Datum vypracování: 2004-10-21


Partial 'Blue Gene' Systems Are Now Two of the Top Ten Most Powerful Supercomputers on Earth



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Partial 'Blue Gene' Systems Are Now Two of the Top Ten Most Powerful Supercomputers on Earth


June 21, 2004--For the first time, two IBM Blue Gene/L prototype systems appear on the Top 10 list of supercomputers. The Blue Gene/L prototype represents a radical new design for supercomputing. At 1/20th the physical size of existing machines of comparable power, Blue Gene/L enables dramatic reductions in power consumption, cost and space requirements for businesses requiring immense computing power. For a new architecture to produce so much compute power in such a small package is a stunning achievement, and provides a glimpse of the future of supercomputing.

The number four-ranked Blue Gene/L DD1 Prototype, with a sustained speed of 11.68 teraflops and a peak speed of 16 teraflops, uses more than 8,000 PowerPC processors packed into just four refrigerator-sized racks. This ground breaking system is only 1/16 of its planned final capacity and has skyrocketed to the 4th place from the 73rd spot on the list in November 2003. The eighth-ranked Blue Gene/L DD2 Prototype has a sustained speed of 8.66 teraflops and a peak speed of 11.47 teraflops. The DD2 system is based on the second generation of the Blue Gene/L chips, which are more powerful than those used in the DD1 prototype.



About IBM's Blue Gene Supercomputing Project
Blue Gene is an IBM supercomputing project dedicated to building a new family of supercomputers optimized for bandwidth, scalability and the ability to handle large amounts of data while consuming a fraction of the power and floor space required by today's fastest systems. The full Blue Gene/L machine is being built for the Lawrence Livermore National Laboratory in California, and will have a peak speed of 360 teraflops. When completed in 2005, IBM expects Blue Gene/L to lead the Top500 supercomputer list. A second Blue Gene/L machine is planned for ASTRON, a leading astronomy organization in the Netherlands. IBM and its partners are currently exploring a growing list of applications including hydrodynamics, quantum chemistry, molecular dynamics, climate modeling and financial modeling. Read more...


Presentations, Preprints, and Publications
Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory; The Journal of Physical Chemistry B; 108(21); 6571-6581

Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 2. Example Applications to Alanine Dipeptide and a beta-Hairpin Peptide; The Journal of Physical Chemistry B; 108(21); 6582-6594

Agenda and Presentations for Blue Gene Briefing Day--February 6, 2004

Molecular Dynamics Investigation of the Structural Properties of Phosphatidylethanolamine Lipid Bilayers

Design and Analysis of the BlueGene/L Torus Interconnection Network

A Volumetric FFT for Blue Gene/L, to appear in the Proceedings of HiPC2003

Blue Matter, An Application Framework for Molecular Simulation on Blue Gene, Journal of Parallel and Distributed Computing Volume 63, Issues 7-8 July-August 2003 , Pages 759-773

Understanding folding and design: Replica-exchange simulations of "Trp-cage" miniproteins, Proc. Natl. Acad. Sci. USA, Vol. 100, Issue 13, June 24, 2003, pp. 7587-7592

An overview of the BlueGene/L supercomputer, Supercomputing 2002 Technical Papers, November 2002

Can a continuum solvent model reproduce the free energy landscape of a beta-hairpin folding in water?, Proc. Natl. Acad. Sci. USA, Vol. 99, Issue 20, October 1, 2002, pp. 12777-12782

The free energy landscape for beta-hairpin folding in explicit water, Proc. Natl. Acad. Sci. USA, Vol. 98, Issue 26, December 18, 2001, pp. 14931-14936

Blue Gene Project Update

Efficient multiple time step method for use with Ewald and particle mesh Ewald for large biomolecular systems, The Journal of Chemical Physics, Volume 115, Issue 5, 2001, pp. 2348-2358

Blue Gene: A vision for protein science using a petaflop supercomputer, IBM Systems Journal, Volume 40, Number 2, 2001, p. 310

Industry Links
Unraveling the Mystery of Protein Folding
Physicists Take on Challenge Of Showing How Proteins Fold, The Scientist
The Bridge from Genes to Proteins

