Browsing by Subject "Computer architecture."

Now showing 1 - 4 of 4

Open Access
Adaptive source routing and route generation for multicomputers
(1995) Aydoğan, Yücel
Scalable multicomputers are based upon interconnection networks that typically provide multiple communication routes between any given pair of processor nodes. In such networks, the selection of the routes is an important problem because of its impact on the communication performance. We propose the adaptive source routing (ASR) scheme which combines adaptive routing and source routing into one which has the advantages of both schemes. In ASR, the degree of adaptivity of each packet is determined at the source processor. Every packet can be routed in a fully adaptive or partially adaptive or nonadaptive manner, all within the same network at the same time. The ASR scheme permits any network topology to be used provided that deadlock constraints are satisfied. We evaluate and compare performance of the adaptive source routing and non-adaptive randomized routing by simulations. Also we propose an algorithm to generate adaptive routes for all pairs of processors in any multistage interconnection network. Adaptive routes are stored in a route table in each processor’s memory and provide high bandwidth and reliable interprocessor communication. We evaluate the performance of the algorithm on IBM SP2 networks in terms of obtained bandwidth, time to fill in the route tables, and efficiency exploited by the parallel execution of the algorithm.
Open Access
Analysis and design of scalable software as a service architecture
(2015) Özcan, Onur
Different from traditional enterprise applications that rely on the infrastructure and services provided and controlled within an enterprise, cloud computing is based on services that are hosted on providers over the Internet. Hereby, services are fully managed by the provider, whereas consumers can acquire the required amount of services on demand, use applications without installation and access their personal files through any computer with internet access. Recently, a growing interest in cloud computing can be observed thanks to the significant developments in virtualization and distributed computing, as well as improved access to high-speed Internet and the need for economical optimization of resources. An important category of cloud computing is the software as a service domain in which software applications are provided over the cloud. In general when describing SaaS, no specific application architecture is prescribed but rather the general components and structure is defined. Based on the provided reference SaaS architecture different application SaaS architectures can be derived each of which will typically perform differently with respect to different quality factors. An important quality factor in designing SaaS architectures is scalability. Scalability is the ability of a system to handle a growing amount of work in a capable manner or its ability to be enlarged to accommodate that growth. In this thesis we provide a systematic modeling and design approach for designing scalable SaaS architectures. To identify the aspects that impact the scalability of SaaS based systems we have conducted a systematic literature review in which we have identified and analyzed the relevant primary studies that discuss scalability of SaaS systems. Our study has yielded the aspects that need to be considered when designing scalable systems. Our research has continued in two subsequent directions. Firstly, we have defined a UML profile for supporting the modeling of scalable SaaS architectures. The profile has been defined in accordance with the existing practices on defining and documenting profiles. Secondly, we provide the socalled architecture design perspective for designing scalable SaaS systems. Architectural Perspectives are a collection of activities, tactics and guidelines to modify a set of existing views, to document and analyze quality properties. Architectural perspectives as such are basically guidelines that work on multiple views together. So far architecture perspectives have been defined for several quality factors such as for performance, reuse and security. However, an architecture perspective dedicated for designing scalable SaaS systems has not been defined explicitly. The architecture perspective that we have defined considers the scalability aspects derived from the systematic literature review as well as the architectural design tactics that represent important proved design rules and practices. Further, the architecture perspective adopts the UML profile for scalability that we have defined. The scalability perspective is illustrated for the design of a SaaS architecture for a real industrial case study.
Open Access
Increasing data reuse in parallel sparse matrix-vector and matrix-transpose-vector multiply on shared-memory architectures
(2014) Karsavuran, Mustafa Ozan
Sparse matrix-vector and matrix-transpose-vector multiplications (Sparse AAT x) are the kernel operations used in iterative solvers. Sparsity pattern of the input matrix A, as well as its transpose, remains the same throughout the iterations. CPU cache could not be used properly during these Sparse AAT x operations due to irregular sparsity pattern of the matrix. We propose two parallelization strategies for Sparse AAT x. Our methods partition A matrix in order to exploit cache locality for matrix nonzeros and vector entries. We conduct experiments on the recently-released Intel R Xeon PhiTM coprocessor involving large variety of sparse matrices. Experimental results show that proposed methods achieve higher performance improvement than the state-of-the-art methods in the literature.
Open Access
Pipelined fission for stream programs with dynamic selectivity and partitioned state
(2014-12) Özsema, Habibe Güldamla
There is an ever increasing rate of digital information available in the form of online data streams. In many application domains, high throughput processing of such data is a critical requirement for keeping up with the soaring input rates. Data stream processing is a computational paradigm that aims at addressing this challenge by processing data streams in an on-the-fly manner. In this thesis, we study the problem of automatically parallelizing data stream processing applications to improve throughput. The parallelization is automatic in the sense that stream programs are written sequentially by the application developers and are parallelized by the system. We adopt the asynchronous data flow model for our work, where operators often have dynamic selectivity and are stateful. We solve the problem of pipelined fission, in which the original sequential program is parallelized by taking advantage of both pipeline and data parallelism at the same time. Our solution supports partitioned stateful data parallelism with dynamic selectivity and is designed for shared-memory multi-core machines. We first develop a cost-based formulation to express pipelined fission as an optimization problem. The bruteforce solution of this problem takes a very long time for moderately sized stream programs. Accordingly, we develop a heuristic algorithm that can quickly, but approximately, solve this problem. We provide an extensive evaluation studying the performance of our solution, including simulations and experiments with an industrial-strength Data Stream Processing Systems (DSPS). Our results show good scalability for applications that contain sufficient parallelism, closeness to optimal performance for the algorithm.