International Journal of Wireless and Microwave Technologies(IJWMT)
ISSN: 2076-1449 (Print), ISSN: 2076-9539 (Online)
Published By: MECS Press
IJWMT Vol.12, No.4, Aug. 2022
A Theoretical Graph based Framework for Parameter Tuning of Multi-core Systems
Full Text (PDF, 527KB), PP.15-25
Multi-core systems are outperforming nowadays. Therefore, various computing paradigms are intrinsically incorporated in the multicore domain to exploit its potential and solve well known computing problems. Parameter tuning is a well-known computing problem in the field of Multicore domain. Addressing the said hurdle would leverage in the performance enhancement of Multicore systems. Various efforts in this direction have been made through the conventional parameter tuning algorithms in a limited scope; however, the problem is yet not addressed completely. In this research article, we have addressed parameter tuning problem by employing applications of graph theory, especially Dijkstra shortest path algorithm to address the said issue. Dijkstra’s principle has been applied to establish correlation among the parameters further tuning by finding the pair of suitable parameters. Two other algorithms which are based on application feedback (to provide performance goals to the system) has been introduced. The proposed algorithms collectively (as a framework), addressed the parameter tuning problem. The effectiveness of the algorithms is verified and further measured in distinct parameter tuning scenarios and promising outcome has been achieved.
Cite This Paper
Surendra Kumar Shukla, Devesh Pratap Singh, Shaili Gupta, Kireet Joshi, Vishan Kumar Gupta, "A Theoretical Graph based Framework for Parameter Tuning of Multi-core Systems", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.12, No.4, pp. 15-25, 2022. DOI:10.5815/ijwmt.2022.04.02
Chien, S., Chang, Y., Yang, C., Hwang, Y., and Lee, J.K., ‘Graph Support and Scheduling for OpenCL on Heterogeneous Multi-core Systems’, In Proceedings of the 47th International Conference on Parallel Processing Companion (ICPP '18). Association for Computing Machinery, New York, NY, USA, Article 14, 2018, pp. 1–7.
Huang, J., Qin, W., Wang, X. and Chen, W., ‘Survey of external memory large-scale graph processing on a multi-core system’, The Journal of Supercomputing’, Vol. 76, 2020, pp. 549–579.
Qiang, F., and Huang, H. H., ‘Automatic Generation of High-Performance Inference Kernels for Graph Neural Networks on Multi-Core Systems’, In 50th International Conference on Parallel Processing (ICPP 2021), Association for Computing Machinery, New York, NY, USA, Article 33, 2021, pp. 1–11.
Bhuiyan, A., Liu, D., Khan, A., Saifullah, A., Guan N. and Guo, Z., ‘Energy-Efficient Parallel Real-Time Scheduling on Clustered Multi-Core’, IEEE Transactions on Parallel and Distributed Systems, Vol. 31, No. 9, 2020, pp. 2097-2111.
Bylina, B., Potiopa, J., Klisowski, M. and Bylina, J., ‘The impact of vectorization and parallelization of the slope algorithm on performance and energy efficiency on multi-core architecture’, 16th Conference on Computer Science and Intelligence Systems (FedCSIS), 2021.
Arndt, O. J., Lüders, M., Riggers, C. and Blume, H., ‘Multicore Performance Prediction with MPET’, Journal of Signal Processing System Vol. 92, 2020, pp. 981–998.
Shukla, S. K., Murthy, C. and Chande, P.K., ‘Parameter Trade-off and Performance Analysis of Multi-core Architecture’, Published in: Progress in Systems Engineering, Springer, 2015.
Huang, C., Li Y. and Yao X., ‘A Survey of Automatic Parameter Tuning Methods for Metaheuristics’, in IEEE Transactions on Evolutionary Computation, Vol. 24, No. 2, 2020, pp. 201-216.
Zhang B. and Hu, D.J., ‘Research on the construction and simulation of PO-Dijkstra algorithm model in parallel network of multicore platform’, EURASIP Journal on Wireless Communications and Networking, Vol. 85, 2020.
Kounev, S., Lewis, P., Bellman, K. L., Bencomo, N., Camara, J., Diaconescu, A., Esterle, L., Geihs, K., Giese, H., Götz, S., Inverardi, P., Kephart J.O. and Zisman, A., ‘The notion of self-aware computing’, In Self-Aware Computing Systems, Springer, pp. 3-16, 2017.
Rathod, A., Thakker, R., ‘Parameter Extraction of PSP MOSFET Model in Multi-core Zynq SoC Platform’, Procedia Computer Science, vol. 171, 2020, pp. 1027-1036.
Shukla S. S. and Chande, P.K., ‘Parameter Analysis of Interfering Applications in Multi-Core Environment for Throughput Enhancement’, International Journal of Engineering and Advanced Technology (IJEAT), Vol.9, Issue.2, 2019, pp. 1272-1286.
Shukla, S. S. and Chande, P.K. ‘Investigating Policies for Performance of Multi-core Processors’, International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, 2019, pp.964-980.
Ezzati-Jivan, N., Fournier, Q., Dagenais M. R. and Hamou-Lhadj, A., ‘DepGraph: Localizing Performance Bottlenecks in Multi- Core Applications Using Waiting Dependency Graphs and Software Tracing’, IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM), pp. 149- 159, 2020.
Jadon, S., Yadav, R.S., ‘Load Balancing in Multicore Systems using Heuristics Based Approach’, International Journal of Intelligent Systems and Applications (IJISA), Vol.10, No.12, pp.56-68, 2018.
Khari, M., Kumar, R. L, Dac-Nhuong, Chatterjee, J. M., ‘Interconnect Network on Chip Topology in Multi-core Processors: A Comparative Study’, International Journal of Computer Network and Information Security, Vol.9, No.11, 2017, pp.52-62
Umbarkar, A. J., Rothe, N. M., Sathe, A.S., ‘OpenMP Teaching-Learning Based Optimization Algorithm over Multi-Core System’, International Journal of Intelligent Systems and Applications (IJISA), vol.7, no.7, pp.57-65, 2015.
Shukla, S. K., Gupta, V. K., Joshi, K., Gupta, A., & Singh, M. K. (2022). Self-aware Execution Environment Model (SAE2) for the Performance Improvement of Multicore Systems. International Journal of Modern Research, Vol. 2, No. 1, pp. 17–27.
Gupta, V. K., and Rana, P. S. ‘Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model, Journal of Bioinformatics and Computational Biology’, 2019. Vol. 17, pp. 1–26.
Jaiswal, N., Gupta, V. K., and Mishra, A., Survey paper on various techniques of recognition and tracking. In 2015 International Conference on Advances in Computer Engineering and Applications, IEEE, 2015, pp. 921-925.
Yadav, P., Varshney, R., and Gupta, V. K., Diagnosis of breast cancer using decision tree models and SVM. International Research Journal of Engineering and Technology (IRJET) e-ISSN, 2018, pp. 2395-0056.
Gupta, V. K., Shukla, S.K., Anupriya, and Rawat, R.S., Crime Tracking System and People’s Safety in India using Machine Learning Approaches. International Journal of Modern Research, 2022, Vol. 2, pp. 1–7.
Gupta, V.K., Gupta, A., Jain, P. and Kumar, P., 2022. Linear B-cell epitopes prediction using bagging based proposed ensemble model. International Journal of Information Technology, pp.1-10.