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About

About

I am currently an auxiliar professor at University of Minho and senior researcher at INESC TEC. I have obtained a PhD degree in Computer Science from the MAP-i Doctoral Program in Computer Science. Currently, I am working on large scale distributed systems with an emphasis on storage and database systems’ scalability, performance, security and dependability. Also, I am interested on the applicability of this research work for solving complex data management challenges for Cloud Computing and HPC centres.

I am the coordinator of the PAStor PT-UTAustin exploratory project and the “Efficient and Secure Data Management for HPC and Cloud Computing” CENTRA project, while leading INESC TEC’s activities on the Compete2020 BigHPC project and ACTPM PT-UTAustin exploratory project. Also, I have several publications in renowned journals and international conferences (e.g., ACM Computing Surveys, IEEE Transactions on Computers, ACM Transactions on Storage, Eurosys, SRDS, SYSTOR).

For more information you can check my personal web page at https://jtpaulo.github.io, as well as, HASLab's research lines on the topics mentioned above: https://dsr-haslab.github.io and https://dbr-haslab.github.io

Interest
Topics
Details

Details

  • Name

    João Tiago Paulo
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st November 2011
006
Publications

2023

Distributed Applications and Interoperable Systems - 23rd IFIP WG 6.1 International Conference, DAIS 2023, Held as Part of the 18th International Federated Conference on Distributed Computing Techniques, DisCoTec 2023, Lisbon, Portugal, June 19-23, 2023, Proceedings

Authors
Martínez, MP; Paulo, J;

Publication
DAIS

Abstract

2023

Diagnosing applications' I/O behavior through system call observability

Authors
Esteves, T; Macedo, R; Oliveira, R; Paulo, J;

Publication
CoRR

Abstract

2023

PADLL: Taming Metadata-intensive HPC Jobs Through Dynamic, Application-agnostic QoS Control

Authors
Macedo, R; Miranda, M; Tanimura, Y; Haga, J; Ruhela, A; Harrell, SL; Evans, RT; Pereira, J; Paulo, J;

Publication
CoRR

Abstract

2022

Accelerating Deep Learning Training Through Transparent Storage Tiering

Authors
Dantas, M; Leitao, D; Cui, P; Macedo, R; Liu, XL; Xu, WJ; Paulo, J;

Publication
2022 22ND IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2022)

Abstract
We present MONARCH, a framework-agnostic storage middleware that transparently employs storage tiering to accelerate Deep Learning (DL) training. It leverages existing storage tiers of modern supercomputers (i.e., compute node's local storage and shared parallel file system (PFS)), while considering the I/O patterns of DL frameworks to improve data placement across tiers. MONARCH aims at accelerating DL training and decreasing the I/O pressure imposed over the PFS. We apply MONARCH to TensorFlow and PyTorch, while validating its performance and applicability under different models and dataset sizes. Results show that, even when the training dataset can only be partially stored at local storage, MONARCH reduces TensorFlow's and PyTorch's training time by up to 28% and 37% for I/O-intensive models, respectively. Furthermore, MONARCH decreases the number of I/O operations submitted to the PFS by up to 56%.

2022

PAIO: General, Portable I/O Optimizations With Minor Application Modifications

Authors
Macedo, R; Tanimura, Y; Haga, J; Chidambaram, V; Pereira, J; Paulo, J;

Publication
20th USENIX Conference on File and Storage Technologies, FAST 2022, Santa Clara, CA, USA, February 22-24, 2022

Abstract

Supervised
thesis

2022

Towards a Privacy-Preserving Distributed Machine Learning Framework

Author
Cláudia Vanessa Martins de Brito

Institution
UM

2022

Towards a Dependable and Decentralized Software-Defined Storage Architecture

Author
Ricardo Gonçalves Macedo

Institution
UM

2022

End-to-End Software-Defined Security for Big Data Ecosystem

Author
Tânia da Conceição Araújo Esteves

Institution
UM

2022

Distributed and Dependable SDS Control Plane for HPC

Author
Mariana Martins de Sá Miranda

Institution
UM

2022

Accelerating Deep Learning Training on High-Performance Computing with Storage Tiering

Author
Marco Filipe Leitão Dantas

Institution
UM