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Publications

Publications by HASLab

2023

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
2023 IEEE/ACM 23RD INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING, CCGRID

Abstract
Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.

2023

Towards MRAM Byte-Addressable Persistent Memory in Edge Database Systems

Authors
Ferreira, LM; Coelho, F; Pereira, JO;

Publication
Joint Proceedings of Workshops at the 49th International Conference on Very Large Data Bases (VLDB 2023), Vancouver, Canada, August 28 - September 1, 2023.

Abstract
There is a growing demand for persistent data in IoT, edge and similar resource-constrained devices. However, standard FLASH memory-based solutions present performance, energy, and reliability limitations in these applications. We propose MRAM persistent memory as an alternative to FLASH based storage. Preliminary experimental results show that its performance, power consumption, and reliability in typical database workloads is competitive for resource-constrained devices. This opens up new opportunities, as well as challenges, for small-scale database systems. MRAM is tested for its raw performance and applicability to key-value and relational database systems on resource-constrained devices. Improvements of as much as three orders of magnitude in write performance for key-value systems were observed in comparison to an alternative NAND FLASH based device. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

2023

Paraconsistent Transition Systems

Authors
Cruz, A; Madeira, A; Barbosa, LS;

Publication
ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE

Abstract
Often in Software Engineering a modelling formalism has to support scenarios of inconsistency in which several requirements either reinforce or contradict each other. Paraconsistent transition systems are proposed in this paper as one such formalism: states evolve through two accessibility relations capturing weighted evidence of a transition or its absence, respectively. Their weights come from a specific residuated lattice. A category of these systems, and the corresponding algebra, is defined providing a formal setting to model different application scenarios. One of them, dealing with the effect of quantum decoherence in quantum programs, is used for illustration purposes.

2023

Policy gradients using variational quantum circuits

Authors
Sequeira, A; Santos, LP; Barbosa, LS;

Publication
QUANTUM MACHINE INTELLIGENCE

Abstract
Variational quantum circuits are being used as versatile quantum machine learning models. Some empirical results exhibit an advantage in supervised and generative learning tasks. However, when applied to reinforcement learning, less is known. In this work, we considered a variational quantum circuit composed of a low-depth hardware-efficient ansatz as the parameterized policy of a reinforcement learning agent. We show that an epsilon-approximation of the policy gradient can be obtained using a logarithmic number of samples concerning the total number of parameters. We empirically verify that such quantum models behave similarly to typical classical neural networks used in standard benchmarking environments and quantum control, using only a fraction of the parameters. Moreover, we study the barren plateau phenomenon in quantum policy gradients using the Fisher information matrix spectrum.

2023

Stepwise Development of Paraconsistent Processes

Authors
Cunha, J; Madeira, A; Barbosa, LS;

Publication
Theoretical Aspects of Software Engineering - 17th International Symposium, TASE 2023, Bristol, UK, July 4-6, 2023, Proceedings

Abstract

2023

Weighted synchronous automata

Authors
Gomes, L; Madeira, A; Barbosa, LS;

Publication
MATHEMATICAL STRUCTURES IN COMPUTER SCIENCE

Abstract
This paper introduces a class of automata and associated languages, suitable to model a computational paradigm of fuzzy systems, in which both vagueness and simultaneity are taken as first-class citizens. This requires a weighted semantics for transitions and a precise notion of a synchronous product to enforce the simultaneous occurrence of actions. The usual relationships between automata and languages are revisited in this setting, including a specific Kleene theorem.

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