  The advent of non-volatile memory (NVM) technologies like PCM, STT,
memristors and Fe-RAM is believed to enhance the system performance by getting
rid of the traditional memory hierarchy by reducing the gap between memory and
storage. This memory technology is considered to have the performance like that
of DRAM and persistence like that of disks. Thus, it would also provide
significant performance benefits for big data applications by allowing
in-memory processing of large data with the lowest latency to persistence.
Leveraging the performance benefits of this memory-centric computing technology
through traditional memory programming is not trivial and the challenges
aggravate for parallel/concurrent applications. To this end, several
programming abstractions have been proposed like NVthreads, Mnemosyne and
intel's NVML. However, deciding upon a programming abstraction which is easier
to program and at the same time ensures the consistency and balances various
software and architectural trade-offs is openly debatable and active area of
research for NVM community.
  We study the NVthreads, Mnemosyne and NVML libraries by building a concurrent
and persistent set and open addressed hash-table data structure application. In
this process, we explore and report various tradeoffs and hidden costs involved
in building concurrent applications for persistence in terms of achieving
efficiency, consistency and ease of programming with these NVM programming
abstractions. Eventually, we evaluate the performance of the set and hash-table
data structure applications. We observe that NVML is easiest to program with
but is least efficient and Mnemosyne is most performance friendly but involves
significant programming efforts to build concurrent and persistent
applications.
