The goal of EMBgrid is to overcome relevant infrastructure needs in Bioinformatics development and services in order to address future needs for the provision of useful Bioinformatics solutions to the Biosciences community. The proposed approach will make use of an extended Grid architecture, built on top of existing EU-DATAGRID services and deployed over a wide number of European nodes, to enable delivery of advanced tools and solutions to the growing demands of Biosciences. We contemplate creation of a pan-european Grid resource, related to other EU Grid initiatives and devoted mainly to Bioinformatics. This initiative will raise Europe's competitiveness in this and related fields. Successful deployment and dissemination will be facilitated by EMBnet long track in delivering tools, services and training to the European scientific community.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI

De Giusti, Armando Eduardo

Denham, Mónica Malén

Grau, Oscar

Russo, Claudia Cecilia

Sanz, Cecilia Verónica

Tinetti, Fernando Gustavo

SEDICI - Repositorio de la UNLP

Distributed and Parallel Processing for the Embgrid Project
Sanz, Cecilia2, Tinetti Fernando3,  Russo Claudia4, Denham Monica5, De Giusti, Armando1, Grau
Oscar6
{degiusti, csanz, fernando, crusso, mdenham}@lidi.info.unlp.edu.ar
grau@biol.unlp.edu.ar
LIDI. Laboratorio de Investigación y Desarrollo en Informática 7.
Facultad de Informática. UNLP.
50 y 115. 1er Piso. La Plata
Summary
The goal of EMBgrid is to overcome relevant infrastructure needs in Bioinformatics development
and services in order to address future needs for the provision of useful Bioinformatics solutions to
the Biosciences community. The proposed approach will make use of an extended Grid
architecture, built on top of existing EU-DATAGRID services and deployed over a wide number of
European nodes, to enable delivery of advanced tools and solutions to the growing demands of
Biosciences. We contemplate creation of a pan-european Grid resource, related to other EU Grid
initiatives and devoted mainly to Bioinformatics. This initiative will raise Europe's competitiveness
in this and related fields. Successful deployment and dissemination will be facilitated by EMBnet
long track in delivering tools, services and training to the European scientific community.
There is a cooperative argentinian group involved in this project, and has the responsability of
analyze the specification, transformation, optimization and verification of concurrent algorithms
executable in distributed/parallel systems; they also have to study the optimization of solutions
types in function of multiprocessor architecture models and complex and efficiency metrics related
to the parallel processing.
In Biocomputing, the need of distributing advanced tools and solutions has arisen for the growing
data generation which takes place in a hyper-exponential manner, and for the solution of complex
problems.
The specific goal proposed for this project is to analyze some Emboss programs, which are used in
Biocomputing area (in particular, those related with alignment, and pattern matching), in order to
transform them into parallel and distributed algorithms.
1 Investigador Principal CONICET. Profesor Titular Ded. Exclusiva.
2 Profesor Dedicación Exclusiva - Facultad de Informática. UNLP.
3 Profesor Dedicación Semi Exclusiva - Facultad de Informática. UNLP.
4 Profesor Dedicación Exclusiva - Facultad de Informática. UNLP.
5 Ayudante Alumno - Becaria LIDI - Facultad de Informática. UNLP.
6 Profesor Dedicación Exclusiva -  Director IBBM - Facultad de Ciencias Exactas. UNLP.
7 LIDI - Facultad de Informática. UNLP - Calle 50 y 115 1er Piso, (1900) La Plata, Argentina.
TE/Fax +(54)(221)422-7707. http://lidi.info.unlp.edu.ar
Workshop de Investigadores en Ciencias de la Computación WICC 2002
Página 385691
Research Topics
Processing Resources Use Optimization
Obtaining the maximum efficiency of a multiprocessor architecture represents an essential objective
in parallel processing. The research in techniques that lead to upgrade the performance of a given
architecture, or to adjust dynamically the processing resource assignation, is the axis of this line of
work. As examples, we can quote specific techniques of:
! Charge Balance: processing and/or communications, depending on the application and the
parallelization. All deviation as regards the charge equilibrium leads to the loss of the resource
use and, therefore, to the loss of the general output.
! Process and processors distribution: the processes distribution in the architecture is specifically
related to the processing charge balance and, therefore, it must be analyzed and optimized [1].
! Migration of data and/or processes: by analyzing the cost-benefit relation of keeping the data
static distribution and processing of all the application, or adapt it dynamically on the basis of
the parallel execution.
Parallelization of Applications
The parallelization of applications with large computing requirements are objects to be studied for
their computational resolution with parallel processing [2][3]. The classical techniques of :
a) Algorithm transformations,
b) Adaptation of algorithms to architecture models, and
c) Specification and verification of real time parallel systems,
are under process of research, looking for their improvement (at least, by areas of massive data
