Manuela Fischer
Breaking the Linear-Memory Barrier in MPC: Fast MIS on Trees with $n^{\eps}$ Memory per Machine
Abstract: Recently, studying fundamental graph problems in the
\emph{Massively Parallel Computation (MPC)} framework, inspired by the
\emph{MapReduce} paradigm, has gained a lot of attention. A standard
assumption, common to most traditional approaches, is to allow Ω(n)
memory per machine, where n is the number of nodes in the graph and Ω
hides polylogarithmic factors. However, as pointed out by Karloff et
al.~[SODA'10] and Czumaj et al.~[arXiv:1707.03478], it might be
unrealistic for a single machine to have linear or only slightly
sublinear memory. In this paper, we propose the study of a more
practical variant of the MPC model which only requires substantially
sublinear or even subpolynomial memory per machine. In contrast to the
standard MPC model and also streaming, in this low-memory MPC setting,
a single machine will only see a small number of nodes in the
graph. We introduce a new technique to cope with this imposed
locality.
In particular, we show that the \emph{Maximal Independent Set (MIS)}
problem can be solved efficiently, that is, in $\bigO(\log^2 \log n)$
rounds, when the input graph is a tree. This substantially reduces the
local memory from $\frac{n}{\poly\log n}$ required by the recent
$\bigO(\log \log n)$-round MIS algorithm of Ghaffari et al., to
$n^{\eps}$, without incurring a significant loss in the round
complexity. Moreover, it demonstrates how to make use of the
all-to-all communication in the MPC model to exponentially improve on
the corresponding bound in the LOCAL and PRAM models by Lenzen and
Wattenhofer [PODC'11].