  Multi-accelerator servers are increasingly being deployed in shared
multi-tenant environments (such as in cloud data centers) in order to meet the
demands of large-scale compute-intensive workloads. In addition, these
accelerators are increasingly being inter-connected in complex topologies and
workloads are exhibiting a wider variety of inter-accelerator communication
patterns. However, existing allocation policies are ill-suited for these
emerging use-cases. Specifically, this work identifies that multi-accelerator
workloads are commonly fragmented leading to reduced bandwidth and increased
latency for inter-accelerator communication. We propose Multi-Accelerator
Pattern Allocation (MAPA), a graph pattern mining approach towards providing
generalized allocation support for allocating multi-accelerator workloads on
multi-accelerator servers. We demonstrate that MAPA is able to improve the
execution time of multi-accelerator workloads and that MAPA is able to provide
generalized benefits across various accelerator topologies. Finally, we
demonstrate a speedup of 12.4% for 75th percentile of jobs with the worst case
execution time reduced by up to 35% against baseline policy using MAPA.
