The trends in virtualization as well as multi-core, multiprocessor environments
have translated to a massive increase in the amount of main memory each individual
system needs to be fitted with, so as to effectively utilize this growing compute capacity.
The increasing demand on main memory implies that the main memory devices and their
issues are as important a part of system design as the central processors. The primary
issues of modern memory are power, energy, and scaling of capacity. Nearly a third of
the system power and energy can be from the memory subsystem. At the same time,
modern main memory devices are limited by technology in their future ability to scale
and keep pace with the modern program demands thereby requiring exploration of
alternatives to main memory storage technology. This dissertation exploits dynamic
knowledge of memory state and memory data value to improve memory performance and
reduce memory energy consumption.
A cross-boundary approach to communicate information about dynamic memory
management state (allocated and deallocated memory) between software and hardware
viii
memory subsystem through a combination of ISA support and hardware structures is
proposed in this research. These mechanisms help identify memory operations to regions
of memory that have no impact on the correct execution of the program because they
were either freshly allocated or deallocated. This inference about the impact stems from
the fact that, data in memory regions that have been deallocated are no longer useful to
the actual program code and data present in freshly allocated memory is also not useful to
the program because the dynamic memory has not been defined by the program. By
being cognizant of this, such memory operations are avoided thereby saving energy and
improving the usefulness of the main memory. Furthermore, when stores write zeros to
memory, the number of stores to the memory is reduced in this research by capturing it as
compressed information which is stored along with memory management state
information.
Using the methods outlined above, this dissertation harnesses memory
management state and data value information to achieve significant savings in energy
consumption while extending the endurance limit of memory technologies. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2011-05-3569 |
| Date | 01 June 2011 |
| Creators | Isen, Ciji |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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