Berkeley Open Infrastructure For Network Computing Research Articles

Crystal search - feasibility study of a real-time deep learning process for crystallization well images.

To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images.

Design of a Flexible, User Friendly Feature Matrix Generation System and its Application on Biomedical Datasets

The generation of a feature matrix is the first step in conducting machine learning analyses on complex data sets such as those containing DNA, RNA or protein sequences. These matrices contain information for each object which have to be identified using complex algorithms to interrogate the data. They are normally generated by combining the results of running such algorithms across various datasets from different and distributed data sources. Thus for non-computing experts the generation of such matrices prove a barrier to employing machine learning techniques. Further since datasets are becoming larger this barrier is augmented by the limitations of the single personal computer most often used by investigators to carry out such analyses. Here we propose a user friendly system to generate feature matrices in a way that is flexible, scalable and extendable. Additionally by making use of The Berkeley Open Infrastructure for Network Computing (BOINC) software, the process can be speeded up using distributed volunteer computing possible in most institutions. The system makes use of a combination of the Grid and Cloud User Support Environment (gUSE), combined with the Web Services Parallel Grid Runtime and Developer Environment Portal (WS-PGRADE) to create workflow-based science gateways that allow users to submit work to the distributed computing. This report demonstrates the use of our proposed WS-PGRADE/gUSE BOINC system to identify features to populate matrices from very large DNA sequence data repositories, however we propose that this system could be used to analyse a wide variety of feature sets including image, numerical and text data.

Enabling BOINC in infrastructure as a service cloud system

Abstract. Volunteer or crowd computing is becoming increasingly popular for solving complex research problems from an increasingly diverse range of areas. The majority of these have been built using the Berkeley Open Infrastructure for Network Computing (BOINC) platform, which provides a range of different services to manage all computation aspects of a project. The BOINC system is ideal in those cases where not only does the research community involved need low-cost access to massive computing resources but also where there is a significant public interest in the research being done.We discuss the way in which cloud services can help BOINC-based projects to deliver results in a fast, on demand manner. This is difficult to achieve using volunteers, and at the same time, using scalable cloud resources for short on demand projects can optimize the use of the available resources. We show how this design can be used as an efficient distributed computing platform within the cloud, and outline new approaches that could open up new possibilities in this field, using Climateprediction.net (http://www.climateprediction.net/) as a case study.

A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model.

Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.

Asteroid models from the Lowell photometric database

We use the lightcurve inversion method to derive new shape models and spin states of asteroids from the sparse-in-time photometry compiled in the Lowell Photometric Database. To speed up the time-consuming process of scanning the period parameter space through the use of convex shape models, we use the distributed computing project Asteroids@home, running on the Berkeley Open Infrastructure for Network Computing (BOINC) platform. This way, the period-search interval is divided into hundreds of smaller intervals. These intervals are scanned separately by different volunteers and then joined together. We also use an alternative, faster, approach when searching the best-fit period by using a model of triaxial ellipsoid. By this, we can independently confirm periods found with convex models and also find rotation periods for some of those asteroids for which the convex-model approach gives too many solutions. From the analysis of Lowell photometric data of the first 100,000 numbered asteroids, we derived 328 new models. This almost doubles the number of available models. We tested the reliability of our results by comparing models that were derived from purely Lowell data with those based on dense lightcurves, and we found that the rate of false-positive solutions is very low. We also present updated plots of the distribution of spin obliquities and pole ecliptic longitudes that confirm previous findings about a non-uniform distribution of spin axes. However, the models reconstructed from noisy sparse data are heavily biased towards more elongated bodies with high lightcurve amplitudes.

Asteroids@home—A BOINC distributed computing project for asteroid shape reconstruction

We present the project Asteroids@home that uses distributed computing to solve the time-consuming inverse problem of shape reconstruction of asteroids. The project uses the Berkeley Open Infrastructure for Network Computing (BOINC) framework to distribute, collect, and validate small computational units that are solved independently at individual computers of volunteers connected to the project. Shapes, rotational periods, and orientations of the spin axes of asteroids are reconstructed from their disk-integrated photometry by the lightcurve inversion method.

Searching the Human Genome for Snail and Slug With DNA@Home.

