Big data applications put more pressure on memory system in the aspect of capacity and power consumption.However,limited by the shortcomings of DRAM and non-volatile memory(NVM),memory consists of single medium like DRAM or NVM is not competent for the requirements of big data applications.Thus,how to effectively design,efficiently manage and accurately evaluate the hybrid memories consist of DRAM and NVM are the major challenges that the academia and industry face today.The challenges of hybrid memory systems for big data processing from the perspective of computer architecture,system software,programming model and application were analyzed,and several solutions and optimizations were correspondingly provided,such as on-chip cache management,parallel processing,memory access scheduling,energy management,virtual-to-physical address translation,object-level memory allocation and migration mechanisms.Meanwhile,a number of prototypes to validate the effectiveness and efficiency of these proposals were developed.
