Search for dissertations about: "NUMA"
Showing result 1 - 5 of 20 swedish dissertations containing the word NUMA.
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1. Group Planning among L2 Learners of Italian: A Conversation Analytic Perspective
Abstract : In line with the call for a process-oriented and ecologically sound approach to planning in SLA (Ellis, 2005), and with the behavioral approach adopted in other fields (Murphy, 2004, 2005; Suchman, 1987, 2007), the present work applies Conversation Analysis to the study of group planning. The participants are four groups of adult learners of Italian as a foreign language, engaged in the preparation of a classroom presentation in their L2. READ MORE
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2. Swedish as multiparty work : Tailoring talk in a second language classroom
Abstract : This dissertation examines classroom conversations involving refugee and immigrant youth in a second language (L2) introduction program, exploring how L2 Swedish emerges as a multiparty accomplishment by both the teacher and the students. Drawing on forty hours of video-recorded Swedish L2 classroom conversations, as well as on observations and informal interviews, it focuses on talk as a form of social action. READ MORE
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3. Multithreaded PDE Solvers on Non-Uniform Memory Architectures
Abstract : A trend in parallel computer architecture is that systems with a large shared memory are becoming more and more popular. A shared memory system can be either a uniform memory architecture (UMA) or a cache coherent non-uniform memory architecture (cc-NUMA). READ MORE
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4. On Composability, Efficient Design and Memory Reclamation of Lock-free Data Structures
Abstract : The transition to multicore processors has brought synchronization, a fundamental challenge in computer science, into focus. In looking for solutions to the problem, interest has developed in the lock-free approach, which has been proven to achieve several advantages over the traditional mutual exclusion approach. READ MORE
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5. Performance Characterization and Optimization of In-Memory Data Analytics on a Scale-up Server
Abstract : The sheer increase in the volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted to understanding the performance of in-memory data analytics with Spark on modern scale-up servers. READ MORE