processing problems), trying to turn them into applicable systematic methodologies with the minor
quantity of potential adaptations to different problems within the area of the given problem.
Parallelism Metrics
In general, they are dealt with from several points of view, such as [4]:
a) Complexity analysis of sequential and parallel algorithms. Considering this analysis as a
basis, a type of solution can be adopted rationally.
b) Output analysis, with which the quality or the cost-benefit relation to the parallel
solutions proposed for the solution of the specific problem can be assessed.
c) Characterization of parallel applications and architectures in order to clearly identify
both the applications and architectures, and to attain a supported proposal of the
proposed solution type.
Parallel Processing Architectures [5]
In data massive processing, the following topics are relevant:
a) The cost-benefit relation and the output in function of the processing potential capacity
of parallel computing architectures (such as parallel supercomputers and workstations
nets or clusters)
b) Dynamic reconfiguration of hardware and/or software in order to adapt to the application
and/or to propose failure tolerance.
Parallel hardware design oriented to application classes.
Workshop de Investigadores en Ciencias de la Computación WICC 2002
Página 386792
Experimental work [6][7]
- Analysis of  Emboss sequential programs
It is necessary to evaluate which programs need to be improve in order to obtain a better
performance for them in cases of massive data treatment. The algorithm specification and the
parallel programming paradigm should be studied in order to optimize time response.
- Parallel system evaluation
On of the analysis to make is evaluate the costs of the parallel solutions. There are different
alternatives for the parallel systems implementation with homogeneous or heterogeneous hardware
and with weekly or strongly coupled components. These alternatives give various possibilities that
can be traduced in costs (including communication costs), which must be evaluated taken into
account the relation cost/performance. The costs are not only related with hardware but with the
development of algorithms and/or the algorithms adjustment for the processing architecture.
The performance of a parallel system is given by a complex relation among different factors (size of
the problem, support architecture, process distribution among processors, balance charge algorithm
existence, etc.). There are numerous metrics to evaluate parallel systems, such as, speed up,
efficient, etc.
- Developed parallel solutions
Developed parallel/solutions for Emboss programs related with alignment and pattern matching on
a cluster of homogeneous PCs, studying absolute performance and speed up.
Study of the best distribution (analyzing the local replication grade) for large data bases related with
this project, in order to optimize their accessibility. To achieve this goal, a simulation environment
for data base development (implemented by LIDI`s group) will be used.
The experimental architecture models proposed (and available for the project) are four:
homogeneous multiprocessor architectures highly coupled, transputer cube type; distributed
multiprocessor architectures (homogeneous or heterogeneous) weakly coupled NOW type;
multiprocessors dedicated to DSPs-based image treatment; and multiprocessor architectures of
shared memory SGI Origin 2000 type (Clementina).
Workshop de Investigadores en Ciencias de la Computación WICC 2002
Página 387893
Conclusions and Future work
An important research area is the specification, transformation, optimization and evaluation of
distributed and parallel algorithms. The optimization of solutions in function of different
architecture models and the study of complexity and efficiency metrics are of special interest. These
encompass the development of parallel processes, the transformation of sequential algorithms in
parallel (exploiting the implicit or explicit concurrency in the problem to be solved) and the
performance assessment metrics on different supporting platforms (both of hardware and software).
There exist several areas in which parallel and distributed programming is required - as it can be
observed in the project “Development of Grid Environment for Biological Applications”, in which
is involved this research line.
The group has experience in the area of parallel and distributed processing applied to complex
problems where large volumes of data are involved. Actually, emboss programs are being analyzed
and studied, in order to obtain parallel solutions.
Bibliography
[1]  Bell, David; Grimson, Jane, "Distributed Database Systems",. Addison Wesley. 1992
[2] Brinch Hansen, P., “Studies in computational science: Parallel Programming Paradigms”,
Prentice-Hall, Inc., 1995.
[3] Gupta A., Kumar V., “Performance properties of large scale parallel systems”, Journal of
Parallel and Distributed Computing, November 1993.
[4] Hwang K., “Advanced Computer Architecture: Paralelism, Scalability, Programability”,
McGraw, 1993.
[5] IEEE Transactions on Parallel and Distributed Processing (colección de revistas 1990-2002)
[6] Zomaya A., “Parallel Computing. Paradigms and Applications”, Int. Thomson Computer Press,
1996.
[7] http://www.embnet.org/
Workshop de Investigadores en Ciencias de la Computación WICC 2002
Página 388994


