Abstract
The article presents a method for transforming algorithm’s information graph using adjacency lists. Algorithm’s information graph always has a large number of vertices. For most algorithms, this graph contains more than 100 vertices. Manual analysis of this graph for the presence of internal parallelism is very difficult. The proposed method does not use conventional adjacency matrix for storing information about the connections between vertices and the adjacency lists. Adjacency lists allow to store information about the graph in a compressed form. As a result, the researcher gets a schedule of the algorithm on a computer, allowing parallel execution. The presented method can be successfully applied to queries in databases, to the distribution of tasks between nodes of a wireless network, to solving problems with large volumes of data in the field of the Internet of things.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, B., Tang, L., Xie, J., Wang, X., Song, A.: Parallel numerical simulations of three-dimensional electromagnetic radiation with MPI-CUDA paradigms. Math. Probl. Eng. 2015, 9 pages (2015). Article ID 823426
Qin, J., Lu, Y., Zhong, Y.: Parallel algorithm for wireless data compression and encryption. J. Sensors 2017, 11 pages (2017). Article ID 4209397
Gong, C., Bao, W., Tang, G., Jiang, Y., Liu, J.: A parallel algorithm for the two-dimensional time fractional diffusion equation with implicit difference method. Sci. World J. 2014, 8 pages (2014). Article ID 219580
Ma, X., Liu, S., Xiao, M., Xie, G.: Parallel algorithm with parameters based on alternating direction for solving banded linear systems. Math. Probl. Eng. 2014, 8 pages (2014). Article ID 752651
Hou, J., Lv, Q., Xiao, M.: A parallel preconditioned modified conjugate gradient method for large sylvester matrix equation. Math. Probl. Eng. 2014, 7 pages (2014). Article ID 598716
Yu, D.-X., Yang, Z.-S., Yu, Y., Jiang, X.-R.: Research on large-scale road network partition and route search method combined with traveler preferences. Math. Probl. Eng. 2013, 8 pages (2013). Article ID 950876
Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Processings AFIPS Spring Joint Computer Conference, Reston, pp. 483–485. AFIPS Press, VA (1967)
Ware, W.: The ultimate computer. IEEE Spectrum 9, 84–91 (1972)
Grama, A., Gupta, A., Karypis, G., Kumar, V.: Introduction to Parallel Computing, Second Edition. Addison Wesley, Reading (2003)
Gergel, V.P., Strongin, R.G.: Parallel Computing for Multiprocessor Computers. NGU Publ, Nizhnij Novgorod (2003). (in Russian)
Quinn, M.J.: Parallel Programming in C with MPI and OpenMP, 1st edn. McGraw-Hill Education, New York (2003)
Wittwer, T.: An Introduction to Parallel Programming, VSSD uitgeverij (2006)
Tiwari, A., Tabatabaee, V., Hollingsworth, J.K.: Tuning parallel applications in parallel. Parallel Comput. 35(8–9), 475–492 (2009)
Mubarak, M., Seol, S., Qiukai, L., Shephard, M.S.: A parallel ghosting algorithm for the flexible distributed mesh database. Sci. Program. 21(1–2), 17–42 (2013)
Kruatrachue, B., Lewis, T.: Grain size determination for parallel processing. IEEE Softw. 5(1), 23–32 (1988)
Lim, A.W., Lam, M.S.: Maximizing parallelism and minimizing synchronization with affine partitions. Parallel Comput. 24(3–4), 445–475 (1998)
Meuer, H., Strohmaier, E., Dongarra, J., Simon, H.: Top500 supercomputing sites (2015)
Yang, T., Gerasoulis, A.: DSC: scheduling parallel tasks on an unbounded number of processors. IEEE Trans. Parallel Distrib. Syst. 5(9), 951–967 (1994)
Darbha, S., Agrawal, D.P.: Optimal scheduling algorithm for distributed memory machines. IEEE Trans. Parallel Distrib. Syst. 9(1), 87–95 (1998)
Liu, C.L., Layland, J.W.: Scheduling algorithms for multiprogramming in hard real-time environment. J. ACM 20(1), 46–61 (1973)
Marte, B.: Preemptive scheduling with release times, deadlines and due times. J. ACM 29(3), 812–829 (1982)
Burns, A.: Scheduling hard real-time systems: a review. Softw. Eng. J. 6(3), 116–128 (1991)
Stankovic, J.A.: Implications of classical scheduling results for real-time systems. IEEE Computer Society Press (1995)
Darbha, S., Agrawal, D.P.: A task duplication based scalable scheduling algorithm for distributed memory systems. IEEE Trans. Parallel Distrib. Syst. 46(1), 15–27 (1997)
Tzen, T.H., Ni, L.M.: Trapezoid self-scheduling: a practical scheduling scheme for parallel compilers. IEEE Trans. Parallel Distrib. Syst. 4, 87–98 (1993)
Sinnen, O., Sousa, L.A.: Communication contention in task scheduling. IEEE Trans. Parallel Distrib. Syst. 16, 503–515 (2005)
Wu, A.S., Yu, H., Jin, S., Lin, K.-C., Schiavone, G.: An incremental genetic algorithm approach to multiprocessor scheduling. IEEE Trans. Parallel Distrib. Syst. 15(9), 824–834 (2004)
Kupriyanov, M.S., Shichkina, Y.A.: Applying the list method to the transformation of parallel algorithms into account temporal characteristics of operations. In: Proceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016, pp. 292–295. https://doi.org/10.1109/scm.2016.7519759, ISBN 978-146738919-8. 7519759
Shichkina, Y., Kupriyanov, M., Al-Mardi, M.: Optimization algorithm for an information graph for an amount of communications. In: Galinina, O., Balandin, S., Koucheryavy, Y. (eds.) NEW2AN/ruSMART -2016. LNCS, vol. 9870, pp. 50–62. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46301-8_5
Shichkina, Y., Degtyarev, A., Gushchanskiy, D., Iakushkin, O.: Application of optimization of parallel algorithms to queries in relational databases. In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9787, pp. 366–378. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42108-7_28
Acknowledgments
The paper has been prepared within the scope of the state project “Initiative scientific project” of the main part of the state plan of the Ministry of Education and Science of Russian Federation (task № 2.6553.2017/8.9 BCH Basic Part).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Shichkina, Y., Kupriyanov, M. (2018). Creating a Schedule for Parallel Execution of Tasks Based on the Adjacency Lists. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2018 2018. Lecture Notes in Computer Science(), vol 11118. Springer, Cham. https://doi.org/10.1007/978-3-030-01168-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-01168-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01167-3
Online ISBN: 978-3-030-01168-0
eBook Packages: Computer ScienceComputer Science (R0)