High Throughput Multidimensional Tridiagonal System Solvers on FPGAs


We present a high performance tridiagonal solver library for Xilinx FPGAs optimized for multiple multi-dimensional systems common in real-world applications. An analytical performance model is developed and used to explore the design space and obtain rapid performance estimates that are over 85% accurate. This library achieves an order of magnitude better performance when solving large batches of systems than previous FPGA work. A detailed comparison with a current state-of-the-art GPU library for multi-dimensional tridiagonal systems on an Nvidia V100 GPU shows the FPGA achieving competitive or better runtime and significant energy savings of over 30%. Through this design, we learn lessons about the types of applications where FPGAs can challenge the current dominance of GPUs.

In ACM International Conference on Supercomputing (ICS)
Kamalavasan Kamalakkannan
Kamalavasan Kamalakkannan
Warwick PhD Alumnus

My research interests include reconfigurable and high performance computing.

Suhaib A. Fahmy
Suhaib A. Fahmy
Associate Professor of Computer Science

Suhaib is Principal Investigator of the Accelerated Connected Computing Lab (ACCL) at KAUST. His research explores hardware acceleration of complex algorithms and the integration of these accelerators within wider computing infrastructure.