Model-oriented co-design of homogeneous computing systems | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2019. № 48. DOI: 10.17223/19988605/48/8

Model-oriented co-design of homogeneous computing systems

This article proposes an approach to the construction of a technological platform for designing function-oriented processors (FOP) built on the basis of homogeneous computing environments (OVS). A feature of the proposed approach is the use of co-design - a simultaneous and interconnected design of hardware (HW) and software (SW) FOP components. The paper presents proposals for the implementation of this approach, based on the use of the principle of model-based design, which allows you to make the co-design process iterative and effectively distribute data processing procedures between HW and SW - components of FOP. Based on the consideration of the experience in implementing such solutions, the paper presents the composition of functional subsystems of technological platforms, a diagram of information interaction between these subsystems based on intensive using of web-technologies at all stages of the design and operation of FOP.

Download file

Counter downloads: 141

Keywords

function-oriented processors, homogeneous computing systems, model-oriented design, parallel programming, однородные вычислительные системы, модельно-ориентированный дизайн, параллельное программирование

Authors

Name	Organization	E-mail
Filimonov Alexander Yu.	Ural Federal University	a.filimonov@urfu.ru
Trishin Vasily N.	Institute of Engineering Science, Ural Branch of the Russian Academy of Sciences	trishinvn@yandex.ru

Всего: 2

References

CompTIA. (2015) Sizing Up the Internet of Things. [Online] Available from: https://www.comptia.org/resources/sizing-up-theinternet-of-things (Accessed: 22nd May 2018).

Audin, G. (2015) The Network Impact of Big Data. [Online] Available from: https://www.nojitter.com/post/240170228/thenetwork-impact-of-big-data (Accessed: 22nd May 2018).

OpenFog Consortium Architecture Working Group. (2016) White Paper.OpenFog Architecture Overview. [Online] Available from: https://www.openfogconsortium.org/wp-content/uploads/OpenFog-Architecture- Overview- WP-2-2016.pdf (Accessed: 22nd May 2018).

Chiang, M. & Zhang, T. (2016) Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal. 3(6). pp. 854-864. DOI: 10.1109/JIOT.2016.2584538

Kuhn, A. (2014) Review of Novel Computing Architectures for Neural Applications. [Online] Available from: https://pdfs.semanticscholar.org/fab9/f4e8834900c0f66d10d7d0e8b786915c7271.pdf (Accessed: 22nd May 2018).

Master, P. (n.d.) A new computing architecture for Big Data and AI applications. [Online] Available from: http:// impactvc.com/anew-computing-architecture-for-big-data-and-ai-applications (Accessed: 22nd May 2018).

Jouppi, N.J. et al. (2017) In - Datacenter Performance Analysis of a Tensor Processing Unit. Proc. of the 44th Annual International Symposium on Computer Architecture, ISCA. pp. 1-12. DOI: 10.1145/3140659.3080246

Lukin, N.A, Filimonov, A.Yu. & Trishin, V.N. (2017) Cloud computing environment of homogeneous computing systems. In: Dubrov, D.V. Programming languages and compilers - 2017. Rostov-on-Don: ∣s.n.∣. pp. 181-184. (In Russian).

Lukin, N.A. (2014) Functionally-oriented processors are key components of built-in supercomputers for real-time systems. Izvestiya Yuzhnogo federal'nogo universiteta. Tekhnicheskie nauki - Izvestiya SFEDU. Engineering Sciences. 12. рр. 52-64. (In Russian).

Voevodin, V.V. (1986) Mathematical Models and Methods in Parallel Processes. Moscow: Nauka.

Lukin, N.A. (2010) [Functionally oriented processors with a homogeneous architecture for the implementation of algorithms of onboard control systems]. Parallel Computing and Control Problems. Proc. of the 5th International Conference. Moscow. pp. 1177-1184. (In Russian).

Atlam, H.F., Walters, R.J. & Wills, G.B. (2018) Fog Computing and the Internet of Things: A Review. Big Data and Cognitive Computing. 2(2). DOI: 10.3390/bdcc2020010.

