San Francisco

This paper develops a prototype of an integrated microsimulation model system combining land use at a parcel level with activity-based travel in San Francisco, California. The paper describes the motivation for the model system, its design, data development, and preliminary application and testing. The land use model is implemented using UrbanSim and the Open Platform for Urban Simulation (OPUS), using parcels and buildings rather than zones or grid cells as spatial units of analysis. Measures of accessibility are derived from the San Francisco SF-CHAMP activity-based travel model, and the predicted locations of households and business establishments are used to update the micro-level inputs needed for the activity-based travel model. Data used in the model include business establishments linked to parcels over several years, and a panel of parcels that allow modeling of parcel development over time. This paper describes several advances that have not been previously integrated in an operational model system, including the use of parcels and buildings as units of location for consumers and developers of real estate, the use of business establishments to represent economic activity, and the interfacing of this microsimulation land use model with a microsimulation activity-based travel model. Computational performance and development effort were found to be modest, with land use model run times averaging one minute per year on a current desktop computer, and two to three minutes on a current laptop. By contrast, long run times of the travel model suggest that there may be a need to reconsider the level of complexity in the travel model for an integrated land use and transportation model system application to be broadly usable. The land use model is currently in refinement, being used to identify input data and model specification adjustments needed in order to bring it into operational use, which is planned over the next several months.

Using desktop computers to solve large-scale dense linear algebra problems

We provide experimental evidence that current desktop computers feature enough computational power to solve large-scale dense linear algebra problems. While the high computational cost of the numerical methods for solving these problems can be tackled by the multiple cores of current processors, we propose to use the disk to store the large data structures associated with these applications. Our results also show that the limited amount of RAM and the comparatively slow disk of the system pose no problem for the solution of very large dense linear systems and linear least-squares problems. Thus, current desktop computers are revealed as an appealing, cost-effective platform for research groups that have to deal with large dense linear algebra problems but have no direct access to large computing facilities.

Estimation of flattening coefficient for absorption and circular dichroism using simulation

The absorbance and circular dichroism (CD) of suspensions is lower than if the same amount of chromophore were uniformly distributed throughout the medium. Several mathematical treatments of this absorption flattening phenomenon have been presented using various assumptions and approximations. This article demonstrates an alternative simulation approach that allows relaxation of assumptions. On current desktop computers, the algorithm runs quickly with enough particles and light paths considered to get answers that are usually accurate to better than 3%. Results from the simulation agree with the most popular analytical model for 0.01 volume fraction of particles, showing that the extent of flattening depends mainly on the absorbance through a particle diameter. Unlike previous models, the simulation can show that flattening is significantly lower when volume fraction increases to 0.1 but is higher when the particles have a size distribution. The simulation can predict the slope of the nearly linear relationship between flattening of CD and the absorbance of the suspension. This provides a method to correct experimental CD data where volume fraction and particle size are known.

R/parallel – speeding up bioinformatics analysis with R

BackgroundR is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians.ResultsWe have designed and implemented an R add-on package, R/parallel, that extends R by adding user-friendly parallel computing capabilities. With R/parallel any bioinformatician can now easily automate the parallel execution of loops and benefit from the multicore processor power of today's desktop computers. Using a single and simple function, R/parallel can be integrated directly with other existing R packages. With no need to change the implemented algorithms, the processing time can be approximately reduced N-fold, N being the number of available processor cores.ConclusionR/parallel saves bioinformaticians time in their daily tasks of analyzing experimental data. It achieves this objective on two fronts: first, by reducing development time of parallel programs by avoiding reimplementation of existing methods and second, by reducing processing time by speeding up computations on current desktop computers. Future work is focused on extending the envelope of R/parallel by interconnecting and aggregating the power of several computers, both existing office computers and computing clusters.

A computerized perioperative data integration and display system

Object The operating room is rich in digital data that must be rapidly gathered and integrated by caregivers, potentially distracting them from direct patient care. We hypothesized that current desktop computers could integrate enough electronically accessible perioperative data to present a unified, contextually appropriate snapshot of the patient to the operating room team without requiring any user intervention. Materials and methods We implemented a system that integrates data from surgical and anesthesia devices and information systems, as well as an active radiofrequency identification location tracking system, to create a comprehensive, unified, time-synchronized database of all digital data produced by these systems. Next, a human factors engineering approach was used to identify selected data to show on a large format display during surgery. Results A prototype system has been in daily use in a clinical operating room since August 2005. The system functions automatically without any user input, as the display system self-configures based on cues from the primary data. The system is vendor agnostic with respect to input data sources and display options. Conclusion Automatic integration and display of team-synchronizing data from medical devices and hospital information systems is now possible using software that runs on a personal computer.

PILER-CR: Fast and accurate identification of CRISPR repeats

BackgroundSequencing of prokaryotic genomes has recently revealed the presence of CRISPR elements: short, highly conserved repeats separated by unique sequences of similar length. The distinctive sequence signature of CRISPR repeats can be found using general-purpose repeat- or pattern-finding software tools. However, the output of such tools is not always ideal for studying these repeats, and significant effort is sometimes needed to build additional tools and perform manual analysis of the output.ResultsWe present PILER-CR, a program specifically designed for the identification and analysis of CRISPR repeats. The program executes rapidly, completing a 5 Mb genome in around 5 seconds on a current desktop computer. We validate the algorithm by manual curation and by comparison with published surveys of these repeats, finding that PILER-CR has both high sensitivity and high specificity. We also present a catalogue of putative CRISPR repeats identified in a comprehensive analysis of 346 prokaryotic genomes.ConclusionPILER-CR is a useful tool for rapid identification and classification of CRISPR repeats. The software is donated to the public domain. Source code and a Linux binary are freely available at .

Likelihood-enhanced fast rotation functions.

Experiences with the molecular-replacement program Beast have shown that maximum-likelihood rotation targets are more sensitive to the correct orientation than traditional targets. However, this comes at a high computational cost: brute-force rotation searches can take hours or even days of computation time on current desktop computers. Series approximations to the full likelihood target have been developed that can be computed by fast Fourier transforms in minutes. These likelihood-enhanced targets are more sensitive to the correct orientation than the Crowther fast rotation function and they take advantage of information from partial solutions. The likelihood-enhanced rotation targets have been implemented in the program Phaser.

Heat rejection limits of air cooled plane fin heat sinks for computer cooling

Current desktop computers typically use fan-heat sinks for cooling the CPU, referred to as active heat sinks. This work seeks to determine the heat rejection limits for such fan-heat sinks, within specific fan and heat sink space limits. A fixed volume, 80 /spl times/ 60 /spl times/ 50 mm is chosen as the limiting dimensions, which includes the fan volume. The present work addresses plane fin heat sinks, on which a typical 60 mm fan is mounted. Both duct flow and impinging flow are considered. Analytically based models are used to predict the optimum geometry (minimum convection resistance) for plane fins with duct and impinging flow configurations. Also assessed are the effects of increased fan speed (up to 25%) and heat sink base size (33% increase) on air-cooling limits in duct and impinging flow. Tests on fan-heat sinks are done to validate the predictions. Optimization is also done for an enhanced (offset-strip) fin geometry in duct flow. The plane fin is found to outperform the enhanced geometry.