Data Allocation with Minimum Cost under Guaranteed Probability for Multiple Types of Memories

Abstract

As the advance of memory technologies, multiple types of memories such as different kinds of non-volatile memory (NVM), SRAM, DRAM, etc. provide a flexible configuration considering performance, energy and cost. For improving the performance of systems with multiple types of memories, data allocation is one of the most important tasks. The previous studies on data allocation problem assume the worst (fixed) case of data-access frequencies. However, the data allocation produced by employing worst case usually leads to an inferior performance for most of time. In this paper, we model this problem by probabilities and design efficient algorithms that can give optimal-cost data allocation with a guaranteed probability. We propose DAGP algorithm produces a set of feasible data allocation solutions which generates the minimum access time or cost guaranteed by a given probability. We also propose a polynomial-time algorithm, MCS algorithm, to solve this problem. The experiments show that our technique can significantly reduce the access cost compared with the technique considering worst case scenario. For example, comparing with the optimal result generated by employing the worst cases, DAGP can reduce memory access cost by 9.92 % on average when guaranteed probability is set to be 0.9. Moreover, for 90 percents of cases, memory access time is reduced by 12.47 % on average. Comparing with greedy algorithm, DAGP and MCS can reduce memory access cost by 78.92 % and 44.69 % on average when guaranteed probability is set to be 0.9.