Euro-Par 2001 Parallel Processing

A software component framework is one where an application designer programs by composing well understood and tested “components” rather than writing large volumes of not-very-reusable code. The software industry has been using component technology to build desktop applications for about ten years now. More recently this idea has been extended to application in distributed systems with frameworks like the Corba Component Model and Enterprise Java Beans. With the advent of Grid computing, high performance applications may be distributed over a wide area network of compute and data servers. Also “peerto-peer” applications exploit vast amounts of parallelism exploiting the resources of thousands of servers. In this talk we look at the problem of building a component technology for scientific applications. The common component architecture project seeks to build a framework that allows software components runing on a massively parallel computers to be linked together to form wide-area, high performance application services that may be accessed from desktop applications. This problem is far from being solved and the talk will describe progress to date and outline some of the difficult problems that remain to be solved. R. Sakellariou et al. (Eds.): Euro-Par 2001, LNCS 2150, pp. 5–5, 2001. c © Springer-Verlag Berlin Heidelberg 2001 Macroand Micro-parallelism in a DBMS Martin Kersten, Stefan Manegold, Peter Boncz, and Niels Nes CWI Kruislaan 413, 1098 SJ, Amsterdam, The Netherlands Abstract. Large memories have become an affordable storage medium Large memories have become an affordable storage medium for databases involving hundreds of Gigabytes on multi-processor systems. In this short note, we review our research on building relational engines to exploit this major shift in hardware perspective. It illustrates that key design issues related to parallelism poses architectural problems at all levels of a system architecture and whose impact is not easily predictable. The sheer size/complexity of a relational DBMS and the sliding requirements of frontier applications are indicative that a substantial research agenda remains wide open.

[1]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[2]  Marco Danelutto,et al.  SkIE: A heterogeneous environment for HPC applications , 1999, Parallel Comput..

[3]  Linyuan Lu,et al.  The diameter of random massive graphs , 2001, SODA '01.

[4]  Philippe Flajolet,et al.  An introduction to the analysis of algorithms , 1995 .

[5]  Mateo Valero,et al.  The effect of code reordering on branch prediction , 2000, Proceedings 2000 International Conference on Parallel Architectures and Compilation Techniques (Cat. No.PR00622).

[6]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[7]  Ulrich Meyer,et al.  Parallel Shortest Path for Arbitrary Graphs , 2000, Euro-Par.

[8]  James E. Smith,et al.  A study of branch prediction strategies , 1981, ISCA '98.

[9]  Philip N. Klein,et al.  A Randomized Parallel Algorithm for Single-Source Shortest Paths , 1997, J. Algorithms.

[10]  Jop F. Sibeyn One-by-One Cleaning for Practical Parallel List Ranking , 2001, Algorithmica.

[11]  Massimo Coppola,et al.  High-performance data mining with skeleton-based structured parallel programming , 2001, Parallel Comput..

[12]  Jason R. C. Patterson,et al.  Accurate static branch prediction by value range propagation , 1995, PLDI '95.

[13]  Yale N. Patt,et al.  The agree predictor: a mechanism for reducing negative branch history interference , 1997, ISCA '97.

[14]  Ulrich Meyer,et al.  Delta-Stepping: A Parallel Single Source Shortest Path Algorithm , 1998, ESA.

[15]  Abhiram G. Ranade,et al.  A simple optimal list ranking algorithm , 1998, Proceedings. Fifth International Conference on High Performance Computing (Cat. No. 98EX238).

[16]  Ravi Kumar,et al.  Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.

[17]  Ulrich Meyer,et al.  Single-source shortest-paths on arbitrary directed graphs in linear average-case time , 2001, SODA '01.

[18]  Yijie Han,et al.  Efficient parallel algorithms for computing all pair shortest paths in directed graphs , 1992, SPAA '92.

[19]  Daniel A. Keim,et al.  Clustering techniques for large data sets—from the past to the future , 1999, KDD '99.

[20]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[21]  T. E. Harris,et al.  The Theory of Branching Processes. , 1963 .