Informace aplikaci AlphaServer společnosti HP v projektu GeneProt:

http://www.hp.com/techservers/life_sciences/success_geneprot.pdf



Informace o projektu skupiny „Computational biology“ (www.sun.com/edu/hpc/compbiosig):

http://www.sun.com/products-n-solutions/edu/events/archive/hpc/presentations/june01/stefan_unger.pdf

Databáze Dialog:

Konference: (Vybráno bylo 61 záznamů z odborných konferencí v posledním období r. 2004)

1)

A sequence-focused parallelisation of EMBOSS on a cluster of workstations
Podesta, K.; Crane, M.; Ruskin, H.J.
Sch. of Comput., Dublin City Univ., Ireland
Conference: Computational Science and it's Applications - ICCSA 2004. International Conference. Proceedings (Lecture Notes in Comput. Sci. Vol.3045)
Part: Vol.3 , Page: 473-80 Vol.3
Editor: Lagana, A.; Gavrilova,M.L.; Kumar,V.; Mun,Y.; Tan,C.J.K.; Gervasi,O.
Publisher: Springer-Verlag , Berlin, Germany , 2004 , 4588 Pages
Conference: Computational Science and it's Applications - ICCSA 2004. International Conference. Proceedings , Sponsor: Univ. of Perugia, Italy, Univ. of Calgary, Canada, Univ. of Minnesota, USA, Queen's Univ. of Belfast, UK, Heuchera Technol., UK, GRID.IT: Enabling Platforms for High-Performance Computational Grids Oriented to Scalable Virtual Organizations of the Minitsty of Sci. and Educ. of Italy, COST - European Cooperation in the Field of Sci. and Tech. Res , 14-17 May 2004 , Assisi, Italy
Language: English

Abstract: A number of individual bioinformatics applications (particularly BLAST and other sequence searching methods) have recently been implemented over clusters of workstations to take advantage of extra processing power. Performance improvements are achieved for increasingly large sets of input data (sequences and databases), using these implementations. We present an analysis of programs in the EMBOSS suite based on increasing sequence size, and implement these programs in parallel over a cluster of workstations using sequence segmentation with overlap. We observe general increases in runtime for all programs, and examine the speedup for the most intensive ones to establish an optimum segmentation size for those programs across the cluster.

2)

Genome database integration


Robinson, A.; Rahayu, W.
Dept. Comput. Sci. & Comput. Eng., LaTrobe Univ., Bundoora, Vic., Australia
Conference: Computational Science and it's Applications - ICCSA 2004. International Conference. Proceedings (Lecture Notes in Comput. Sci. Vol.3045)
Part: Vol.3 , Page: 443-53 Vol.3
Editor: Lagana, A.; Gavrilova,M.L.; Kumar,V.; Mun,Y.; Tan,C.J.K.; Gervasi,O.
Publisher: Springer-Verlag , Berlin, Germany , 2004 , 4588 Pages
Conference: Computational Science and it's Applications - ICCSA 2004. International Conference. Proceedings , Sponsor: Univ. of Perugia, Italy, Univ. of Calgary, Canada, Univ. of Minnesota, USA, Queen's Univ. of Belfast, UK, Heuchera Technol., UK, GRID.IT: Enabling Platforms for High-Performance Computational Grids Oriented to Scalable Virtual Organizations of the Minitsty of Sci. and Educ. of Italy, COST - European Cooperation in the Field of Sci. and Tech. Res , 14-17 May 2004 , Assisi, Italy
Language: English

Abstract: This paper presents a solution to many of the problems in genome database integration including an integrated interface for accessing all genome databases simultaneously and the problem of a common interchange data format. The solution is the addition of a middle or mediation layer of a three layer approach. The solution provides a simple step by step approach to connect other existing genome databases quickly and efficiently. The internal data format used is a commonly used bioinformatics format called BSML, a subset of the XML standard. The architecture also allows easy addition and deletion of functionality. Finally, an implementation of this solution is presented with the required support functionality to validate the proposed integration method.

