Numerical validation enables one to ensure the reliability of
numerical computations that rely on floating-point operations. Discrete
Stochastic Arithmetic (DSA) makes it possible to validate the accuracy
of floating-point computations using random rounding. However, it may
bring a large performance overhead compared with the standard floating-
point operations. In this talk, we show that with perturbed data it is
possible to use standard floating-point arithmetic instead of DSA for the
purpose of numerical validation. For instance, for codes including matrix
multiplications, we can directly utilize the matrix multiplication routine
(GEMM) of level-3 BLAS that is performed with standard floating-point
arithmetic. Consequently, we can achieve a significant performance im-
provement by avoiding the performance overhead of DSA operations as
well as by exploiting the speed of highly-optimized BLAS implementa-
tions. Finally, we demonstrate the performance gain using Intel MKL
routines compared against the DSA version of BLAS routines.