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The advent of non-volatile memory (NVM) technologies like PCM, STT,memristors and Fe-RAM is believed to enhance the system performance by gettingrid of the traditional memory hierarchy by reducing the gap between memory andstorage. This memory technology is considered to have the performance like thatof DRAM and persistence like that of disks. Thus, it would also providesignificant performance benefits for big data applications by allowingin-memory processing of large data with the lowest latency to persistence.Leveraging the performance benefits of this memory-centric computing technologythrough traditional memory programming is not trivial and the challengesaggravate for parallel/concurrent applications. To this end, severalprogramming abstractions have been proposed like NVthreads, Mnemosyne andintel's NVML. However, deciding upon a programming abstraction which is easierto program and at the same time ensures the consistency and balances varioussoftware and architectural trade-offs is openly debatable and active area ofresearch for NVM community.  We study the NVthreads, Mnemosyne and NVML libraries by building a concurrentand persistent set and open addressed hash-table data structure application. Inthis process, we explore and report various tradeoffs and hidden costs involvedin building concurrent applications for persistence in terms of achievingefficiency, consistency and ease of programming with these NVM programmingabstractions. Eventually, we evaluate the performance of the set and hash-tabledata structure applications. We observe that NVML is easiest to program withbut is least efficient and Mnemosyne is most performance friendly but involvessignificant programming efforts to build concurrent and persistentapplications.

Persistent Memory Programming Abstractions in Context of Concurrent Applications