Name
Title | ||
|---|---|---|
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters. |
Abstract | ||
|---|---|---|
The problem of scheduling of workloads onto heterogeneous processors (e.g., CPUs, GPUs, FPGAs) is of fundamental importance in modern data centers. Current system schedulers rely on application/system-specific heuristics that have to be built on a case-by-case basis. Recent work has demonstrated ML techniques for automating the heuristic search by using black-box approaches which require significant training data and time, which make them challenging to use in practice. This paper presents Symphony, a scheduling framework that addresses the challenge in two ways: (i) a domain-driven Bayesian reinforcement learning (RL) model for scheduling, which inherently models the resource dependencies identified from the system architecture; and (ii) a sampling-based technique to compute the gradients of a Bayesian model without performing full probabilistic inference. Together, these techniques reduce both the amount of training data and the time required to produce scheduling policies that significantly outperform black-box approaches by up to 2.2{\texttimes}. |
Year | Venue | DocType |
|---|---|---|
2020 | ICML | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subho S. Banerjee | 1 | 26 | 6.88 |
| Saurabh Jha | 2 | 9 | 2.94 |
| Zbigniew Kalbarczyk | 3 | 1896 | 159.48 |
| Ravishankar K. Iyer | 4 | 3489 | 504.32 |