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Open-access neuroimaging datasets have reached petabyte scale, and continueto grow. The ability to leverage the entirety of these datasets is limited to arestricted number of labs with both the capacity and infrastructure to processthe data. Whereas Big Data engines have significantly reduced applicationperformance penalties with respect to data movement, their applied strategies(e.g. data locality, in-memory computing and lazy evaluation) are notnecessarily practical within neuroimaging workflows where intermediary resultsmay need to be materialized to shared storage for post-processing analysis. Inthis paper we evaluate the performance advantage brought by Intel(R) OptaneTMDC persistent memory for the processing of large neuroimaging datasets usingthe two available configurations modes: Memory mode and App Direct mode. Weemploy a synthetic algorithm on the 76 GiB and 603 GiB BigBrain, as well asapply a standard neuroimaging application on the Consortium for Reliability andReproducibility (CoRR) dataset using 25 and 96 parallel processes in bothcases. Our results show that the performance of applications leveragingpersistent memory is superior to that of other storage devices,with theexception of DRAM. This is the case in both Memory and App Direct mode andirrespective of the amount of data and parallelism. Furthermore, persistentmemory in App Direct mode is believed to benefit from the use of DRAM as acache for writing when output data is significantly smaller than availablememory. We believe the use of persistent memory will be beneficial to bothneuroimaging applications running on HPC or visualization of large,high-resolution images.

Performance benefits of Intel(R) OptaneTM DC persistent memory for the parallel processing of large neuroimaging data