Gamatie, A., Le Beux, S., Piel, E., Ben Atitallah, R., Etien, A., Marquet, P. & Dekeyser, J.-L. (2011) A model-driven design framework for massively parallel embedded systems. ACM Trans. Embed. Comput.Syst. 10(4). Article 3936. DOI: 10.1145/2043662.2043663

Adler, R., Schaefer, I., Schuele, T. & Vecchie, E. (2007) From Model-Based Design to Formal Verification of Adaptive Embedded Systems. ICFEM 2007, LNCS 4789. pp. 76-95. DOI: 10.1007/978-3-540-76650-6_6

Rovers, K.C., van de Burgwal, M.D., Kuper, J., Kokkeler, A.B.J. & Smit, G.J.M. (2011) Multi-domain transformational design flow for embedded systems. International Conference on Embedded Computer Systems (SAMOS 2011). pp. 93-101. DOI: 10.1109/SAMOS.2011.6045449

Lukin, N.A. & Filimonov, A.Y. (2017) Software technologies for homogeneous computing environment. Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitel'naya tekhnika i informatika - Tomsk State University Journal of Control and Computer Science. 40. pp. 61-70. (In Russian). DOI: 10.17223/19988605/40/7

Kovartsev, A.N. & Zhidchenko, V.V. (2011) Methods and means of visual parallel programming. Automation ofprogramming. Samara: Samara State Aerospace University.

Devin, F., Boulet, P., Dekeyser, J-L. & Marquet, P. (2002) GASPARD a visual parallel programming environment. Parallel Computing in Electrical Engineering, PARELEC '02. Proc. International Conference. DOI: 10.1109/PCEE.2002.1115225

Object Management Group, Inc. (n.d.) UML Profilefor MARTETM: Modeling and Analysis of Real-time Embedded Systems. [Online] Available from: http ://www.omg.org/spec/MARTE/1.1 (Accessed: 22nd May 2018).

Boulet, P. (2008) Formal Semantics of Array-OL, a Domain Specific Language for Intensive Multidimensional Signal Processing. Research Report RR-6467, INRIA.

Johnston, W.M., Hanna, P. & Millar, R.J. (2004) Advances in dataflow programming languages. ACM Computing Surveys (CSUR). 36(1). pp. 1-34. DOI: 10.1145/1013208.1013209

Bohm, A.P.W., Hammes, J., Draper, B.A., Chawathe, M., Ross, C., Rinker, R. & Najjar, W. (2002) Mapping a Single Assignment Programming Language to Reconfigurable Systems. Supercomputing. 21. pp. 117-130. DOI: 10.1023/A:1013623303037

Hammarlund, P. & Lisper, B. (1993) Data Parallel Programming: A Survey and a Proposal for a New Model. Technical Report 93/8-SE, Department of Teleinformatics Royal Institute of Technology, Sweden.

Wolfram, S. (2017) An Elementary Introduction to the Wolfram Language. 2nd ed. Wolfram Media.

Nemanich, B., Cooke, D. & Rushton, N. (2010) Sequencel: transparency and multicore parallelisms. DAMP '10 Proceedings of the 5th ACM SIGPLAN workshop on Declarative Aspects of Multicore Programming.

Cooke, D., Rushton, N., Nemanich, B. & Watson, R.G. (2008) Normalize, transpose, and distribute: An automatic approach for handling nonscalars. ACM Transactions on Programming Languages and Systems. 30(2). DOI: 10.1145/1330017.1330020

Project Jupyter. (n.d.) Jupyter Notebook Documentation Release 5.1. [Online] Available from: https://media.readthedocs.org/pdf/ jupyter-notebook/latest/jupyternotebook.pdf. (Accessed: 22nd May 2018).

The IPython Development Team. (n.d.) IPython Documentation Release 6.2.0. [Online] Available from: http://ipython.readthedocs.io/ en/latest/whatsnew/development.html. (Accessed: 22nd May 2018).

Xu, H., Wang, C., Liu, W. & Chen, H. (2012) NETCONF-based Integrated Management for Internet of Things using RESTful Web Services. International Journal of Future Generation Communication and Networking. 5(3). pp. 73-82.

Scheffler, T. & Bonness, O. (2017) Manage Resource-constrained IoT Devices Through Dynamically Generated and Deployed YANG Models. Proc. of the Applied Networking Research Workshop. 1. pp. 42-47. DOI: 10.1145/3106328.3106331