2,133 research outputs found
Coordinating a team of agents in the forest firefighting domain
Documento confidencial. Não pode ser disponibilizado para consultaTese de mestrado. Inteligência Artificial e Sistemas Inteligentes. 2006. Faculdade de Engenharia. Universidade do Port
Three-Dimensional Biplanar Reconstruction of the Scoliotic Spine for Standard Clinical Setup
Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201
Learning to Rank Graph-based Application Objects on Heterogeneous Memories
Persistent Memory (PMEM), also known as Non-Volatile Memory (NVM), can
deliver higher density and lower cost per bit when compared with DRAM. Its main
drawback is that it is typically slower than DRAM. On the other hand, DRAM has
scalability problems due to its cost and energy consumption. Soon, PMEM will
likely coexist with DRAM in computer systems but the biggest challenge is to
know which data to allocate on each type of memory. This paper describes a
methodology for identifying and characterizing application objects that have
the most influence on the application's performance using Intel Optane DC
Persistent Memory. In the first part of our work, we built a tool that
automates the profiling and analysis of application objects. In the second
part, we build a machine learning model to predict the most critical object
within large-scale graph-based applications. Our results show that using
isolated features does not bring the same benefit compared to using a carefully
chosen set of features. By performing data placement using our predictive
model, we can reduce the execution time degradation by 12\% (average) and 30\%
(max) when compared to the baseline's approach based on LLC misses indicator
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