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Sobre

Sobre

João M. P. Cardoso obteve o grau de Doutor em Engenharia Electrotécnica e Computadores no IST/UTL (Instituto Superior Técnico/Universidade Técnica de Lisboa), Lisboa, Portugal, em 2001. É actualmente Professor Catedrático no Departamento de Engenharia Informática (DEI) da Faculdade de Engenharia da Universidade do Porto (FEUP) e investigador sénior no INESC TEC. Previamente, ele foi Prof. Auxiliar no IST/UTL (2006-2008), investigador sénior no INESC-ID (2001-2009), e Prof. Auxiliar na Universidade do Algarve (1993-2006). Em 2001/2002, trabalhou na PACT XPP Technologies, Inc., em Munique, Alemanha. Tem estado envolvido na organização e tem servido como membro do comité científico de muitas conferências internacionais. Por exemplo, foi General Co-Chair da IEEE/IFIP EUC’2015 e da IEEE CSE’2015, General Chair da FPL’2013, General Co-Chair da ARC’2014 e ARC’2006, Program Co-Chair da ARCS’2016, DASIP’2014, e RAW’2010. É co-autor de mais de 150 publicações científicas em tópicos relacionados com compiladores, sistemas embebidos, e computação reconfigurável. Coordenou vários projectos de investigação. É um membro sénior do IEEE e do ACM e membro da IEEE Computer Society. Os seus interesses de investigação incluem técnicas de compiladores, linguages específicas ao domínio, computação reconfigurável, arquitecturas específicas à aplicação, e computação de elevado desempenho com ênfase em computação embebida.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Paiva Cardoso
  • Cargo

    Investigador Sénior
  • Desde

    01 julho 2011
002
Publicações

2023

Electrical sensing of the plant Mimosa pudica under environmental temperatures

Autores
Lobo, MA; Cardoso, JMP; Rocha, PRF;

Publicação
2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Abstract
Plants gather and process information about their surroundings to make decisions that prioritize their well-being while considering the environment. These decisions are conveyed through electrical signals within and between cells, mainly in the form of action and variation potentials, in response to stimuli, including mechanical vibrations, changes in temperature, light intensity, and humidity. Although the ability of some plants, such as the Mimosa pudica, to react to sudden environmental stimuli (e.g., touch) is well known, their long-term electrical response under slow environmental changes remains not fully understood. Here, a multi-source monitoring system has been developed to collect and store electrical signals from the plant Mimosa pudica, and surrounding environmental temperature and humidity, over a period of approximately 5 days. A realtime dashboard shows the environmental temperature and variation potential (VP) from Mimosa pudica. The VP mimics the environmental temperature changes, with an associated delay. Our long-term physiological observations suggest that environmental temperature sensing in the plant Mimosa pudica can be monitored and is likely driven by bioelectricity.

2023

A Study on Hyperparameters Configurations for an Efficient Human Activity Recognition System

Autores
Ferreira, PJS; Moreira, JM; Cardoso, JMP;

Publicação
CoRR

Abstract

2023

Preface ASAP 2023

Autores
Cardoso, JMP; Jimborean, A; Mentens, N; Coutinho, JGF;

Publicação
34th IEEE International Conference on Application-specific Systems, Architectures and Processors, ASAP 2023, Porto, Portugal, July 19-21, 2023

Abstract
[No abstract available]

2022

Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

Autores
Pinto, P; Bispo, J; Cardoso, J; Barbosa, JG; Gadioli, D; Palermo, G; Martinovic, J; Golasowski, M; Slaninova, K; Cmar, R; Silvano, C;

Publicação
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

Abstract
Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces (APIs), compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

2021

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

Autores
Vitali, E; Gadioli, D; Palermo, G; Golasowski, M; Bispo, J; Pinto, P; Martinovic, J; Slaninova, K; Cardoso, JMP; Silvano, C;

Publicação
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING

Abstract
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 selecting dynamically 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 make decisions on tuning the number of samples to improve the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36 and 81 percent) with respect to a static approach, while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, the execution-time speedup is between 1.5x and 5.1x. This speedup is reflected at the infrastructure-level in terms of a reduction of 36 percent of the computing resources needed to support the whole navigation pipeline.

Teses
supervisionadas

2022

Automatic Selection of Software Code Regions for Migrating to GPUs

Autor
Fábio Daniel Reis Gaspar

Instituição
UP-FEUP

2022

Runtime-aware Compiler Optimizations for High-Performance Embedded Computing

Autor
Pedro Miguel dos Santos Pinto

Instituição
UP-FEUP

2022

Strategies for Compiler Phase Ordering Targeting CPUs

Autor
João Miguel Araújo Monteiro da Rocha

Instituição
UP-FEUP

2022

Programming FPGAs Using Task-Graphs and C code

Autor
Luís Miguel Jardim Noites

Instituição
UP-FEUP

2022

Energy-Computing Efficient Classification Techniques for Mobile-Based HAR Systems

Autor
Paulo Jorge Silva Ferreira

Instituição
UP-FEUP