In this paper, we investigate the problem of multi-user linearly decomposable function computation, where $N$ servers help compute functions for $K$ users, and where each such function can be expressed as a linear combination of $L$ basis subfunctions. The process begins with each server computing some of the subfunctions, then broadcasting a linear combination of its computed outputs to a selected group of users, and finally having each user linearly combine its received data to recover its function. As it has become recently known, this problem can be translated into a matrix decomposition problem $\mathbf{F}=\mathbf{D}\mathbf{E}$, where $\mathbf{F} \in \mathbf{GF}(q)^{K \times L}$ describes the coefficients that define the users' demands, where $\mathbf{E} \in \mathbf{GF}(q)^{N \times L}$ describes which subfunction each server computes and how it combines the computed outputs, and where $\mathbf{D} \in \mathbf{GF}(q)^{K \times N}$ describes which servers each user receives data from and how it combines this data. Our interest here is in reducing the total number of subfunction computations across the servers (cumulative computational cost), as well as the worst-case load which can be a measure of computational delay. Our contribution consists of novel bounds on the two computing costs, where these bounds are linked here to the covering and packing radius of classical codes. One of our findings is that in certain cases, our distributed computing problem -- and by extension our matrix decomposition problem -- is treated optimally when $\mathbf{F}$ is decomposed into a parity check matrix $\mathbf{D}$ of a perfect code, and a matrix $\mathbf{E}$ which has as columns the coset leaders of this same code.