DNA@Home is a volunteer computing project that aims to use Gibbs Sampling for the identification and location of DNA control signals on full genome-scale datasets. A fault tolerant and asynchronous implementation of Gibbs sampling using the Berkeley Open Infrastructure for Network Computing (BOINC) was used to identify the location of binding sites of the SNAI1 (Snail) and SNAI2 (Slug) transcription factors across the human genome. Genes regulated by Slug but not Snail, and genes regulated by Snail but not Slug provided two datasets with known motifs. These datasets contained up to 994 DNA sequences which to our knowledge is largest scale use of Gibbs sampling for discovery of binding sites. 1000 parallel sampling walks were used to search for the presence of 1, 2 or 3 possible motifs using small, medium, and full size sets of these sequences. These runs were performed over a period of two months using over 1500 volunteered computing hosts and generated over 2.2 Terabytes of sampling data. High performance computing resources were used for post processing. This paper presents intra and inter walk analyses used to determine walk convergence. The results were validated against current biological knowledge of the Snail and Slug promoter regions and present avenues for further biological study.

Large-scale asynchronous and distributed multidimensional replica exchange molecular simulations and efficiency analysis.

We describe methods to perform replica exchange molecular dynamics (REMD) simulations asynchronously (ASyncRE). The methods are designed to facilitate large scale REMD simulations on grid computing networks consisting of heterogeneous and distributed computing environments as well as on homogeneous high-performance clusters. We have implemented these methods on NSF (National Science Foundation) XSEDE (Extreme Science and Engineering Discovery Environment) clusters and BOINC (Berkeley Open Infrastructure for Network Computing) distributed computing networks at Temple University and Brooklyn College at CUNY (the City University of New York). They are also being implemented on the IBM World Community Grid. To illustrate the methods, we have performed extensive (more than 60 ms in aggregate) simulations for the beta-cyclodextrin-heptanoate host-guest system in the context of one- and two-dimensional ASyncRE, and we used the results to estimate absolute binding free energies using the binding energy distribution analysis method. We propose ways to improve the efficiency of REMD simulations: these include increasing the number of exchanges attempted after a specified molecular dynamics (MD) period up to the fast exchange limit and/or adjusting the MD period to allow sufficient internal relaxation within each thermodynamic state. Although ASyncRE simulations generally require long MD periods (>picoseconds) per replica exchange cycle to minimize the overhead imposed by heterogeneous computing networks, we found that it is possible to reach an efficiency similar to conventional synchronous REMD, by optimizing the combination of the MD period and the number of exchanges attempted per cycle.

Определение добровольных вычислений: формальный подход

Volunteer computing resembles private desktop grids whereas desktop grids are not fully equivalent to volunteer computing. There are several attempts to distinguish and categorize them using informal and formal methods. However, most formal approaches model a particular middleware and do not focus on the general notion of volunteer or desktop grid computing. This work makes an attempt to formalize their characteristics and relationship. To this end formal modeling is applied that tries to grasp the semantic of their functionalities — as opposed to comparisons based on properties, features, etc. We apply this modeling method to formalize the Berkeley Open Infrastructure for Network Computing (BOINC) [Anderson D. P., 2004] volunteer computing system.

Firing patterns in a random network cellular automata model of the brain

One of the main challenges in the simulation of even reduced areas of the brain is the presence of a large number of neurons and a large number of connections among them. Even from a theoretical point of view, the behaviour of dynamical models of complex networks with high connectivity is unknown, precisely because the cost of computation is still unaffordable and it will likely be in the near future. In this paper we discuss the simulation of a cellular automata network model of the brain including up to one million sites with a maximum average of three hundred connections per neuron. This level of connectivity was achieved thanks to a distributed computing environment based on the BOINC (Berkeley Open Infrastructure for Network Computing) platform. Moreover, in this work we consider the interplay among excitatory neurons (which induce the excitation of their neighbours) and inhibitory neurons (which prevent resting neurons from firing and induce firing neurons to pass to the refractory state). Our objective is to classify the normal (noisy but asymptotically constant patterns) and the abnormal (high oscillations with spindle-like behaviour) patterns of activity in the model brain and their stability and parameter ranges in order to determine the role of excitatory and inhibitory compensatory effects in healthy and diseased individuals.

OmniClassifier: a Desktop Grid Computing System for Big Data Prediction Modeling.

Robust prediction models are important for numerous science, engineering, and biomedical applications. However, best-practice procedures for optimizing prediction models can be computationally complex, especially when choosing models from among hundreds or thousands of parameter choices. Computational complexity has further increased with the growth of data in these fields, concurrent with the era of "Big Data". Grid computing is a potential solution to the computational challenges of Big Data. Desktop grid computing, which uses idle CPU cycles of commodity desktop machines, coupled with commercial cloud computing resources can enable research labs to gain easier and more cost effective access to vast computing resources. We have developed omniClassifier, a multi-purpose prediction modeling application that provides researchers with a tool for conducting machine learning research within the guidelines of recommended best-practices. omniClassifier is implemented as a desktop grid computing system using the Berkeley Open Infrastructure for Network Computing (BOINC) middleware. In addition to describing implementation details, we use various gene expression datasets to demonstrate the potential scalability of omniClassifier for efficient and robust Big Data prediction modeling. A prototype of omniClassifier can be accessed at http://omniclassifier.bme.gatech.edu/.

