Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
OPT versus LOAD in dynamic storage allocation
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Phase-change random access memory: a scalable technology
IBM Journal of Research and Development
Scalable high performance main memory system using phase-change memory technology
Proceedings of the 36th annual international symposium on Computer architecture
PDRAM: a hybrid PRAM and DRAM main memory system
Proceedings of the 46th Annual Design Automation Conference
PCRAMsim: system-level performance, energy, and area modeling for phase-change ram
Proceedings of the 2009 International Conference on Computer-Aided Design
Proceedings of the 47th Design Automation Conference
Register allocation for write activity minimization on non-volatile main memory
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Cooperating Write Buffer Cache and Virtual Memory Management for Flash Memory Based Systems
RTAS '11 Proceedings of the 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium
Power-aware variable partitioning for DSPs with hybrid PRAM and DRAM main memory
Proceedings of the 48th Design Automation Conference
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This paper targets task allocation problem on hybrid main memory composed of non-volatile memory (NVM) and DRAM. Compared to the conventional memory technology DRAM, the emerging NVM has excellent energy performance due to the ultra low leakage power. However, most types of NVMs come with the disadvantages of much shorter write endurance and longer write latency as opposed to DRAM. This paper explores task allocation problems on hybrid memory which consists of energy-efficient NVM and write-endurable DRAM. The objectives of the task allocation include minimizing the energy consumption, extending the lifetime and minimizing the size. The contributions of this work are twofold. First, we design Integer Linear Programming (ILP) formulations that can solve different objectives optimally. Then, we propose three effective polynomial time heuristic algorithms. All the ILP formulations and the proposed heuristics are executed to optimize multiple objectives offline. Experiments show that compared to the optimal solutions generated by the ILP formulations, the proposed heuristics can produce near-optimal results.