While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect so errors o ine in the fast Fourier transform (FFT) a er computation nishes, none of the existing ABFT schemes detect so errors online before the computation nishes. is paper presents an online ABFT scheme for FFT so that so errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and nally incorporate it into the widely used FFTW library -one of the today's fastest FFT so ware implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing o ine ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and be er fault coverage.
Another ABFT scheme has recently been proposed to correct soft errors online in the Fast Fourier Transform REF .
Correcting soft errors online in fast fourier transform
SC '17
Computer Science