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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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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