Title
Platform-aware dynamic data type refinement methodology for radix tree Data Structures
Abstract
Modern embedded systems are now capable of executing complex and demanding applications that are often based on large data structures. The design of the critical data structures of the application affects the performance and the memory requirements of the whole system. Dynamic Data Structure Refinement methodology provides optimizations, mainly in list and array data structures, which are based on the application's features and access patterns. In this work, we extend various aspects of the methodology: First, we integrate radix tree optimizations. Then, we provide a set of platform-aware data structure implementations, for performing optimizations based on the hardware features. The extended methodology is evaluated using a wide set of synthetic and real-world benchmarks, in which we achieved performance and memory trade-offs up to 29.6%. Additionally, Pareto optimal data structure implementations that were not available by the previous methodology, are identified with the extended one.
Year
DOI
Venue
2015
10.1109/SAMOS.2015.7363662
2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)
Keywords
DocType
Citations 
real-world benchmarks,synthetic benchmarks,hardware features,platform-aware data structure implementations,radix tree optimizations,access patterns,application features,array data structures,list data structures,dynamic data structure refinement methodology,embedded systems,radix tree data structures,platform-aware dynamic data type refinement methodology
Conference
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Thomas Papastergiou100.34
Lazaros Papadopoulos2298.99
Dimitrios Soudris324348.41