Energy-conscious memory allocation and deallocation for pointer-intensive applications

Victor De La Luz, Mahmut Kandemir, Guangyu Chen, Ibrahim Kolcu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A multi-bank memory architecture is composed of multiple memory banks, each of which can be energy-managed independently. In this paper, we present a set of strategies for reducing energy consumption in a multi-bank memory architecture using energy-conscious dynamic memory allocation/deallocation. Applications that make dynamic memory allocations are used very frequently in mobile computing/networking area. Our strategies focus on such applications and try to cluster dynamically created data with temporal affinity in the physical address space such that the data occupy a small number of memory banks. The remaining banks can be shut off, saving energy. All of our strategies have been implemented and tested using an in-house energy simulator and an application suite that consists of nine pointer-intensive real-life applications. Our results show that all the strategies considered in this paper save energy (e.g., our user-initiated strategy saves 49% leakage energy on the average). The results also indicate that the best savings are obtained when energy-aware memory allocation/deallocation is combined with automatic data migration.

Original languageEnglish (US)
Pages (from-to)156-172
Number of pages17
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - Dec 1 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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