Hardware-Assisted Characterization of NAS Benchmarks

The UAH Logging, Trace Recording, and Analysis instrumentation (ULTRA) provides highly repeatable (0.0002% variation) application instruction counts for parallel programs which are invariant to the communication network used, the number of processors used, and the MPI communication library used. ULTRA, implemented as an MPI profiling wrapper, avoids the data collection system artifacts of time-based measurements by using instruction counts as the basic measure of work performed and records the operation performed and the amount of data sent for each network operation. These measurements can be scaled appropriately for various target architectures. ULTRA's instrumentation overhead is minimized by using the Pentium II processors's performance monitoring hardware, allowing large, production-run applications to be quickly characterized. Traces of the NAS benchmarks representing 6.67×10¹² application instructions were generated by ULTRA. The application instructions executed per byte injected into the network and the instructions executed per message sent were computed from the traces. These values can be scaled by the expected processor performance to estimate the minimum network performance required to support the programs. It is impossible to use time-based measurements for this purpose due to measurement artifacts caused by the background processes and the communication network of the data collection system.     

A Monitoring Sensor Management System for Grid Environments

Large distributed systems such as Computational Grids require a large amount of monitoring data be collected for a variety of tasks such as fault detection, performance analysis, performance tuning, performance prediction, and scheduling. Ensuring that all necessary monitoring is turned on and that data is being collected can be a very tedious and error-prone task. We have developed an agent-based system to automate the execution of monitoring sensors and the collection of event data.     

The Rutgers Computational Grid: A Distributed Linux PC Cluster

The Rutgers Computational Grid (RCG) project is aimed at providing high throughput performance to Rutgers university faculty and students. The RCG employs dual processor PCs, with Pentium II and III processors, as computational nodes, running the Linux RedHat operating system. The Load Sharing Facility (LSF) scheduling system from Platform Computing is used for job control and monitoring. The nodes are grouped into subclusters physically located in several departments and controlled by a single master node through LSF. The hardware and software used in RCG are described. Utilization and performance issues, including parallel performance, are discussed based on the experience of the first two years of RCG operation.     

A Synchronous Co-Allocation Mechanism for Grid Computing Systems

Grid computing systems are emerging as a computing infrastructure that will enable the use of wide-area network computing systems for a variety of challenging applications. One of these is the ever increasing demand for multimedia from users engaging in a wide range of activities such as scientific research, education, commerce, and entertainment. To provide an adequate level of service to multimedia applications, it is often necessary to simultaneously allocate resources including predetermined capacities from interconnecting networks to the applications. The simultaneous allocation of resources is often referred to as co-allocation in the Grid literature. In this paper, we formally define the co-allocation problem and propose a novel scheme called synchronous queuing (SQ) for implementing co-allocation with quality of service (QoS) assurances in Grids. Unlike existing approaches, SQ does not require advance reservation capabilities at the resources. This enables an SQ-based approach to over subscribe the resources and hence improve resource utilization. The simulation studies performed to evaluate SQ indicate that it outperforms an QoS-based scheme with strict admission control by a significant margin.     

Cactus Tools for Grid Applications

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure facilitates parallel computation across different architectures and collaborative code development between different groups. The Cactus Code originated in the academic research community, where it has been developed and used over many years by a large international collaboration of physicists and computational scientists. We discuss here how the intensive computing requirements of physics applications now using the Cactus Code encourage the use of distributed and metacomputing, and detail how its design makes it an ideal application test-bed for Grid computing. We describe the development of tools, and the experiments which have already been performed in a Grid environment with Cactus, including distributed simulations, remote monitoring and steering, and data handling and visualization. Finally, we discuss how Grid portals, such as those already developed for Cactus, will open the door to global computing resources for scientific users.     