3)

Cell modeling using agent-based formalisms


Webb, K.; White, T.
Sch. of Comput. Sci., Carleton Univ., Ont., Canada
Conference: Innovations in Applied Artificial Intelligence. 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004. Proceedings (Lecture Notes in Artificial Intelligence Vol.3029) , Page: 128-37
Editor: Orchard, B.; Yang, C.; Ali, M.
Publisher: Springer-Verlag , Berlin, Germany , 2004 , xxi+1272 Pages
Conference: Innovations in Applied Artificial Intelligence. 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004. Proceedings , 17-20 May 2004 , Ottawa, Ont., Canada
Language: English

Abstract: The systems biology community is building increasingly complex models and simulations of cells and other biological entities. In doing so the community is beginning to look at alternatives to traditional representations such as those provided by ordinary differential equations (ODE). Making use of the object-oriented (OO) paradigm, the unified modeling language (UML) and real-time object-oriented modeling (ROOM) visual formalisms, we describe a simple model that includes membranes with lipid bilayers, multiple compartments including a variable number of mitochondria, substrate molecules, enzymes with reaction rules, and metabolic pathways. We demonstrate the validation of the model by comparison with Gepasi and comment on the reusability of model components.

4)

Digital signal processing in predicting secondary structures of proteins


Mitra, D.; Smith, M.
Dept. of Comput. Sci., Florida Inst. of Technol., Melbourne, FL, USA
Conference: Innovations in Applied Artificial Intelligence. 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004. Proceedings (Lecture Notes in Artificial Intelligence Vol.3029) , Page: 40-9
Editor: Orchard, B.; Yang, C.; Ali, M.
Publisher: Springer-Verlag , Berlin, Germany , 2004 , xxi+1272 Pages
Conference: Innovations in Applied Artificial Intelligence. 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004. Proceedings , 17-20 May 2004 , Ottawa, Ont., Canada
Language: English

Abstract: Traditionally protein secondary structure prediction methods work with aggregate knowledge gleaned over a training set of proteins, or with some knowledge acquired from the experts about how to assign secondary structural elements to each amino acid. We are proposing here a methodology that is primarily targeted for any given query protein rather being trained over a pre-determined training set. For some query proteins our prediction accuracies are predictably higher than most other methods, while for other proteins they may not be so, but we would at least know that even before running the algorithms. Our method is based on homology-modeling. When a significantly homologous protein (to the query) with known structure is available in the database our prediction accuracy could be even 90% or above. Our objective is to improve the accuracy of the predictions for the so called "easy" proteins (where sufficiently similar homologues with known structures are available), rather than improving the bottom-line of the structure prediction problem, or the average prediction accuracy over many query proteins. We use digital signal processing (DSP) technique that is of global nature in assigning structural elements to the respective residues. This is the key to our success. We have tried some variation of the proposed core methodology and the experimental results are presented in this article.

5)

Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering


Publisher: IEEE , Los Alamitos, CA, USA , 2004 , xviii+613 Pages
Conference: Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering , 19-21 May 2004 , Taichung, Taiwan
Language: English

Abstract: The following topics are dealt with: bioengineering; data integration, medical image processing; parallel computing; medical informatics; gene analysis; transcriptome and functional genomics; homology search; structural biology; algorithms; protein-protein interactions, indexing techniques and intelligent systems.

6)

Lymphoma cancer classification using genetic programming with SNR features


Jin-Hyuk Hong; Sung-Bae Cho
Dept. of Comput. Sci., Yonsei Univ., South Korea
Conference: Genetic Programming. 7th European Conference on Genetic Programming EuroGP 2004. Proceedings. (Lecture Notes in Comput. Sci. Vol.3003) , Page: 78-88
Editor: Keijzer, M.; O'Reilly, U.-M.; Lucas, S.M.; Costa, E.; Soule, T.
Publisher: Springer-Verlag , Berlin, Germany , 2004 , xi+410 Pages
Conference: Genetic Programming. 7th European Conference on Genetic Programming EuroGP 2004. Proceedings , 5-7 April 2004 , Coimbra, Portugal
Language: English

