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Multicore, multithreaded processors are rapidly becoming the platform of choice for designing high-throughput request processing applications. We refer to this class of modern parallel architectures as multi-[star] systems. In this dissertation, we describe the design and implementation of Lagniappe, a programming environment that simplifies the development of portable, high-throughput request-processing applications on multi-[star] systems. Lagniappe makes the following four key contributions: First, Lagniappe defines and uses a unique hybrid programming model for this domain that separates the concerns of writing applications for uni-processor, single-threaded execution platforms (single-[star]systems) from the concerns of writing applications necessary to efficiently execute on a multi-[star] system. We provide separate tools to the programmer to address each set of concerns. Second, we present meta-models of applications and multi-[star] systems that identify the necessary entities for reasoning about the application domain and multi-[star] platforms. Third, we design and implement a platform-independent mechanism called the load-distributing channel that factors out the key functionality required for moving an application from a single-[star] architecture to a multi-[star] one. Finally, we implement a platform-independent adaptation framework that defines custom adaptation policies from application and system characteristics to change resource allocations with changes in workload. Furthermore, applications written in the Lagniappe programming environment are portable; we separate the concerns of application programming from system programming in the programming model. We implement Lagniappe on a cluster of servers each with multiple multicore processors. We demonstrate the effectiveness of Lagniappe by implementing several stateful request-processing applications and showing their performance on our multi-[star] system. / text

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Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2010. / "Graduate Program in Computer Science." Includes bibliographical references (p. 93-97).

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references. Also available from UMI.

Thesis (M.S.E.C.E.)--University of Delaware, 2007. / Principal faculty advisor: Dennis W. Prather, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

The new generations of multi-core chipset architectures achieve unprecedented levels of computational power while respecting physical and economical constraints. The cost of this power is bewildering program complexity. Atmospheric modeling is a grand-challenge problem that could make good use of these architectures if they were more accessible to the average programmer. To that end, software tools and programming methodologies that greatly simplify the acceleration of atmospheric modeling and simulation with emerging multi-core technologies are developed. A general model is developed to simulate atmospheric chemical transport and atmospheric chemical kinetics. The Cell Broadband Engine Architecture (CBEA), General Purpose Graphics Processing Units (GPGPUs), and homogeneous multi-core processors (e.g. Intel Quad-core Xeon) are introduced. These architectures are used in case studies of transport modeling and kinetics modeling and demonstrate per-kernel speedups as high as 40x. A general analysis and code generation tool for chemical kinetics called "KPPA" is developed. KPPA generates highly tuned C, Fortran, or Matlab code that uses every layer of heterogeneous parallelism in the CBEA, GPGPU, and homogeneous multi-core architectures. A scalable method for simulating chemical transport is also developed. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) is accelerated with these methods with good results: real forecasts of air quality are generated for the Eastern United States 65% faster than the state-of-the-art models. / Ph. D.

It was observed, that the RT-PICLS code ran by FWKT on the hypnos cluster was producing an unusual amount of system load, according to Ganglia metrics. Since this may point to an IO-problem in the code, this code was analyzed more closely.

Systems biology is an emerging field of study that seeks to provide systems-level understanding of biological systems through the integration of high-throughput biological data into predictive computational models. The integrative nature of this field is in sharp contrast as compared to the Reductionist methods that have been employed since the advent of molecular biology. Systems biology investigates not only the individual components of the biological system, such as metabolic pathways, organelles, and signaling cascades, but also considers the relationships and interactions between the components in the hope that an understandable model of the entire system can eventually be developed. This field of study is being hailed by experts as a potential vital technology in revolutionizing the pharmaceutical development process in the post-genomic era. This work not only provides a systems biology investigation into principles governing de novo sphingolipid metabolism but also the various computational obstacles that are present in converting high-throughput data into an insightful model.