By demonstrating upper bounds on power-constrained hybrid
MPI + OpenMP application performance, we have shown that cur-
rent runtimes are leaving some performance on the table and wast-
ing power. In our tests, we found that an optimal power realloca-
tion and configuration selection algorithm can achieve up to 41.1%
more performance than existing algorithms, and that requiring con-
figurable node-level parallelism such as OpenMP in addition to
MPI goes a long way towards the goal of flexible power and per-
formance management. However, in most cases, our Conductor
runtime is quite close to the optimal performance as indicated by
our LP. In some cases, Conductor even achieves optimal schedules
as identified by the LP.
The ability to efficiently manage power at runtime will become
increasingly important with future generations of supercomputers,
which will require new power-aware runtimes. Our work in this
paper provides the necessary goalposts and realistic targets for any
future runtime to compare against.
By demonstrating upper bounds on power-constrained hybridMPI + OpenMP application performance, we have shown that cur-rent runtimes are leaving some performance on the table and wast-ing power. In our tests, we found that an optimal power realloca-tion and configuration selection algorithm can achieve up to 41.1%more performance than existing algorithms, and that requiring con-figurable node-level parallelism such as OpenMP in addition toMPI goes a long way towards the goal of flexible power and per-formance management. However, in most cases, our Conductorruntime is quite close to the optimal performance as indicated byour LP. In some cases, Conductor even achieves optimal schedulesas identified by the LP.The ability to efficiently manage power at runtime will becomeincreasingly important with future generations of supercomputers,which will require new power-aware runtimes. Our work in thispaper provides the necessary goalposts and realistic targets for anyfuture runtime to compare against.
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