Scheduling Splittable Jobs on Configurable Machines
CoRR(2023)
摘要
Motivated by deep neural network applications, we study the problem of
scheduling splittable jobs (e.g., neural network inference tasks) on
configurable machines (e.g., multi-instance GPUs). We are given $n$ jobs and a
set $C$ of configurations (e.g, representing ways to configure a GPU)
consisting of multisets of blocks (e.g., representing GPU instances). A
schedule consists of a set of machines, each assigned some configuration in $C$
with each block in the configuration assigned to process one job. The amount of
a job's demand that is satisfied by a given block is an arbitrary function of
the job and block. The objective is to satisfy all demands on as few machines
as possible. We provide a tight logarithmic approximation algorithm for this
problem in the general setting, an asymptotic $(2 + \varepsilon)$-approximation
with $O(1)$ input configurations for arbitrary $\varepsilon > 0$, and a
polynomial time approximation scheme when both the number and size of
configurations are $O(1)$.
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