Joao M. P. Cardoso | Searcharxiv

arXiv2019

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline.

cs.DC↗

arXiv2014

Representation of Evolutionary Algorithms in FPGA Cluster for Project of Large-Scale Networks

Many problems are related to network projects, such as electric distribution, telecommunication and others. Most of them can be represented by graphs, which manipulate thousands or millions of nodes, becoming almost an impossible task to obtain real-time solutions. Many efficient solutions use Evolutionary Algorithms (EA), where researches show that performance of EAs can be substantially raised by using an appropriate representation, such as the Node-Depth Encoding (NDE). The objective of this work was to partition an implementation on single-FPGA (Field-Programmable Gate Array) based on NDE from 512 nodes to a multi-FPGAs approach, expanding the system to 4096 nodes.

cs.DC↗

arXiv2007

An Infrastructure to Functionally Test Designs Generated by Compilers Targeting FPGAs

This paper presents an infrastructure to test the functionality of the specific architectures output by a high-level compiler targeting dynamically reconfigurable hardware. It results in a suitable scheme to verify the architectures generated by the compiler, each time new optimization techniques are included or changes in the compiler are performed. We believe this kind of infrastructure is important to verify, by functional simulation, further research techniques, as far as compilation to Field-Programmable Gate Array (FPGA) platforms is concerned.

cs.AR↗