QHELIX: A Computational Tool for the Improved Measurement of Inter-Helical Angles in Proteins

Knowledge about the assembled structures of the secondary elements in proteins is essential to understanding protein folding and functionality. In particular, the analysis of helix geometry is required to study helix packing with the rest of the protein and formation of super secondary structures, such as, coiled coils and helix bundles, formed by packing of two or more helices. Here we present an improved computational method, QHELIX, for the calculation of the orientation angles between helices. Since a large number of helices are known to be in curved shapes, an appropriate definition of helical axes is a prerequisite for calculating the orientation angle between helices. The present method provides a quantitative measure on the irregularity of helical shape, resulting in discriminating irregular-shaped helices from helices with an ideal geometry in a large-scale analysis of helix geometry. It is also capable of straightforwardly assigning the direction of orientation angles in a consistent way. These improvements will find applications in finding a new insight on the assembly of protein secondary structure. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Programming the Grid: Distributed Software Components, P2P and Grid Web Services for Scientific Applications

Computational Grids [17,25] have become an important asset in large-scale scientific and engineering research. By providing a set of services that allow a widely distributed collection of resources to be tied together into a relatively seamless computing framework, teams of researchers can collaborate to solve problems that they could not have attempted before. Unfortunately the task of building Grid applications remains extremely difficult because there are few tools available to support developers. To build reliable and re-usable Grid applications, programmers must be equipped with a programming framework that hides the details of most Grid services and allows the developer a consistent, non-complex model in which applications can be composed from well tested, reliable sub-units. This paper describes experiences with using a software component framework for building Grid applications. The framework, which is based on the DOE Common Component Architecture (CCA) [1,2,3,8], allows individual components to export function/service interfaces that can be remotely invoked by other components. The framework also provides a simple messaging/event system for asynchronous notification between application components. The paper also describes how the emerging Web-services [52] model fits with a component-oriented application design philosophy. To illustrate the connection between Web services and Grid application programming we describe a simple design pattern for application factory services which can be used to simplify the task of building reliable Grid programs. Finally we address several issues of Grid programming that better understood from the perspective of Peer-to-Peer (P2P) systems. In particular we describe how models for collaboration and resource sharing fit well with many Grid application scenarios.     

An Evaluation of Alternative Designs for a Grid Information Service

Computational grids consisting of large and diverse sets of distributed resources have recently been adopted by organizations such as NASA and the NSF. One key component of a computational grid is an information services that provides information about resources, services, and applications to users and their tools. This information is required to use a computational grid and therefore should be available in a timely and reliable manner. In this work, we describe the Globus information service, describe how this service is used, analyze its current performance, and perform trace-driven simulations to evaluate alternative implementations of this grid information service. We find that the majority of the transactions with the information service are changes to the data maintained by the service. We also find that of the three servers we evaluate, one of the commercial products provides the best performance for our workload and that the response time of the information service was not improved during the single experiment we performed with data distributed across two servers.     

AutoMate: Enabling Autonomic Applications on the Grid

The increasing complexity, heterogeneity, and dynamism of emerging pervasive Grid environments and applications has necessitated the development of autonomic self-managing solutions, that are inspired by biological systems and deal with similar challenges of complexity, heterogeneity, and uncertainty. This paper introduces Project AutoMate and describes its key components. The overall goal of Project Automate is to investigate conceptual models and implementation architectures that can enable the development and execution of such self-managing Grid applications. Illustrative autonomic scientific and engineering Grid applications enabled by AutoMate are presented. The research presented in this paper is supported in part by the National Science Foundation via grants numbers ACI 9984357, EIA 0103674, EIA 0120934, ANI 0335244, CNS 0305495, CNS 0426354 and IIS 0430826. The authors would like to acknowledge the contributions of M. Agarwal, V. Bhat and N. Jiang to this research.     

In silico model‐based inference: A contemporary approach for hypothesis testing in network biology

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model‐based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model‐based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:1247–1261, 2014     

Distributed Shared Arrays: Portable Shared-Memory Programming Interface for Multiple Computer Systems

This paper describes the design and implementation of a parallel programming environment called Distributed Shared Array (DSA), which provides a shared global array abstract across different machines connected by a network. In DSA, users can define and use global arrays that can be accessed uniformly from any machines in the network. Explicit management of array area allocation, replication, and migration is achieved by explicit calls for array manipulation: defining array regions, reading and writing array regions, synchronization, and control of replication and migration. The DSA is integrated with Grid (Globus) services. This paper also describes the use of our model for gene cluster analysis, multiple alignment and molecular dynamics simulation. In these applications, global arrays are used for storing the distance matrix, alignment matrix and atom coordinates, respectively. Large array areas, which cannot be stored in the memory of individual machines, are made available by the DSA. Scalable performance of DSA was obtained compared to that of conventional parallel programs written in MPI.     

