Planting Fast-growing Forest by Leveraging the Asymmetric Read/Write Latency of NVRAM-based Systems

Owing to the considerations of cell density and low static power consumption, non-volatile random-access memory (NVRAM) has been a promising candidate for collaborating with a dynamic random-access memory (DRAM) as main memory in modern computer systems. As NVRAM also brings technical challenges (e.g., limited endurance and high writing cost) to computer system developers, the concept of write reduction becomes the famous doctrine in NVRAM-based system design. Unfortunately, a well-known machine learning algorithm, random forest, will generate a massive amount of write traffic to main memory space during its construction phase. In other words, a random forest hits the Achilles&null heel of NVRAMbased systems. For remedying this pain, our work proposes an NVRAM-friendly random forest algorithm, namely Amine, for an NVRAM-based system. The design principle of Amine is to replace write operations with read accesses without raising the read complexity of the random forest algorithm. According to experimental results, Amine can effectively decrease the latency of random forest construction by 64%, compared with the original random forest algorithm.