Browsing by Subject "High-performance computing"

Now showing 1 - 2 of 2
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    Open Access
    A programmable web platform for distributed access, analysis, and visualization of data
    (Elsevier, 2023-10-26) Esquembre, Francisco; Chacón, Jesús; Saenz, Jacobo; Vega, Jesús; Dormido-Canto, Sebastián; Matemáticas
    Daily work of Fusion Data Research (FDR) scientists faces three practical challenges: (i) getting access to vast amounts of validated, curated, and (ideally) annotated discharge data, (ii) applying a wide variety of standard, domain-specific, and home-made analysis and visualization software libraries and routines, and (iii) using fast, specialized, and not easy to obtain hardware and software installations. This paper introduces a novel web platform that addresses these three challenges in a federated way. Based on a client–server architecture, the new platform allows for easy use and exchange of curated data, validated analysis and visualization routines, and even networked hardware and software installations among the FDR community. This exchange goes beyond the mere use of a code repository, but facilitates the creation of an actual ready-to-use network of computers which can be used remotely to configure and perform data analysis. The network functions in a federated way, in which each member of the community contributes, using the same web platform, with its data, programming experience, and hardware and software availability. The platform is open source.
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    Publication
    Open Access
    Code Detection for Hardware Acceleration Using Large Language Models
    (2024-03-01) Martínez Sánchez, Pablo Antonio; Bernabé García, Gregorio; García Carrasco, José Manuel; Ingeniería y Tecnología de Computadores
    Large language models (LLMs) have been massively applied to many tasks, often surpassing state-of-the-art approaches. While their effectiveness in code generation has been extensively studied (e.g., AlphaCode), their potential for code detection remains unexplored. This work presents the first analysis of code detection using LLMs. Our study examines essential kernels, including matrix multiplication, convolution, fast-fourier transform and LU factorization, implemented in C/C++. We propose both a preliminary, naive prompt and a novel prompting strategy for code detection. Results reveal that conventional prompting achieves great precision but poor accuracy (67.5%, 22.5%, 79.5% and 64% for GEMM, convolution, FFT and LU factorization, respectively) due to a high number of false positives. Our novel prompting strategy substantially reduces false positives, resulting in excellent overall accuracy (91.2%, 98%, 99.7% and 99.7%, respectively). These results pose a considerable challenge to existing state-of-the-art code detection methods.