Abstract
Spiking Neural Network (SSN) simulators based on clusters of FPGA-based System-on-Chip (SoC) involve the transmission of large amounts of data (from hundreds of MB to tens of GB per second) from and to a data host, usually a PC or a server. TECBrain is an SNN simulator which currently uses Ethernet for transmitting results from its simulations, which can potentially take hours if the effective connection speed is around 100 Mbps. This paper proposes data transfer techniques that optimize data transmissions by grouping data into packages making the most of the payload size and the use of thread-level parallelism, trying to minimize the impact of multiple clients transmitting at the same time. The proposed method achieves its highest throughput when inserting simulation results directly into a No-SQL database.
Using the proposed optimization techniques over an Ethernet connection, the minimum overhead reached is 2.93% (out of the theoretical 2.47%) for five nodes sending data simultaneously from C++, with speeds up to 95 Mbps on a network at 100 Mbps. Besides, the maximum database insertion speed reached is 32.5 MB/s, using large packages and parallelism, which is 26% of the bandwidth of the connection link at 1 Gbps.
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References
Alfaro-Badilla, K., et al.: Improving the simulation of biologically accurate neural networks using data flow HLS transformations on heterogeneous SoC-FPGA platforms. In: CARLA 2019 - Latin America High Performance Computing Conference, September 2019
Alfaro-Badilla, K., et al.: Prototyping a biologically plausible neuron model on a heterogeneous CPU-FPGA board. In: 2019 IEEE 10th Latin American Symposium on Circuits Systems (LASCAS), pp. 5–8, February 2019. https://doi.org/10.1109/LASCAS.2019.8667538
Altera: White paper accelerating high-performance computing with FPGAs. Cluster Computing, pp. 1–8 (2007). https://www.intel.com/content/dam/www/programmable/us/en/pdfs/literature/wp/wp-01029.pdf. Accessed 04 April 2019
Arnst, D., Plenk, V., Adrian, W.: Comparative evaluation of database performance in an Internet of Things context comparative evaluation of database performance in an Internet of Things context. In: ICSNC 2018, vol. 13, pp. 45–50, October (2018)
Chodorow, K.: MongoDB: The Definitive Guide: Powerful and Scalable Data Storage. O’Reilly Media Inc., Sebastopol (2013)
Cramer, T., Friedman, R., Miller, T., Seberger, D., Wilson, R., Wolczko, M.: Compiling Java just in time. IEEE Micro 17(3), 36–43 (1997). https://doi.org/10.1109/40.591653
Dong, T., Dobrev, V., Kolev, T., Rieben, R., Tomov, S., Dongarra, J.: A step towards energy efficient computing: redesigning a hydrodynamic application on CPU-GPU, pp. 972–981, May 2014. https://doi.org/10.1109/IPDPS.2014.103
Furber, S.B., Galluppi, F., Temple, S., Plana, L.A.: The spiNNaker project. Proc. IEEE 102(5), 652–665 (2014). https://doi.org/10.1109/JPROC.2014.2304638
Hamada, T., Benkrid, K., Nitadori, K., Taiji, M.: A comparative study on ASIC, FPGAs, GPUs and general purpose processors in the O(N 2) gravitational N-body simulation. In: Proceedings - 2009 NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2009), pp. 447–452 (2009). https://doi.org/10.1109/AHS.2009.55
Hsieh, C.W., Chou, C.Y., Tsai, T.C., Cheng, Y.F., Kuo, S.H.: NCHC’s Formosa v GPU cluster enters the TOP500 ranking. In: 2012 Proceedings of 4th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2012), pp. 622–624 (2012). https://doi.org/10.1109/CloudCom.2012.6427507
Huang, J., Cai, L.: Research on TCP/IP network communication based on Node.js. In: AIP Conference Proceedings, vol. 1955, issue 1, pp. 040115 (2018). https://doi.org/10.1063/1.5033779. https://aip.scitation.org/doi/abs/10.1063/1.5033779
IEEE: IEEE Standard for Ethernet. IEEE Std 802.3-2018, (Revision of IEEE Std 802.3-2015), pp. 1–5600, August 2018. https://doi.org/10.1109/IEEESTD.2018.8457469.
Li, C., Yang, W.: The distributed storage strategy research of remote sensing image based on Mongo DB. In: The 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2014) - (41271390), pp. 101–104 (2014). https://doi.org/10.1109/EORSA.2014.6927858
Milluzzi, A., George, A., Lam, H.: Computational and memory analysis of Tegra SoCs. In: 2016 IEEE High Performance Extreme Computing Conference (HPEC 2016), issue (1), pp. 1–7 (2016). https://doi.org/10.1109/HPEC.2016.7761602
MongoDB: MongoDB Limits and Thresholds. https://docs.mongodb.com/manual/reference/limits/. Accessed 14 April 2019
Rojas, J., Verastegui, J., Milla, M.: Design and implementation of a high speed interface system over Gigabit Ethernet based on FPGA for use on radar acquisition systems. In: Proceedings of the 2017 Electronic Congress (E-CON UNI 2017) (2018). https://doi.org/10.1109/ECON.2017.8247311
Satheesh, M., D’mello, B.J., Krol, J.: Web Development with MongoDB and NodeJS. Packt Publishing Ltd., Birmingham (2015)
Szebenyi, Z.: Capturing Parallel Performance Dynamics. Forschungszentrum Jülich, Jülich (2012). http://hdl.handle.net/2128/4603
Truica, C.O., Radulescu, F., Boicea, A., Bucur, I.: Performance evaluation for CRUD operations in asynchronously replicated document oriented database. In: Proceedings - 2015 20th International Conference on Control Systems and Computer Science (CSCS 2015), pp. 191–196 (2015). https://doi.org/10.1109/CSCS.2015.32
Xilinx, Inc.: Xilinx WP375 high performance computing using FPGAs. White Pap. 375, 1–15 (2010). https://www.xilinx.com/support/documentation/white_papers/wp375_HPC_Using_FPGAs.pdf
Zamora-Umaña, D.: Desarrollo y validación de un método para la visualización de resultados en la implementación del algoritmo de simulación de redes neuronales. Bachelor’s thesis, Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, December 2017
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León-Vega, L.G., Alfaro-Badilla, K., Chacón-Rodríguez, A., Salazar-García, C. (2020). Optimizing Big Data Network Transfers in FPGA SoC Clusters: TECBrain Case Study. In: Crespo-Mariño, J., Meneses-Rojas, E. (eds) High Performance Computing. CARLA 2019. Communications in Computer and Information Science, vol 1087. Springer, Cham. https://doi.org/10.1007/978-3-030-41005-6_4
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