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| Content Provider | ACM Digital Library |
|---|---|
| Author | Malony, Allen D. Mahéo, Aurèle Shende, Sameer Panda, Dhabaleswar K. DK Subramoni, Hari Ramesh, Srinivasan |
| Abstract | MPI implementations are becoming increasingly complex and highly tunable, and thus scalability limitations can come from numerous sources. The MPI Tools Interface (MPI_T) introduced as part of the MPI 3.0 standard provides an opportunity for performance tools and external software to introspect and understand MPI runtime behavior at a deeper level to detect scalability issues. The interface also provides a mechanism to re-configure the MPI library dynamically at runtime to fine-tune performance. In this paper, we propose an infrastructure that extends existing components - TAU, MVAPICH2 and BEACON to take advantage of the MPI_T interface to offer runtime introspection, online monitoring, recommendation generation and autotuning capabilities. We validate our design by developing optimizations for a combination of production and synthetic applications. We use our infrastructure to implement an autotuning policy for AmberMD[1] that monitors and reduces MVAPICH2 library internal memory footprint by 20% without affecting performance. For applications where collective communication is latency sensitive such as MiniAMR[2], our infrastructure is able to generate recommendations to enable hardware offloading of collectives supported by MVAPICH2. By implementing this recommendation, we see a 5% improvement in application runtime. |
| Starting Page | 1 |
| Ending Page | 11 |
| Page Count | 11 |
| File Format | |
| ISBN | 9781450348492 |
| DOI | 10.1145/3127024.3127036 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-09-25 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Performance recommendations Performance engineering Mvapich2 Beacon Tau Mpi_t Autotuning Runtime introspection |
| Content Type | Text |
| Resource Type | Article |
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