Keynote: HPCC with Grids and Clouds

Summary form only given. The paper discuss the impact of clouds and grid technology on HPCC using examples from a variety of fields especially the life sciences. It covers the impact of the growing importance of data analysis and note that it is more suitable for these modern architectures than the large simulations (particle dynamics and partial differential equation solution) that are mainstream use of large scale "massively parallel" supercomputers. The importance of grids is seen in the support of distributed data collection and archiving while clouds should replace grids for the large scale analysis of the data. It discusses the structure of applications that will run on current clouds and use either the basic "ondemand" computing paradigm or higher level frameworks based on MapReduce and its extensions. Current MapReduce implementations run well on algorithms that are a "Map" followed by a "Reduce" but perform poorly on algorithms that iterate over many such phases. Several important algorithms including parallel linear algebra fall into latter class. One can define MapReduce extensions to accommodate iterative map and reduce but these have less fault tolerance than basic MapReduce. Both clouds and exascale computing suggest research into a new generation of run times that lie between MapReduce and MPI and trade-off performance, fault-tolerance and asynchronicity. It concludes with a description of FutureGrid - a TeraGrid system for prototyping new middleware and applications.