English

Distributed and parallel processing for the embgrid project

Servicio de Difusión de la Creación Intelectual

Distributed and Parallel Processing for the Embgrid ProjectSanz, Cecilia2, Tinetti Fernando3,  Russo Claudia4, Denham Monica5, De Giusti, Armando1, GrauOscar6{degiusti, csanz, fernando, crusso, mdenham}@lidi.info.unlp.edu.argrau@biol.unlp.edu.arLIDI. Laboratorio de Investigación y Desarrollo en Informática 7.Facultad de Informática. UNLP.50 y 115. 1er Piso. La PlataSummaryThe goal of EMBgrid is to overcome relevant infrastructure needs in Bioinformatics developmentand services in order to address future needs for the provision of useful Bioinformatics solutions tothe Biosciences community. The proposed approach will make use of an extended Gridarchitecture, built on top of existing EU-DATAGRID services and deployed over a wide number ofEuropean nodes, to enable delivery of advanced tools and solutions to the growing demands ofBiosciences. We contemplate creation of a pan-european Grid resource, related to other EU Gridinitiatives and devoted mainly to Bioinformatics. This initiative will raise Europe's competitivenessin this and related fields. Successful deployment and dissemination will be facilitated by EMBnetlong track in delivering tools, services and training to the European scientific community.There is a cooperative argentinian group involved in this project, and has the responsability ofanalyze the specification, transformation, optimization and verification of concurrent algorithmsexecutable in distributed/parallel systems; they also have to study the optimization of solutionstypes in function of multiprocessor architecture models and complex and efficiency metrics relatedto the parallel processing.In Biocomputing, the need of distributing advanced tools and solutions has arisen for the growingdata generation which takes place in a hyper-exponential manner, and for the solution of complexproblems.The specific goal proposed for this project is to analyze some Emboss programs, which are used inBiocomputing area (in particular, those related with alignment, and pattern matching), in order totransform them into parallel and distributed algorithms.1 Investigador Principal CONICET. Profesor Titular Ded. Exclusiva.2 Profesor Dedicación Exclusiva - Facultad de Informática. UNLP.3 Profesor Dedicación Semi Exclusiva - Facultad de Informática. UNLP.4 Profesor Dedicación Exclusiva - Facultad de Informática. UNLP.5 Ayudante Alumno - Becaria LIDI - Facultad de Informática. UNLP.6 Profesor Dedicación Exclusiva -  Director IBBM - Facultad de Ciencias Exactas. UNLP.7 LIDI - Facultad de Informática. UNLP - Calle 50 y 115 1er Piso, (1900) La Plata, Argentina.TE/Fax +(54)(221)422-7707. http://lidi.info.unlp.edu.arWorkshop de Investigadores en Ciencias de la Computación WICC 2002Página 385691Research TopicsProcessing Resources Use OptimizationObtaining the maximum efficiency of a multiprocessor architecture represents an essential objectivein parallel processing. The research in techniques that lead to upgrade the performance of a givenarchitecture, or to adjust dynamically the processing resource assignation, is the axis of this line ofwork. As examples, we can quote specific techniques of:! Charge Balance: processing and/or communications, depending on the application and theparallelization. All deviation as regards the charge equilibrium leads to the loss of the resourceuse and, therefore, to the loss of the general output.! Process and processors distribution: the processes distribution in the architecture is specificallyrelated to the processing charge balance and, therefore, it must be analyzed and optimized [1].! Migration of data and/or processes: by analyzing the cost-benefit relation of keeping the datastatic distribution and processing of all the application, or adapt it dynamically on the basis ofthe parallel execution.Parallelization of ApplicationsThe parallelization of applications with large computing requirements are objects to be studied fortheir computational resolution with parallel processing [2][3]. The classical techniques of :a) Algorithm transformations,b) Adaptation of algorithms to architecture models, andc) Specification and verification of real time parallel systems,are under process of research, looking for their improvement (at least, by areas of massive dataprocessing problems), trying to turn them into applicable systematic methodologies with the minorquantity of potential adaptations to different problems within the area of the given problem.Parallelism MetricsIn general, they are dealt with from several points of view, such as [4]:a) Complexity analysis of sequential and parallel algorithms. Considering this analysis as abasis, a type of solution can be adopted rationally.b) Output analysis, with which the quality or the cost-benefit relation to the parallelsolutions proposed for the solution of the specific