Hybrid Network Utilization for Efficient Communication in a Tightly Coupled FPGA Cluster A tightly coupled FPGA cluster is a promising approach for largescale parallel processing with application specialized hardware. Along with the advantages of FPGAbased custom computing, such as high power efficiency, a customized network subsystem with efficient communication through direct InterFPGA links allows an FPGA cluster to be an effective platform for largescale parallel processing. However, the cluster can suffer from substantial communication costs when a cluster becomes larger to obtain higher computing performance. In this paper, we propose to exploit the communication capacity of a host server network to improve communication performance. Besides, we show estimations for practical communication patterns on a network model in which we efficiently use both the FPGA and the host networks.

Optimisation of System Throughput Exploiting Tasks Heterogeneity on Space Shared FPGAs There are challenges in optimising system throughput in FPGAbased cloud computing due to mapping constraints resulting in suboptimal space sharing of resources, as the number of tasks grow and become more heterogeneous. This work proposes a methodology for exploring and optimising their resource utilisation. By identifying highlevel synthesis parameters for each task, machine learning models and intelligent clustering are then employed to define clusters of tasks which will share the FPGA space. Assuming heterogeneity characterisation of tasks and thus static partitioning of the FPGA, it is ensured that each task in a cluster accommodates other tasksu0027 resource requirements resulting in a higher compute density. Using 11 high performance computing tasks, we achieve an average 3.3xc3x97 higher system throughput at 2.8xc3x97 better energy efficiency when compared to existing approaches.