  This paper studies the master-worker distributed linearly separable
computation problem, where the considered computation task, referred to as
linearly separable function, is a typical linear transform model widely used in
cooperative distributed gradient coding, real-time rendering, linear
transformers, etc. %A master asks $\Nsf$ distributed workers to compute a
linearly separable function from $\Ksf$ datasets. The computation task on
$\Ksf$ datasets can be expressed as $\Ksf_{\rm c}$ linear combinations of
$\Ksf$ messages, where each message is the output of an individual function on
one dataset. Straggler effect is also considered, such that from the answers of
any $\Nsf_{\rm r}$ of the $\Nsf$ distributed workers, the master should
accomplish the task. The computation cost is defined as the number of datasets
assigned to each worker, while the communication cost is defined as the number
of (coded) messages that should be received. The objective is to characterize
the optimal tradeoff between the computation and communication costs. The
problem has remained so far open, even under the cyclic data assignment.Since
in fact various distributed computing schemes were proposed in the literature
under the cyclic data assignment, with this paper we close the problem for the
cyclic assignment. This paper proposes a new computing scheme with the cyclic
assignment based on the concept of interference alignment, by treating each
message which cannot be computed by a worker as an interference from this
worker. Under the cyclic assignment, the proposed computing scheme is then
proved to be optimal when $\Nsf=\Ksf$ and be order optimal within a factor of
$2$ otherwise.
