Sökning: "NUMA"

Visar resultat 1 - 5 av 20 avhandlingar innehållade ordet NUMA.

1. 1. Group Planning among L2 Learners of Italian: A Conversation Analytic Perspective

   Författare :Silvia Kunitz; Numa Markee; Andrea Golato; Makoto Hayashi; Diane Musumeci; USA University of Illinois at Urbana-Champaign; []
   Nyckelord :HUMANIORA; HUMANITIES; SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; conversation analysis; planning; second language studies; group work; language alternation; språkdidaktik; Language Education;

   Sammanfattning : 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. LÄS MER

3. 2. Swedish as multiparty work : Tailoring talk in a second language classroom

   Författare :Anna Åhlund; Karin Aronsson; Rickard Jonsson; Numa Markee; Stockholms universitet; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; SSL education; language socialization; conversation analysis; identity work; participation; participation frameworks; classroom community; performance; verbal improvisations; alignment; repair work; multiparty talk; peer corrections; tailoring talk; Child and Youth Science; barn- och ungdomsvetenskap;

   Sammanfattning : 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. LÄS MER

4. 3. Multithreaded PDE Solvers on Non-Uniform Memory Architectures

   Författare :Markus Nordén; Michael Thuné; Sverker Holmgren; Xing Cai; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; PDE solver; high-performance; NUMA; UMA; OpenMP; MPI; data migration; data replication; thread scheduling; data affinity; Beräkningsvetenskap; Scientific Computing;

   Sammanfattning : 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). LÄS MER

5. 4. On Composability, Efficient Design and Memory Reclamation of Lock-free Data Structures

   Författare :Dang Nhan Nguyen; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Mark-Compact; Garbage Collection; Composability; Multicore Programming; Non-blocking; Concurrent Data Structure; Synchronization; Lock-free; Mark-Split; NUMA; Parallel Garbage Collection;

   Sammanfattning : 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. LÄS MER

7. 5. Performance Characterization and Optimization of In-Memory Data Analytics on a Scale-up Server

   Författare :Ahsan Javed Awan; Eduard Ayguade; Mats Brorsson; Vladimir Vlassov; Lieven Eeckhout; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Workload Characterization; Big Data Analytics; Multicore Performance; Apache Spark; Near Data Processing; NUMA; Hyperthreading; Prefetchers; Coherently attached accelerators; Informations- och kommunikationsteknik; Information and Communication Technology;

   Sammanfattning : 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. LÄS MER