Efficient Implementation of Branch-and-bound Method on Desktop Grids

The Berkeley Open Infrastructure for Network Computing (BOINC) is an opensource middleware system for volunteer and desktop grid computing. In this paper we propose BNBTEST, a BOINC version of distributed branch and bound method. The crucial issues of distributed branch-and-bound method are traversing the search tree and loading balance. We developed subtaskspackaging method and three dierent subtasks' distribution strategies to solve these.

A BOINC based, citizen-science project for pixel spectral energy distribution fitting of resolved galaxies in multi-wavelength surveys

Abstract In this work we present our experience from the first year of theSkyNet Pan-STARRS1 Optical Galaxy Survey (POGS) project. This citizen-scientist driven research project uses the Berkeley Open Infrastructure for Network Computing (BOINC) middleware and thousands of Internet-connected computers to measure the resolved galactic structural properties of ∼ 100,000 low redshift galaxies. We are combining the spectral coverage of GALEX, Pan-STARRS1, SDSS, and WISE to generate a value-added, multi-wavelength UV–optical–NIR galaxy atlas for the nearby Universe. Specifically, we are measuring physical parameters (such as local stellar mass, star formation rate, and first-order star formation history) on a resolved pixel-by-pixel basis using spectral energy distribution (SED) fitting techniques in a distributed computing mode.

On the powerful use of simulations in the Quake-Catcher Network to efficiently position low-cost earthquake sensors

The Quake-Catcher Network (QCN) represents a paradigm shift in seismic networks by involving the general public in the collection, detection, and recognition of seismic events. The QCN uses low-cost sensors connected to volunteer computers across the world to monitor seismic events. The location and density of these sensors can impact the accuracy of event detection. Testing different arrangements of new sensors could disrupt the currently active project; thus such an experiment is best accomplished in a simulated environment.This paper presents an accurate and efficient framework for simulating low-cost QCN sensors and identifying their most effective locations and densities. To build the framework, we extend EmBOINC, an emulator of Berkeley Open Infrastructure for Network Computing (BOINC) projects, to handle the trickle messages generated by sensors connected to volunteer hosts and sent to the QCN server when strong ground motion is detected. EmBOINC allows us to rigorously study QCN simulations at 100,000 or even 1,000,000 sensors, highlight strengths and weaknesses of different sensor density and placement, and test the network with various parameters, conditions, and earthquake scenarios. Results obtained with EmBOINC and presented in this paper show how our simulations can reliably study diverse sensor densities and seismic scenarios under different geographical and infrastructural constraints.

BOINC service for volunteer cloud computing

Since a couple of years, a team at CERN and partners from the Citizen Cyberscience Centre (CCC) have been working on a project that enables general physics simulation programs to run in a virtual machine on volunteer PCs around the world. The project uses the Berkeley Open Infrastructure for Network Computing (BOINC) framework. Based on CERNVM and the job management framework Co-Pilot, this project was made available for public beta-testing in August 2011 with Monte Carlo simulations of LHC physics under the name “LHC@home 2.0” and the BOINC project: “Test4Theory”. At the same time, CERN's efforts on Volunteer Computing for LHC machine studies have been intensified; this project has previously been known as LHC@home, and has been running the “Sixtrack” beam dynamics application for the LHC accelerator, using a classic BOINC framework without virtual machines. CERN-IT has set up a BOINC server cluster, and has provided and supported the BOINC infrastructure for both projects. CERN intends to evolve the setup into a generic BOINC application service that will allow scientists and engineers at CERN to profit from volunteer computing. This paper describes the experience with the two different approaches to volunteer computing as well as the status and outlook of a general BOINC service.

