Abstract: In the popular big data processing platforms like Spark, it is common to collect data in a many-to-many fashion during a stage traditionally known as the Shuffle phase. Data exchange happens across different types of tasks or stages via Shuffle phase. And during this phase, the data need to be transferred via network and persisted into traditional disk-based file system. Hence, the efficiency of Shuffle phase is one of the key factors in the performance of the big data processing. In order to reducing I/O overheads, we propose an optimized Shuffle strategy based on Non-Volatile Memory (NVM)—NV-Shuffle. Next-generation non-volatile memory (NVM) technologies, such as Phase Change Memory (PCM), Spin-Transfer Torque Magnetic Memories (STTMs) introduce new opportunities for reducing I/O overhead, due to their non-volatility, high read/write performance, low energy, etc. In the big data processing platform based on memory computing such as Spark, Shuffle data access based on disks is an important factor of application performance, NV-Shuffle uses NVM as persist memory to store Shuffle data and employs direct data accesses like memory by introducing NV-Buffer to organize data instead of traditional file system.We implemented NV-Shuffle in Spark. Our performance results show, NV-shuffle reduces job execution time by 10%~40% for Shuffle-heavy workloads.