Memory Management Methodology for Application Data Structure Refinement and Placement on Heterogeneous DRAM/NVM Systems
Manolis Katsaragakis1,2,a, Lazaros Papadopoulos1,b, Christos Baloukas1,c and Dimitrios Soudris1,d
1Microprocessors and Digital Systems Laboratory, ECE , National Technical University of Athens, Greece
2Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee, Belgium
amkatsaragakis@microlab.ntua.gr
blpapadop@microlab.ntua.gr
ccmpalouk@microlab.ntua.gr
ddsoudris@microlab.ntua.gr
ABSTRACT
The emergence of memory systems that combine multiple memory technologies with alternative performance and energy characteristics are becoming mainstream. Existing data placement strategies evolve to map application requirements to the underlying heterogeneous memory systems. In this work, we propose a memory management methodology that leverages a data structure refinement approach to improve data placement results, in terms of execution time and energy consumption. The methodology is evaluated on three machine learning algorithms deployed on various NVM technologies, both on emulated and on real DRAM/NVM systems. Results show execution time improvement up to 57% and energy consumption gains up to 41%.
