The Impact of Resource Sharing Control on the Design of Multicore Processors

被引:0
|
作者
Liu, Chen [1 ]
Gaudiot, Jean-Luc [2 ]
机构
[1] Florida Int Univ, Dept Elect & Comp Engn, 10555 W Flagler St, Miami, FL 33174 USA
[2] Univ Calif Irvine, Dept Elect Engn & Comp Sci, Irvine, CA 92697 USA
关键词
PERFORMANCE;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
One major obstacle faced by designers when entering the multicore era is how to harness the massive computing power which these cores provide. Since Instructional-Level Parallelism (ILP) is inherently limited, one single thread is not capable of efficiently utilizing the resource of a single core. Hence, Simultaneous MultiThreading (SMT) microarchitecture can be introduced in an effort to achieve improved system resource utilization and a correspondingly higher instruction throughput through the exploitation of Thread-Level Parallelism (TLP) as well as ILP. However, when multiple threads execute concurrently in a single core, they automatically compete for system resources. Our research shows that, without, control over the number of entries each thread can occupy in system resources like instruction fetch queue and/or reorder buffer, a scenario called "mutual-hindrance" execution takes place. Conversely introducing active resource sharing control mechanisms causes the opposite situation ("mutual-benefit" execution), with a possible significant performance improvement and lower cache miss frequency. This demonstrates that active resource sharing control is essential for future multicore multithreading microprocessor design.
引用
收藏
页码:315 / +
页数:3
相关论文
共 50 条
  • [1] Resource Usage Templates and Signatures for COTS Multicore Processors
    Fernandez, Gabriel
    Jalle, Javier
    Abella, Jaume
    Quinones, Eduardo
    Vardanega, Tullio
    Cazorla, Francisco J.
    [J]. 2015 52ND ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC), 2015,
  • [2] Parallel Design of Control Systems Utilizing Dead Time for Embedded Multicore Processors
    Suzuki, Yuta
    Sata, Kota
    Kako, Junichi
    Yamaguchi, Kohei
    Arakawa, Fumio
    Edahiro, Masato
    [J]. 2014 IEEE COOL CHIPS XVII, 2014,
  • [3] Efficient ASIP Design for Configurable Processors with Fine-Grained Resource Sharing
    Dinh, Quang
    Chen, Deming
    Wong, Martin D. F.
    [J]. FPGA 2008: SIXTEENTH ACM/SIGDA INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE GATE ARRAYS, 2008, : 99 - 106
  • [4] Addressing Shared Resource Contention in Multicore Processors via Scheduling
    Zhuravlev, Sergey
    Blagodurov, Sergey
    Fedorova, Alexandra
    [J]. ACM SIGPLAN NOTICES, 2010, 45 (03) : 129 - 141
  • [5] Addressing Shared Resource Contention in Multicore Processors via Scheduling
    Zhuravlev, Sergey
    Blagodurov, Sergey
    Fedorova, Alexandra
    [J]. ASPLOS XV: FIFTEENTH INTERNATIONAL CONFERENCE ON ARCHITECTURAL SUPPORT FOR PROGRAMMING LANGUAGES AND OPERATING SYSTEMS, 2010, : 129 - 141
  • [6] Resource-conscious Scheduling for Energy Efficiency on Multicore Processors
    Merkel, Andreas
    Stoess, Jan
    Bellosa, Frank
    [J]. EUROSYS'10: PROCEEDINGS OF THE EUROSYS 2010 CONFERENCE, 2010, : 153 - 166
  • [7] Parallel Design of Feedback Control Systems Utilizing Dead Time for Embedded Multicore Processors
    Suzuki, Yuta
    Sata, Kota
    Kako, Jun'ichi
    Yamaguchi, Kohei
    Arakawa, Fumio
    Edahiro, Masato
    [J]. IEICE TRANSACTIONS ON ELECTRONICS, 2016, E99C (04): : 491 - 502
  • [8] Design of MOESI protocol for multicore processors based on FPGA
    Ibrahim, Raed K.
    Jumma, Laith F.
    Amory, Ibrahim A.
    AI-Hilali, Aqeel
    [J]. INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 2021, 12 : 1229 - 1242
  • [9] The impact of dynamically heterogeneous multicore processors on thread scheduling
    Bower, Fred A.
    Sorin, Daniel J.
    Cox, Landon P.
    [J]. IEEE MICRO, 2008, 28 (03) : 17 - 25
  • [10] Exploring the Design Space of Specialized Multicore Neural Processors
    Taha, Tarek M.
    Hasan, Raqibul
    Yakopcic, Chris
    McLean, Mark R.
    [J]. 2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2013,