Abstract: Lymphoma cancer classification with DNA microarray data is one of the important problems in bioinformatics. Many machine learning techniques have been applied to the problem and produced valuable results. However the medical field requires not only a high-accuracy classifier, but also the in-depth analysis and understanding of classification rules obtained. Since gene expression data have thousands of features, it is nearly impossible to represent and understand their complex relationships directly. In this paper, we adopt the SNR (signal-to-noise ratio) feature selection to reduce the dimensionality of the data, and then use genetic programming to generate cancer classification rules with the features. In the experimental results on Lymphoma cancer dataset, the proposed method yielded 96.6% test accuracy on average, and an excellent arithmetic classification rule set that classifies all the samples correctly is discovered by the proposed method.

7)

Bioinformatics in the undergraduate curriculum: opportunities for computer science educators


Burhans, D.T.; Doom, T.E.; DeJongh, M.; Leblanc, M.
Dept. of Comput. Sci., Canisius Coll., Buffalo, NY, USA
SIGCSE Bulletin
Conference: SIGCSE Bull. (USA) , vol.36, no.1 , Page: 229-30
Publisher: ACM , March 2004
Conference: Thirty-Fifth SIGCSE Technical Symposium on Computer Science Education , Sponsor: ACM Spcial Interest Group on Comput. Sci. Educ , 3-7 March 2004 , Norfolk, VA, USA
Language: English

Abstract: Biology has become an increasingly data-driven science. Modern experimental techniques, including automated DNA sequencing, gene expression micro arrays, and X-ray crystallography are producing molecular data at a rate that has made traditional data analysis methods impractical. Computational methods are becoming an increasingly important aspect of the evaluation and analysis of experimental data in molecular biology. Bioinformatics is the term coined for the new field that merges biology and computer science to manage and analyze this data, with the ultimate goal of understanding and modeling living systems (2003). The emergence of bioinformatics provides new challenges and opportunities for computer science educators. This panel assembles four individuals who collectively have experience teaching bioinformatics at both liberal arts colleges and universities, and who also have industry experience in bioinformatics, to discuss various approaches to incorporating bioinformatics into the undergraduate curriculum.

8)

hMiDas and hMitChip: new opportunities in mitochondrial bioinformatics and genomic medicine


Alesci, S.; Su, Y.A.; Chrousos, G.P.
Conference: Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems , Page: 329-34
Editor: Long, R.; Antani, S.; Lee, D.J.; Nutter, B.; Zhang, M.
Publisher: IEEE Comput. Soc , Los Alamitos, CA, USA , 2004 , xv+603 Pages
Conference: Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems , Sponsor: IEEE Comput. Soc. Tech. Committee on Computational Medicine, Texas Tech Univ. College of Eng , 24-25 June 2004 , Bethesda, MD, USA
Language: English

Abstract: We developed a human mitochondria-focused gene database (hMiDas) and customized cDNA microarray chip (hMitChip) to help biomedical research in mitochondrial genomics. The current version of hMiDas contains 1,242 gene entries (including mtDNA genes, nuclear genes related to mitochondria structure and junctions, predicted loci and experimental genes), organized in 15 categories and 24 subcategories. The database interface allows keyword-based searches as well as advanced field and/or case-sensitive searches. Each gene record includes 19 fields, mostly hyperlinked to the corresponding source. Moreover, for each gene, the user is given the option to run literature search using PubMed, and gene/protein homology search using BLAST and FASTA. The hMitChip was constructed using hMiDas as a reference. Currently, it contains a selection of 501 mitochondria-related nuclear genes and 192 control elements, all spotted in duplicate on glass slides. Slide quality was checked by microarray hybridization with 50 mu g of Cy3-labeled sample cDNA and Cy5-labeled comparing cDNA, followed by array scan and image analysis. The hMitChip was tested in vitro using RNA extracted from cancer cell lines. Gene expression changes detected by hMitChip were confirmed by quantitative real-time RT-PCR analysis.

