arXiv subjects

Juan C. Pichel

Publications and source records attributed to Juan C. Pichel.

6 recordsLinked to original sources

arXiv2025

OMP4Py: a pure Python implementation of OpenMP

Python demonstrates lower performance in comparison to traditional high performance computing (HPC) languages such as C, C++, and Fortran. This performance gap is largely due to Python's interpreted nature and the Global Interpreter Lock (GIL), which hampers multithreading efficiency. However, the latest version of Python includes the necessary changes to make the interpreter thread-safe, allowing Python code to run without the GIL. This important update will enable users to fully exploit multithreading parallelism in Python. In order to facilitate that task, this paper introduces OMP4Py, the first pure Python implementation of OpenMP. We demonstrate that it is possible to bring OpenMP's familiar directive-based parallelization paradigm to Python, allowing developers to write parallel code with the same level of control and flexibility as in C, C++, or Fortran. The experimental evaluation shows that OMP4Py significantly impacts the performance of various types of applications, although the current threading limitation of Python's interpreter (v3.13) reduce its effectiveness for numerical applications.

cs.DC↗

arXiv2025

NetQIR: An Extension of QIR for Distributed Quantum Computing

The rapid advancement of quantum computing has highlighted the need for scalable and efficient software infrastructures to fully exploit its potential. Current quantum processors face significant scalability constraints due to the limited number of qubits per chip. In response, distributed quantum computing (DQC) -- achieved by networking multiple quantum processor units (QPUs) -- is emerging as a promising solution. To support this paradigm, robust intermediate representations (IRs) are needed to translate high-level quantum algorithms into executable instructions suitable for distributed systems. This paper presents NetQIR, an extension of Microsoft's Quantum Intermediate Representation (QIR), specifically designed to facilitate DQC by incorporating new instruction specifications. NetQIR was developed in response to the lack of abstraction at the network and hardware layers identified in the existing literature as a significant obstacle to effectively implementing distributed quantum algorithms. Based on this analysis, NetQIR introduces new essential abstraction features to support compilers in DQC contexts. It defines network communication instructions independent of specific hardware, abstracting the complexities of inter-QPU communication. Leveraging the QIR framework, NetQIR aims to bridge the gap between high-level quantum algorithm design and low-level hardware execution, thus promoting modular and scalable approaches to quantum software infrastructures for distributed applications.

quant-ph↗

arXiv2025

Evaluating Search Engines and Large Language Models for Answering Health Questions

Search engines (SEs) have traditionally been primary tools for information seeking, but the new Large Language Models (LLMs) are emerging as powerful alternatives, particularly for question-answering tasks. This study compares the performance of four popular SEs, seven LLMs, and retrieval-augmented (RAG) variants in answering 150 health-related questions from the TREC Health Misinformation (HM) Track. Results reveal SEs correctly answer between 50 and 70% of questions, often hindered by many retrieval results not responding to the health question. LLMs deliver higher accuracy, correctly answering about 80% of questions, though their performance is sensitive to input prompts. RAG methods significantly enhance smaller LLMs' effectiveness, improving accuracy by up to 30% by integrating retrieval evidence.

cs.IR↗

arXiv2024

Review of Distributed Quantum Computing. From single QPU to High Performance Quantum Computing

The emerging field of quantum computing has shown it might change how we process information by using the unique principles of quantum mechanics. As researchers continue to push the boundaries of quantum technologies to unprecedented levels, distributed quantum computing raises as an obvious path to explore with the aim of boosting the computational power of current quantum systems. This paper presents a comprehensive survey of the current state of the art in the distributed quantum computing field, exploring its foundational principles, landscape of achievements, challenges, and promising directions for further research. From quantum communication protocols to entanglement-based distributed algorithms, each aspect contributes to the mosaic of distributed quantum computing, making it an attractive approach to address the limitations of classical computing. Our objective is to provide an exhaustive overview for experienced researchers and field newcomers.

quant-ph↗

arXiv2021

A unified framework to improve the interoperability between HPC and Big Data languages and programming models

One of the most important issues in the path to the convergence of HPC and Big Data is caused by the differences in their software stacks. Despite some research efforts, the interoperability between their programming models and languages is still limited. To deal with this problem we introduce a new computing framework called IgnisHPC, whose main objective is to unify the execution of Big Data and HPC workloads in the same framework. IgnisHPC has native support for multi-language applications using JVM and non-JVM-based languages. Since MPI was used as its backbone technology, IgnisHPC takes advantage of many communication models and network architectures. Moreover, MPI applications can be directly executed in a efficient way in the framework. The main consequence is that users could combine in the same multi-language code HPC tasks (using MPI) with Big Data tasks (using MapReduce operations). The experimental evaluation demonstrates the benefits of our proposal in terms of performance and productivity with respect to other frameworks such as Apache Spark. IgnisHPC is publicly available for the Big Data and HPC research community.

cs.DC↗

arXiv2018

Polypus: a Big Data Self-Deployable Architecture for Microblogging Text Extraction and Real-Time Sentiment Analysis

In this paper we propose a new parallel architecture based on Big Data technologies for real-time sentiment analysis on microblogging posts. Polypus is a modular framework that provides the following functionalities: (1) massive text extraction from Twitter, (2) distributed non-relational storage optimized for time range queries, (3) memory-based intermodule buffering, (4) real-time sentiment classification, (5) near real-time keyword sentiment aggregation in time series, (6) a HTTP API to interact with the Polypus cluster and (7) a web interface to analyze results visually. The whole architecture is self-deployable and based on Docker containers.

cs.DC↗