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Scientific applications use checkpointing for failure recovery. The existing checkpointing approaches were proposed for storing persistent states of applications as checkpoints in disk-based file systems via the block interface. As non-volatile main memory (NVMM) will be included in high-performance computing systems, storing the checkpoints in NVMM-based file systems can significantly waste the performance benefits of NVMM. This is because it underutilizes memory resources and it does not take advantage of the byte-addressability of NVMM. In this paper, we propose an NVMM-aware checkpointing approach, named NV-Checkpoint. It uses a compiler-aided technique to automatically generate multi-version data structures, which consist of both the persistent version of data stored in NVMM for failure recovery and the ephemeral version of data placed across DRAM and NVMM. Because of the byte-addressability of NVMM, any versions can be accessed via the memory interface. The multiple versions may share data that are not mutated during the program's execution to reduce data redundancy. NV-Checkpoint provides the same level of guarantee of failure recovery compared to the conventional checkpointing approaches proposed for file systems. Furthermore, its runtime system manages the layout of the data structures to reduce the number of writes to NVMM. It also manages the checkpointing frequency to reduce persistence overhead using machine learning models. Our experimental results with real-world scientific applications show that the performance of annotated programs with NV-Checkpoint using a hybrid of DRAM and NVMM matches the performance of best-effort handwritten versions. It achieves similar scalability as those with ephemeral data structures using only DRAM. It offers up to 121X speedup of execution time

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