9)

From sequence to structure using PF2: improving methods for protein folding prediction


Hussain, S.
Conference: Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems , Page: 323-8
Editor: Long, R.; Antani, S.; Lee, D.J.; Nutter, B.; Zhang, M.
Publisher: IEEE Comput. Soc , Los Alamitos, CA, USA , 2004 , xv+603 Pages
Conference: Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems , Sponsor: IEEE Comput. Soc. Tech. Committee on Computational Medicine, Texas Tech Univ. College of Eng , 24-25 June 2004 , Bethesda, MD, USA
Language: English

Abstract: Projects dependent on proteomic data are challenged not by the lack of methods to analyze this information, but by the lack of means to capture and manage the data. A few primary players in the bioinformatics realm are promoting the use of selected standardized technologies to access biological data. Many organizations exposing bioinformatics tools, however, do not have the resources required for utilizing these technologies. In order to provide interfaces for non-standardized bioinformatics tools, open-source projects have led to the development of hundreds of software libraries. These tools lack architectural unity, making it difficult to script bioinformatics research projects, such as protein structure prediction algorithms, which involve the use of multiple tools in varying order and number. As a solution, we have focused on building a software model, named the Protein Folding Prediction Framework (PF2), which provides a unifying method for the addition and usage of connection modules to bioinformatics databases exposed via Web-based tools, software suites, or e-mail services. The framework provides mechanisms that allow users to create and add new connections without supplementary code as well as to introduce entirely new logical scenarios. In addition, PF2 offers a convenient interface, a multi-threaded execution-engine, and a built-in visualization suite to provide the bioinformatics community with an end-to-end solution for performing complex genomic and proteomic inquiries.

10)


Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems
Editor: Long, R.; Antani, S.; Lee, D.J.; Nutter, B.; Zhang, M.
Publisher: IEEE Comput. Soc , Los Alamitos, CA, USA , 2004 , xv+603 Pages
Conference: Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems , Sponsor: IEEE Comput. Soc. Tech. Committee on Computational Medicine, Texas Tech Univ. College of Eng , 24-25 June 2004 , Bethesda, MD, USA
Language: English

Abstract: The following topics are dealt with: medical databases; content-based image retrieval; medical systems; signal processing; imaging, telemedicine; data mining; image processing; pattern recognition; segmentation; medical devices; image processing tools; clinical applications; handheld computing for medicine; decision support systems; and bioinformatics.

11)


Integrating ontology and workflow in PROTEUS, a grid-based problem solving environment for bioinformatics
Cannataro, M.; Comito, C.; Guzzo, A.; Veltri, P.
Univ. of Catanzaro, Italy
Conference: Proceedings. ITCC 2004. International Conference on Information Technology: Coding and Computing
Part: Vol.2 , Page: 90-4 Vol.2
Editor: Srimani, P.K.
Publisher: IEEE Comput. Soc , Los Alamitos, CA, USA , 2004 , 1710 Pages
Conference: Proceedings. ITCC 2004. International Conference on Information Technology: Coding and Computing , Sponsor: IEEE Comput. Soc. Task Force on Information Technology for Business Application , 5-7 April 2004 , Las Vegas, NV, USA
Language: English

Abstract: Bioinformatics is as a bridge between life science and computer science: computer algorithms are needed to face complexity of biological processes. Bioinformatics applications manage complex biological data stored into distributed and often heterogeneous databases and require large computing power. We discuss requirements of such applications and present the architecture of PROTEUS, a grid-based problem solving environment that integrates ontology and workflow approaches to enhance composition and execution of bioinformatics applications on the grid.

12)


Algorithm Theory - SWAT 2004. 9th Scandinavian Workshop on Algorithm Theory. Proceedings (Lecture Notes in Comput. Sci. Vol.3111)
Editor: Hagerup, T.; Katajainen, J.
Publisher: Springer-Verlag , Berlin, Germany , 2004 , xi+506 Pages
Conference: Algorithm Theory - SWAT 2004. 9th Scandinavian Workshop on Algorithm Theory. Proceedings , Sponsor: DIKU, Univ. of Southern Denmark, Dept. of Math. and Comput. Sci., IT Univ. Copenhagen, Danish Nat. Sci. Res. Council, First Graduate School, LESS, Nokia, SAS , 8-10 July 2004 , Humlebaek, Denmark

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