Progress Towards Petascale Applications in Biology: Status in 2006

Petascale computing is currently a common topic of discussion in the high performance computing community. Biological applications, particularly protein folding, are often given as examples of the need for petascale computing. There are at present biological applications that scale to execution rates of approximately 55 teraflops on a special-purpose supercomputer and 2.2 teraflops on a general-purpose supercomputer. In comparison, Qbox, a molecular dynamics code used to model metals, has an achieved performance of 207.3 teraflops. It may be useful to increase the extent to which operation rates and total calculations are reported in discussion of biological applications, and use total operations (integer and floating point combined) rather than (or in addition to) floating point operations as the unit of measure. Increased reporting of such metrics will enable better tracking of progress as the research community strives for the insights that will be enabled by petascale computing.This research was supported in part by the Indiana Genomics Initiative and the Indiana Metabolomics and Cytomics Initiative. The Indiana Genomics Initiative of Indiana University and the Indiana Metabolomics and Cytomics Initiative of Indiana University are supported in part by Lilly Endowment, Inc. The authors also wish to thank IBM, Inc. for support via Shared University Research Grants and partnerships via IU’s relationship as an IBM Life Sciences Institute of Innovation. Indiana University also thanks the TeraGrid partners; IU’s participation in the TeraGrid is funded by National Science Foundation grant numbers 0338618, 0504075, and 0451237. The early development of this paper was supported by a Fulbright Senior Scholars award from the Council for International Exchange of Scholars (CIES) and the United States Department of State to Dr. Craig A. Stewart; Matthias Mueller and the Technische Universität Dresden were hosts. Many reviewers contributed to the improvement of the ideas expressed in this paper and are gratefully appreciated; Thom Dunning, Robert Germain, Chris Mueller, Jim Phillips, Richard Repasky, Ralph Roskies, and Allan Snavely are thanked particularly for their insights

Optical Technologies and Control Methods for Scalable Data Centre Networks

Attributing to the increasing adoption of cloud services, video services and associated machine learning applications, the traffic demand inside data centers is increasing exponentially, which necessitates an innovated networking infrastructure with high scalability and cost-efficiency. As a promising candidate to provide high capacity, low latency, cost-effective and scalable interconnections, optical technologies have been introduced to data center networks (DCNs) for approximately a decade. To further improve the DCN performance to meet the increasing traffic demand by using photonic technologies, two current trends are a)increasing the bandwidth density of the transmission links and b) maximizing IT and network resources utilization through disaggregated topologies and architectures. Therefore, this PhD thesis focuses on introducing and applying advanced and efficient technologies in these two fields to DCNs to improve their performance. On the one hand, at the link level, since the traditional single-mode fiber (SMF) solutions based on wavelength division multiplexing (WDM) over C+L band may fall short in satisfying the capacity, front panel density, power consumption, and cost requirements of high-performance DCNs, a space division multiplexing (SDM) based DCN using homogeneous multi-core fibers (MCFs) is proposed.With the exploited bi-directional model and proposed spectrum allocation algorithms, the proposed DCN shows great benefits over the SMF solution in terms of network capacity and spatial efficiency. In the meanwhile, it is found that the inter-core crosstalk (IC-XT) between the adjacent cores inside the MCF is dynamic rather than static, therefore, the behaviour of the IC-XT is experimentally investigated under different transmission conditions. On the other hand, an optically disaggregated DCN is developed and to ensure the performance of it, different architectures, topologies, resource routing and allocation algorithms are proposed and compared. Compared to the traditional server-based DCN, the resource utilization, scalability and the cost-efficiency are significantly improved