  In this article, we propose an accuracy-assuring technique for finding a
solution for unsymmetric linear systems. Such problems are related to different
areas such as image processing, computer vision, and computational fluid
dynamics. Parallel implementation of Krylov subspace methods speeds up finding
approximate solutions for linear systems. In this context, the refined approach
in pipelined BiCGStab enhances scalability on distributed memory machines,
yielding to substantial speed improvements compared to the standard BiCGStab
method. However, it's worth noting that the pipelined BiCGStab algorithm
sacrifices some accuracy, which is stabilized with the residual replacement
technique. This paper aims to address this issue by employing the ExBLAS-based
reproducible approach. We validate the idea on a set of matrices from the
SuiteSparse Matrix Collection.
