  Distributed computation is a framework used to break down a complex
computational task into smaller tasks and distributing them among computational
nodes. Erasure correction codes have recently been introduced and have become a
popular workaround to the well known "straggling nodes" problem, in particular,
by matching linear coding for linear computation tasks. We observe that
decoding tends to amplify the computation "noise", i.e., the numerical errors
at the computation nodes. We use noise amplification as a performance measure
to compare various erasure-correction codes, and in particular polynomial codes
(which Reed-Solomon codes and other popular codes are a subset of). We show
that noise amplification can be significantly reduced by a clever selection of
the sampling points and powers of the polynomial code.