Condor-G: A Computation Management Agent for Multi-Institutional Grids

In recent years, there has been a dramatic increase in the number of available computing and storage resources. Yet few tools exist that allow these resources to be exploited effectively in an aggregated form. We present the Condor-G system, which leverages software from Globus and Condor to enable users to harness multi-domain resources as if they all belong to one personal domain. We describe the structure of Condor-G and how it handles job management, resource selection, security, and fault tolerance. We also present results from application experiments with the Condor-G system. We assert that Condor-G can serve as a general-purpose interface to Grid resources, for use by both end users and higher-level program development tools.     

Distributed File System Virtualization Techniques Supporting On-Demand Virtual Machine Environments for Grid Computing

This paper presents a data management solution which allows fast Virtual Machine (VM) instantiation and efficient run-time execution to support VMs as execution environments in Grid computing. It is based on novel distributed file system virtualization techniques and is unique in that: (1) it provides on-demand cross-domain access to VM state for unmodified VM monitors; (2) it enables private file system channels for VM instantiation by secure tunneling and session-key based authentication; (3) it supports user-level and write-back disk caches, per-application caching policies and middleware-driven consistency models; and (4) it leverages application-specific meta-data associated with files to expedite data transfers. The paper reports on its performance in wide-area setups using VMware-based VMs. Results show that the solution delivers performance over 30% better than native NFS and with warm caches it can bring the application-perceived overheads below 10% compared to a local-disk setup. The solution also allows a VM with 1.6 GB virtual disk and 320 MB virtual memory to be cloned within 160 seconds for the first clone and within 25 seconds for subsequent clones. Ming Zhao is a PhD candidate in the department of Electrical and Computer Engineering and a member of the Advance Computing and Information Systems Laboratory, at University of Florida. He received the degrees of BE and ME from Tsinghua University. His research interests are in the areas of computer architecture, operating systems and distributed computing. Jian Zhang is a PhD student in the Department of Electrical and Computer Engineering at University of Florida and a member of the Advance Computing and Information Systems Laboratory (ACIS). Her research interest is in virtual machines and Grid computing. She is a member of the IEEE and the ACM. Renato J. Figueiredo received the B.S. and M.S. degrees in Electrical Engineering from the Universidade de Campinas in 1994 and 1995, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Purdue University in 2001. From 2001 until 2002 he was on the faculty of the School of Electrical and Computer Engineering of Northwestern University at Evanston, Illinois. In 2002 he joined the Department of Electrical and Computer Engineering of the University of Florida as an Assistant Professor. His research interests are in the areas of computer architecture, operating systems, and distributed systems.     

GIV: A Tool for Genomic Islands Visualization

A Genomic Islands (GI) is a chunk of DNA sequence in a genome whose origin can be traced back to other organisms or viruses. The detection of GIs plays an indispensable role in biomedical research, due to the fact that GIs are highly related to special functionalities such as disease-causing GIs - pathogenicity islands. It is also very important to visualize genomic islands, as well as the supporting features corresponding to the genomic islands in the genome. We have developed a program, Genomic Island Visualization (GIV), which displays the locations of genomic islands in a genome, as well as the corresponding supportive feature information for GIs. GIV was implemented in C++, and was compiled and executed on Linux/Unix operating systems.

