The paper presents Sea, a data-management library designed to reduce data transfer-related overheads in neuroimaging applications. Sea leverages the LD_PRELOAD trick to intercept and redirect application read and write calls to local or remote storage transparently.
The authors benchmarked Sea by processing three functional MRI datasets of increasing sizes (ds001545, PREVENT-AD, Human Connectome Project) with three common neuroimaging preprocessing pipelines (AFNI, SPM, FSL) on both a controlled HPC cluster and a production cluster.
The results show that Sea can provide large speedups (up to 32x) when the shared file system's (e.g., Lustre) performance is deteriorated by other users' workloads. When the shared file system is not overburdened, Sea's performance is comparable to the baseline, suggesting minimal overhead. The speedups are most significant for data-intensive pipelines and larger datasets with bigger individual image files.
Sea complements existing neuroimaging tools and standards by facilitating the processing of neuroimaging big data. It provides transparent data management capabilities without requiring modifications to the existing applications.
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