  In recent years, the Multi-message Private Information Retrieval (MPIR)
problem has received significant attention from the research community. In this
problem, a user wants to privately retrieve $D$ messages out of $K$ messages
whose identical copies are stored on $N$ remote servers, while maximizing the
download rate. The MPIR schemes can find applications in many practical
scenarios and can serve as an important building block for private computation
and private machine learning applications. The existing solutions for MPIR
require a large degree of subpacketization, which can result in large
overheads, high complexity, and impose constraints on the system parameters.
These factors can limit practical applications of the existing solutions. In
this paper, we present a methodology for the design of scalar-linear MPIR
schemes. Such schemes are easy to implement in practical systems as they do not
require partitioning of messages into smaller size sub-messages and do not
impose any constraints on the minimum required size of the messages. Focusing
on the case of $N=D+1$, we show that when $D$ divides $K$, our scheme achieves
the capacity, where the capacity is defined as the maximum achievable download
rate. When the divisibility condition does not hold, the performance of our
scheme is the same or within a small additive margin compared to the best known
scheme that requires a high degree of subpacketization.