Heuristic algorithms for optimization of task allocation and result distribution in peer-to-peer computing systems

Abstract Recently, distributed computing system have been gaining much attention due to a growing demand for various kinds of effective computations in both industry and academia. In this paper, we focus on Peer-to-Peer (P2P) computing systems, also called public-resource computing systems or global computing systems. P2P computing systems, contrary to grids, use personal computers and other relatively simple electronic equipment (e.g., the PlayStation console) to process sophisticated computational projects. A significant example of the P2P computing idea is the BOINC (Berkeley Open Infrastructure for Network Computing) project. To improve the performance of the computing system, we propose to use the P2P approach to distribute results of computational projects, i.e., results are transmitted in the system like in P2P file sharing systems (e.g., BitTorrent). In this work, we concentrate on offline optimization of the P2P computing system including two elements: scheduling of computations and data distribution. The objective is to minimize the system OPEX cost related to data processing and data transmission. We formulate an Integer Linear Problem (ILP) to model the system and apply this formulation to obtain optimal results using the CPLEX solver. Next, we propose two heuristic algorithms that provide results very

Improving volunteer computing scheduling for evolutionary algorithms

Evolutionary Algorithms (EA) have been extensively used in research to resolve optimization problems involving computationally intensive objective function evaluations. It is even more interesting to use a low-cost distributed computing platform based on Volunteer Computing (VC), to perform such optimizations. The downside is that VC compute nodes’ volatility and unreliability associated with the level of task dependency introduced by parallel EA’s tend to delay the algorithm’s progress.This work proposes an enhanced scheduling of the BOINC (Berkeley Open Infrastructure for Network Computing) tasks associated with a Genetic Algorithm (GA) that aims at improving the performance of the algorithm. BOINC is the most popular middleware used for VC. While the GA has been chosen as it is the most commonly used EA, this approach is applicable to most of iterative EA’s. The scheduling performs a matchmaking between a pool of tasks, classified according to their potential (predicted) fitness, and the pool of available hosts, classified according to their reliability. The scheduling technique have been implemented in a simulation environment and tested with benchmark functions. It proved to be effective in increasing the convergence speed and reducing the execution time of the GA.

Towards a volunteer cloud system

The paper completes the work started in the EU FP7 EDGI project for extending service grids with volunteer (global) and institutional (local) desktop grids. The Generic BOINC Application Client (GBAC) concept described in the paper enables the transparent and automatic forwarding of parameter sweep application (parametric) jobs from service grid VOs (Virtual Organizations) into connected desktop grids without any porting effort. GBAC that introduces virtualization for the volunteer BOINC (Berkeley Open Infrastructure for Network Computing) clients can also be considered as a first step towards establishing volunteer cloud systems since it provides solutions for several problems of creating such a volunteer cloud system.

Arabic Cursive Characters Distributed Recognition using the DTW Algorithm on BOINC: Performance Analysis

Volunteer computing or volunteer grid computing constitute a very promising infrastructure which provides enough computing and storage powers without any prior cost or investment. Indeed, such infrastructures are the result of the federation of several, geographically dispersed, computers or/and LAN computers over the Internet. Berkeley Open Infrastructure for Network Computing (BOINC) is considered the most well- known volunteer computing infrastructure. In this paper, we are interested, rather, by the distribution of the Arabic OCR (Optical Character Recognition) based on the DTW (Dynamic Time Warping) algorithm on the BOINC, in order, to prove again that volunteer computing provides very interesting and promising infrastructures to speed up, at will, several greedy algorithms or applications, especially, the Arabic OCR based on the DTW algorithm. What makes very attractive the Arabic OCR based on the DTW algorithm is the following, first, its ability to recognize, properly, words or sub words, without any prior segmentation, from within a reference library of isolated characters. Second, its good immunity against a wide range of noises. Obtained first results confirm, indeed, that the Berkeley Open Infrastructure for Network Computing constitutes an interesting and promising framework to speed up the Arabic OCR based on the DTW algorithm.

Distributed computing as a virtual supercomputer: Tools to run and manage large-scale BOINC simulations

Distributed computing (DC) projects tackle large computational problems by exploiting the donated processing power of thousands of volunteered computers, connected through the Internet. To efficiently employ the computational resources of one of world's largest DC efforts, GPUGRID, the project scientists require tools that handle hundreds of thousands of tasks which run asynchronously and generate gigabytes of data every day. We describe RBoinc, an interface that allows computational scientists to embed the DC methodology into the daily work-flow of high-throughput experiments. By extending the Berkeley Open Infrastructure for Network Computing (BOINC), the leading open-source middleware for current DC projects, with mechanisms to submit and manage large-scale distributed computations from individual workstations, RBoinc turns distributed grids into cost-effective virtual resources that can be employed by researchers in work-flows similar to conventional supercomputers. The GPUGRID project is currently using RBoinc for all of its in silico experiments based on molecular dynamics methods, including the determination of binding free energies and free energy profiles in all-atom models of biomolecules.