Search for dissertations about: "Single Thread Performance"

Showing result 1 - 5 of 9 swedish dissertations containing the words Single Thread Performance.

  1. 1. Techniques to Reduce Thread-Level Speculation Overhead

    Author : Fredrik Warg; [2006]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Computer architecture; multithreaded processors; performance evaluation; speculation overhead; thread-level speculation; chip multiprocessors;

    Abstract : The traditional single-core processors are being replaced by chip multiprocessors (CMPs) where several processor cores are integrated on a single chip. While this is beneficial for multithreaded applications and multiprogrammed workloads, CMPs do not provide performance improvements for single-threaded applications. READ MORE

  2. 2. Leveraging Existing Microarchitectural Structures to Improve First-Level Caching Efficiency

    University dissertation from Uppsala : Acta Universitatis Upsaliensis

    Author : Ricardo Alves; Uppsala universitet.; [2019]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Energy Efficient Caching; Memory Architecture; Single Thread Performance; First-Level Caching; Out-of-Order Pipelines; Instruction Scheduling; Filter-Cache; Way-Prediction; Value-Prediction; Register-Sharing.;

    Abstract : Low-latency data access is essential for performance. To achieve this, processors use fast first-level caches combined with out-of-order execution, to decrease and hide memory access latency respectively. READ MORE

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

    University dissertation from Stockholm : KTH Royal Institute of Technology

    Author : Ahsan Javed Awan; KTH.; [2017]
    Keywords : 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;

    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

  4. 4. Power and Performance Optimization for Network-on-Chip based Many-Core Processors

    University dissertation from KTH Royal Institute of Technology

    Author : Yuan Yao; KTH.; [2019]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Many-Core Processor; Network-on-Chip; Performance; Power Management; DVFS; Shared Memory Synchronization; Hardware Software Co-Design; Cache Coherency; Performance Isolation; Informations- och kommunikationsteknik; Information and Communication Technology;

    Abstract : Network-on-Chip (NoC) is emerging as a critical shared architecture for CMPs (Chip Multi-/Many-Core Processors) running parallel and concurrent applications. As the core count scales up and the transistor size shrinks, how to optimize power and performance for NoC open new research challenges. READ MORE

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

    University dissertation from KTH Royal Institute of Technology

    Author : Ahsan Javed Awan; KTH.; [2016]
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Informations- och kommunikationsteknik; Information and Communication Technology;

    Abstract : The sheer increase in 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 at understanding the performance of in-memory data analytics with Spark on modern scale-up servers. READ MORE