Name
Title | ||
|---|---|---|
Balancing job performance with system performance via locality-aware scheduling on torus-connected systems |
Abstract | ||
|---|---|---|
Torus-connected network is widely used in modern supercomputers due to its linear per node cost scaling and its competitive overall performance. Job scheduling system plays a critical role for the efficient use of supercomputers. As supercomputers continue growing in size, a fundamental problem arises: how to effectively balance job performance with system performance on torus-connected machines? In this work, we will present a new scheduling design named window-based locality-aware scheduling. Our design contains three novel features. First, rather than one-by-one job scheduling, our design takes a “window” of jobs, i.e. multiple jobs, into consideration for job prioritizing and resource allocation. Second, our design maintains a list of slots to preserve node contiguity information for resource allocation. Finally, we formulate our scheduling decision making into a 0-1 Multiple Knapsack Problem and present two algorithms to solve the problem. A series of trace-based simulations using job logs collected from production supercomputers indicate that this new scheduling design has real potentials and can effectively balance job performance and system performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CLUSTER.2014.6968751 | CLUSTER |
Keywords | DocType | ISSN |
competitive overall performance,processor scheduling,supercomputers,torus-connected network,scheduling decision making,trace-based simulations,job prioritizing,node contiguity information,torus-connected machines,system performance,resource allocation,scheduling design,0-1 multiple knapsack problem,job scheduling system,job performance,knapsack problems,performance evaluation,window-based locality-aware scheduling,mobile computing,parallel machines | Conference | 1552-5244 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Yang | 1 | 87 | 6.95 |
| Zhou Zhou | 2 | 85 | 6.02 |
| Wei Tang | 3 | 152 | 10.65 |
| Xingwu Zheng | 4 | 2 | 0.38 |
| Jia Wang | 5 | 148 | 12.47 |
| Zhiling Lan | 6 | 818 | 54.25 |