  In this paper, due to the important value in practical applications, we
consider the coded distributed matrix multiplication problem of computing
$AA^\top$ in a distributed computing system with $N$ worker nodes and a master
node, where the input matrices $A$ and $A^\top$ are partitioned into $m$-by-$p$
and $p$-by-$m$ blocks of equal-size sub-matrices respectively. For effective
straggler mitigation, we propose a novel computation strategy, named
\emph{folded polynomial code}, which is obtained by modifying the entangled
polynomial codes. Moreover, we characterize a lower bound on the optimal
recovery threshold among all linear computation strategies when the underlying
field is the real number field, and our folded polynomial codes can achieve
this bound in the case of $m=1$. Compared with all known computation strategies
for coded distributed matrix multiplication, our folded polynomial codes
outperform them in terms of recovery threshold, download cost, and decoding
complexity.