problem can be assessed.c) Characterization of parallel applications and architectures in order to clearly identifyboth the applications and architectures, and to attain a supported proposal of theproposed solution type.Parallel Processing Architectures [5]In data massive processing, the following topics are relevant:a) The cost-benefit relation and the output in function of the processing potential capacityof parallel computing architectures (such as parallel supercomputers and workstationsnets or clusters)b) Dynamic reconfiguration of hardware and/or software in order to adapt to the applicationand/or to propose failure tolerance.Parallel hardware design oriented to application classes.Workshop de Investigadores en Ciencias de la Computación WICC 2002Página 386792Experimental work [6][7]- Analysis of  Emboss sequential programsIt is necessary to evaluate which programs need to be improve in order to obtain a betterperformance for them in cases of massive data treatment. The algorithm specification and theparallel programming paradigm should be studied in order to optimize time response.- Parallel system evaluationOn of the analysis to make is evaluate the costs of the parallel solutions. There are differentalternatives for the parallel systems implementation with homogeneous or heterogeneous hardwareand with weekly or strongly coupled components. These alternatives give various possibilities thatcan be traduced in costs (including communication costs), which must be evaluated taken intoaccount the relation cost/performance. The costs are not only related with hardware but with thedevelopment of algorithms and/or the algorithms adjustment for the processing architecture.The performance of a parallel system is given by a complex relation among different factors (size ofthe problem, support architecture, process distribution among processors, balance charge algorithmexistence, etc.). There are numerous metrics to evaluate parallel systems, such as, speed up,efficient, etc.- Developed parallel solutionsDeveloped parallel/solutions for Emboss programs related with alignment and pattern matching ona cluster of homogeneous PCs, studying absolute performance and speed up.Study of the best distribution (analyzing the local replication grade) for large data bases related withthis project, in order to optimize their accessibility. To achieve this goal, a simulation environmentfor data base development (implemented by LIDI`s group) will be used.The experimental architecture models proposed (and available for the project) are four:homogeneous multiprocessor architectures highly coupled, transputer cube type; distributedmultiprocessor architectures (homogeneous or heterogeneous) weakly coupled NOW type;multiprocessors dedicated to DSPs-based image treatment; and multiprocessor architectures ofshared memory SGI Origin 2000 type (Clementina).Workshop de Investigadores en Ciencias de la Computación WICC 2002Página 387893Conclusions and Future workAn important research area is the specification, transformation, optimization and evaluation ofdistributed and parallel algorithms. The optimization of solutions in function of differentarchitecture models and the study of complexity and efficiency metrics are of special interest. Theseencompass the development of parallel processes, the transformation of sequential algorithms inparallel (exploiting the implicit or explicit concurrency in the problem to be solved) and theperformance assessment metrics on different supporting platforms (both of hardware and software).There exist several areas in which parallel and distributed programming is required - as it can beobserved in the project “Development of Grid Environment for Biological Applications”, in whichis involved this research line.The group has experience in the area of parallel and distributed processing applied to complexproblems where large volumes of data are involved. Actually, emboss programs are being analyzedand studied, in order to obtain parallel solutions.Bibliography[1]  Bell, David; Grimson, Jane, "Distributed Database Systems",. Addison Wesley. 1992[2] Brinch Hansen, P., “Studies in computational science: Parallel Programming Paradigms”,Prentice-Hall, Inc., 1995.[3] Gupta A., Kumar V., “Performance properties of large scale parallel systems”, Journal ofParallel and Distributed Computing, November 1993.[4] Hwang K., “Advanced Computer Architecture: Paralelism, Scalability, Programability”,McGraw, 1993.[5] IEEE Transactions on Parallel and Distributed Processing (colección de revistas 1990-2002)[6] Zomaya A., “Parallel Computing. Paradigms and Applications”, Int. Thomson Computer Press,1996.[7] http://www.embnet.org/Workshop de Investigadores en Ciencias de la Computación WICC 2002Página 388994

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Distributed and parallel processing for the embgrid project

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