Availability

GIV is freely available for non-commercial use at http://www5.esu.edu/cpsc/bioinfo/software/GIV     

MAFCO: A Compression Tool for MAF Files

In the last decade, the cost of genomic sequencing has been decreasing so much that researchers all over the world accumulate huge amounts of data for present and future use. These genomic data need to be efficiently stored, because storage cost is not decreasing as fast as the cost of sequencing. In order to overcome this problem, the most popular general-purpose compression tool, gzip, is usually used. However, these tools were not specifically designed to compress this kind of data, and often fall short when the intention is to reduce the data size as much as possible. There are several compression algorithms available, even for genomic data, but very few have been designed to deal with Whole Genome Alignments, containing alignments between entire genomes of several species. In this paper, we present a lossless compression tool, MAFCO, specifically designed to compress MAF (Multiple Alignment Format) files. Compared to gzip, the proposed tool attains a compression gain from 34% to 57%, depending on the data set. When compared to a recent dedicated method, which is not compatible with some data sets, the compression gain of MAFCO is about 9%. Both source-code and binaries for several operating systems are freely available for non-commercial use at: http://bioinformatics.ua.pt/software/mafco.     

InSatDb: A Genomic Tool for Insect Geneticists

Microsatellites show tremendous variation between genomes in terms of their occurrence and composition. Availability of whole genome sequences allows us to study microsatellite characteristics of fully sequenced insect genomes to understand the evolution and biological significance of microsatellites. InSatDb is an insect microsatellite database that provides an interactive interface to query information on microsatellites annotated with size (in base pairs and repeat units); genomic location (exon, intron, up-stream or transposon); nature (perfect or imperfect); and sequence composition (repeat motif and GC%). Here, we present a snap shot of the distribution and composition of microsatellites in introns and exons of insect genomes. The data present interesting observations regarding the microsatellite life-cycle and genome flux.     

LIBS: A Quality Control Tool for Food Supplements

In the present paper the ability of calibration free laser induced breakdown spectroscopy (CF-LIBS) as a quality control tool to monitor the composition of different minerals present in food supplement samples belonging to Indian brands (brand-A and brand-B) has been demonstrated. LIBS spectra of these two food supplements (brand-A and brand-B) available in the form of tablet have been recorded. As reported by manufacturers of these two food supplements, LIBS spectra of brand-A contains the spectral signatures of minerals like Ca, Mg, C, P, Zn, Fe, Cu, and Cr whereas LIBS spectra of brand-B shows the presence of spectral lines like Ca, Mg and C. The spectral signatures of Na and K are also found in both brands whereas spectral signature of Ti is observed only in brand-B but these elements are not mentioned on the nutritional label of the brands. The quantitative analysis of mineral contents in food supplements has been done using CF-LIBS for brand A and brand B to verify the content of the minerals reported by the manufacturer of the food supplements. Our results show that Ca and Mg are the main matrix elements of these brands. The concentration of minor and trace elements estimated using CF-LIBS technique is found in agreement with the reported nutritional values of both the brands. The concentration of major elements Ca and Mg are also estimated from Atomic Absorption Spectroscopy which is in close agreement with CF-LIBS result.     

galign: A Tool for Rapid Genome Polymorphism Discovery

Background

Highly parallel sequencing technologies have become important tools in the analysis of sequence polymorphisms on a genomic scale. However, the development of customized software to analyze data produced by these methods has lagged behind.

Methods/Principal Findings

Here I describe a tool, ‘galign’, designed to identify polymorphisms between sequence reads obtained using Illumina/Solexa technology and a reference genome. The ‘galign’ alignment tool does not use Smith-Waterman matrices for sequence comparisons. Instead, a simple algorithm comparing parsed sequence reads to parsed reference genome sequences is used. ‘galign’ output is geared towards immediate user application, displaying polymorphism locations, nucleotide changes, and relevant predicted amino-acid changes for ease of information processing. To do so, ‘galign’ requires several accessory files easily derived from an annotated reference genome. Direct sequencing as well as in silico studies demonstrate that ‘galign’ provides lesion predictions comparable in accuracy to available prediction programs, accompanied by greater processing speed and more user-friendly output. We demonstrate the use of ‘galign’ to identify mutations leading to phenotypic consequences in C. elegans.

Conclusion/Significance

Our studies suggest that ‘galign’ is a useful tool for polymorphism discovery, and is of immediate utility for sequence mining in C. elegans.