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Managing Hybrid Memories by Predicting Object Write Intensity

Emerging Non-Volatile Memory (NVM) technologies offer more capacity and energy efficiency than DRAM, but their write endurance is lower and latency is higher. Hybrid memories seek the best of both worlds — scalability, ef ciency, and performance — by combining DRAM and NVM. Our work proposes modifying a standard managed-language runtime to allocate objects either in DRAM or NVM to maximize the use of NVM capacity without wearing it out. The key to our approach is correctly predicting highly mutated objects and allocating them in DRAM and allocating rarely mutated objects in NVM. We explore write-intensity prediction based on object (1) size, (2) class type, and (3) allocation site. We find predictions using allocation site are the most accurate.

Our memory manager for hybrid memories consists of (1) an of ine pro ling phase that produces placement advice on a per allocation-site basis, and (2) a garbage collector that allocates mature objects in DRAM or NVM based on this advice and that allocates highly mutated nursery objects in DRAM. We explore two heuristics for classifying sites as write-intensive (DRAM) or rarely written (NVM). Write-Frequency (FREQ) uses the number of writes to objects allocated by each site. Although it can limit writes to NVM up to 1 and 3%, it allocates just 50% to 20% of mature objects in DRAM. Write-Density (DENS) computes number of writes to objects relative to object size. Write-Density is a better predictor. When it limits NVM writes to 2%, it can allocate 88% of mature objects to NVM. Pareto optimal con gurations may increase writes to 10% and store 99% of mature objects in NVM. Using write-intensity predictors to proactively place objects in hybrid DRAM and NVM memory systems prolongs NVM’s lifetime while exploiting its capacity.