A Software Toolchain for Variability Awareness on Heterogenous Multicore Platforms

被引:0
|
作者
Nittala, Ramakrishna [1 ]
Acquaviva, Andrea [1 ]
Macii, Enrico [1 ]
机构
[1] Politecn Torino, Dept Control & Comp Engn DAUIN, Cso Duca Abruzzi 24, I-10129 Turin, TO, Italy
关键词
SysML modelling; model-based development; model code generation; parallel annotations; variability awareness; runtime customization; TASK ALLOCATION;
D O I
10.1109/TETC.2016.2562598
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Workload allocation in embedded multicore platforms is an increasing challenging issue due to heterogeneity of components and their parallelism. Additionally, the impact of process variations in current and next generation technology nodes is becoming relevant and cannot be compensated at the device or architectural level. Intra-die process variations raising at the core level and platform level makes parallel multicore platforms intrinsically heterogeneous, because the various cores are clocked at different operational frequencies. Power consumption becomes heterogeneous too, both considering dynamic and leakage consumption. In this context, to fully exploit the computational capability of the platform parallelism, variability aware task allocation strategies must be adopted. Despite the consistent research performed to design variability-aware task allocation policies, little effort has been devoted make available to programmers a software toolchain enabling the exploitation of these policies. Such toolchain need to exploit fabrication-level information about core clock speed and power consumption. In this work, we address to present a methodology and the associated toolchain to program in presence of process variability, integrating power and performance variability information in all the steps of the toolchain. To this purpose, the proposed approach is vertically integrated, from high level modelling down to runtime management. Variability information is introduced through a XML configuration file that is exploited by toolchain components to make the appropriate runtime allocation decision. We demonstrate the proposed toolchain using state of art variability-aware task allocation policies on two multicore platforms: i) The MIPS-based GENEPY simulator with 4 and 8 parallel homogeneous cores and ii) The Tegra2-based Zynq platform, where the on-board FPGA has been used to map 10 microblaze slave cores. Experiments show that the proposed toolchain supports the integration of variability awareness in a simple yet effective programming environment.
引用
收藏
页码:95 / 107
页数:13
相关论文
共 50 条
  • [21] Predicting Performance of Applications on Multicore Platforms
    Ranadive, Priti
    Vaidya, Vinay G.
    2016 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2016, : 1778 - 1784
  • [22] Trends in Multicore DSP Platforms Examining architectures, programming models, software tools, emerging applications, and challenges
    Karam, Lina J.
    AlKamal, Ismail
    Gatherer, Alan
    Frantz, Gene A.
    Anderson, David V.
    Evans, Brian L.
    IEEE SIGNAL PROCESSING MAGAZINE, 2009, 26 (06) : 38 - 49
  • [23] HardOps: utilising the software development toolchain for hardware design
    Stirling, Julian
    Bumke, Kaspar
    Collins, Joel
    Dhokia, Vimal
    Bowman, Richard
    INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2022, 35 (12) : 1297 - 1309
  • [24] Modeling and Reusing Robotic Software Architectures: the HyperFlex Toolchain
    Gherardi, Luca
    Brugali, Davide
    2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2014, : 6414 - 6420
  • [25] Multicore Software Technologies A survey
    Kim, Hahn
    Bond, Robert
    IEEE SIGNAL PROCESSING MAGAZINE, 2009, 26 (06) : 80 - 89
  • [26] Optimizing software for multicore processors
    Verplanke, Edwin
    DR DOBBS JOURNAL, 2007, 32 (06): : 44 - +
  • [27] The impact of multicore on math software
    Buttari, Alfredo
    Dongarra, Jack
    Kurzak, Jakub
    Langou, Julien
    Luszczek, Piotr
    Tomov, Stanimire
    APPLIED PARALLEL COMPUTING: STATE OF THE ART IN SCIENTIFIC COMPUTING, 2007, 4699 : 1 - +
  • [28] SOFTWARE STANDARDS FOR THE MULTICORE ERA
    Holt, Jim
    Agarwal, Anant
    Brehmer, Suen
    Domeika, Max
    Griffin, Patrick
    Schirrmeister, Frank
    IEEE MICRO, 2009, 29 (03) : 40 - 50
  • [29] Awareness of variability in awareness
    Neundorfer, MM
    ALZHEIMER DISEASE & ASSOCIATED DISORDERS, 1997, 11 (03): : 121 - 122
  • [30] Building Confidence in Multicore Software
    Sarkar, Vivek
    COMMUNICATIONS OF THE ACM, 2010, 53 (06) : 96 - 96