Extending Ginkgo to Manage Reconfigurable Hardware-Based Kernels

Although heterogeneous systems based on hardware accelerators are a trending topic in the HPC community, exploring the trade-offs of reconfigurable hardware-based ones in linear algebra libraries for high-performance systems, has not been deeply studied. Therefore, in this research, we aim to take advantage of FPGAs' reconfigurability, adaptability, and capacity to reduce power consumption to generate FPGA-based kernels in Ginkgo, a specialized high-performance linear algebra library for many-core systems. We generated 3 FPGA-based kernels for the CSR, SELLP, and SELL SpMV formats, and obtained speedups of at least 10x concerning CPU-based kernels. Furthermore, we demonstrated via a performance characterization study that FPGAs outperform general-purpose processors in terms of compute time.