Applying Machine Learning to Understand Write Performance of Large-scale Parallel Filesystems

Conference · Fri Nov 01 04:00:00 EDT 2019

DOI:https://doi.org/10.1109/PDSW49588.2019.00008· OSTI ID:1606822

Xie, Bing ^[1]; Tan, Zilong ^[2]; Carns, Philip ^[3]; Chase, Jeffrey ^[4]; Harms, Kevin ^[3]; Lofstead, Gerald ^[5]; Oral, Sarp ^[1]; Vazhkudai, Sudharshan ^[1]; Wang, Feiyi ^[1]

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1. ORNL
2. Carnegie Mellon University (CMU)
3. Argonne National Laboratory (ANL)
4. Duke University
5. Sandia National Laboratories (SNL)

In high-performance computing (HPC), I/O performance prediction offers the potential to improve the efficiency of scientific computing. In particular, accurate prediction can make runtime estimates more precise, guide users toward optimal checkpoint strategies, and better inform facility provisioning and scheduling policies. HPC I/O performance is notoriously difficult to predict and model, however, in large part because of inherent variability and a lack of transparency in the behaviors of constituent storage system components. In this work we seek to advance the state of the art in HPC I/O performance prediction by (1) modeling the mean performance to address high variability, (2) deriving model features from write patterns, system architecture and system configurations, and (3) employing Lasso regression model to improve model accuracy. We demonstrate the efficacy of our approach by applying it to a crucial subset of common HPC I/O motifs, namely, file-per-process checkpoint write workloads. We conduct experiments on two distinct production HPC platforms—Titan at the Oak Ridge Leadership Computing Facility and Cetus at the Argonne Leadership Computing Facility—to train and evaluate our models. We find that we can attain ≤ 30% relative error for 92.79% and 99.64% of the samples in our test set on these platforms, respectively.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1606822
• Country of Publication: United States
• Language: English