RADAR: Runtime Asymmetric Data-access Driven Scientific Data Replication
|Title||RADAR: Runtime Asymmetric Data-access Driven Scientific Data Replication|
|Year of Publication||2014|
|Authors||Jenkins, J, Zou, X, Tang, H, Kimpe, D, Ross, RB, Samatova, NF|
EfficientI/Oonlarge-scalespatiotemporalscientificdatarequiresscrutiny of both the logical layout of the data (e.g., row-major vs. column-major) and the physical layout (e.g., distribution on parallel filesystems). For increasingly complex datasets, hand optimization is a difficult matter prone to error and not scalable to the increasing heterogeneity of analysis workloads. Given these factors, we present a partial data replication system called RADAR. We capture datatype-and collective-aware I/O access patterns (indicating logical access) via MPI-IO tracing and use a combination of coarse-grained and fine-grained performance modeling to evaluate and select optimized physical data distributions for the task at hand. Unlike conventional methods, we store all replica data and metadata, along with the original untouched data, under a single file container using the object abstraction in parallel filesystems. Our system can produce up to manyfold improvements in commonly used subvolume decomposition access patterns. Moreover, the modeling approach can determine whether such optimiza- tions should be undertaken in the first place.