Výsledky vyhledávání - "Ayguadé Parra, Eduard"

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Aloe: A Family of Fine-tuned Open Healthcare LLMs

Autor: Gururajan, Ashwin Kumar, Lopez-Cuena, Enrique, Bayarri-Planas, Jordi, Tormos, Adrian, Hinjos, Daniel, Bernabeu-Perez, Pablo, Arias-Duart, Anna, Martin-Torres, Pablo Agustin, Urcelay-Ganzabal, Lucia, Gonzalez-Mallo, Marta, Alvarez-Napagao, Sergio, Ayguadé-Parra, Eduard, Garcia-Gasulla, Ulises Cortés Dario

As the capabilities of Large Language Models (LLMs) in healthcare and medicine continue to advance, there is a growing need for competitive open-source models that can safeguard public interest. With the increasing availability of highly competitive

Externí odkaz: http://arxiv.org/abs/2405.01886

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Mitigating the NUMA effect on task-based runtime systems

Autor: Maroñas Bravo, Marcos, Navarro Muñoz, Antoni, Ayguadé Parra, Eduard, Beltran Querol, Vicenç

Publikováno v: The Journal of Supercomputing.

Processors with multiple sockets or chiplets are becoming more conventional. These kinds of processors usually expose a single shared address space. However, due to hardware restrictions, they adopt a NUMA approach, where each processor accesses loca

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abd86186972399f62bedce017ddf0f2e
https://doi.org/10.1007/s11227-023-05164-9

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Automatic aggregation of subtask accesses for nested OpenMP-style tasks

Autor: Ali, Omar Shaaban Ibrahim, Aguilar Mena, Jimmy, Beltran Querol, Vicenç, Carpenter, Paul Matthew, Ayguadé Parra, Eduard, Labarta Mancho, Jesús José

Publikováno v: 2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD).

Task-based programming is a high performance and productive model to express parallelism. Tasks encapsulate work to be executed across multiple cores or offloaded to GPUs, FPGAs, other accelerators or other nodes. In order to maintain parallelism and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd232db6bcdc58f97165b4ca87314b1c
https://doi.org/10.1109/sbac-pad55451.2022.00042

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b8c: an FPGA-friendly sparse matrix representation suitable for the SpMV kernel

Autor: Oliver, José, Ayguadé Parra, Eduard, Martorell Bofill, Xavier, Álvarez, Carlos

Sparse Matrix-Vector multiplication (SpMV), computing y = Ax where y and x are dense vectors and A is a sparse matrix, is a key kernel in many HPC applications. SpMV exhibits a kind of memory access that is extremely hard to perform efficiently, due

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3484::c845db0e119ce7de468e7a37438a560b
https://hdl.handle.net/2117/383678

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Cost-aware prediction of uncorrected DRAM errors in the field

Autor: Boixaderas, Isaac, Carpenter, Paul Matthew, Radojković, Petar, Ayguadé Parra, Eduard

One of the main causes of hardware failure in large-scale clusters is an uncorrected error in main memory [1]–[4]. Node failures are especially problematic in high-performance computing (HPC), where a single tightly-coupled job may execute for days

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3484::c8a7c20d25bebbc4a8d98ab948494313
https://hdl.handle.net/2117/346246

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Towards resilient EU HPC systems: A blueprint

Autor: Radojković, Petar, Marazakis, Manolis, Carpenter, Paul Matthew, Jeyapaul, Reiley, Gizopoulos, Dimitris, Schulz, Martin, Armejach Sanosa, Adrià, Ayguadé Parra, Eduard, Canal Corretger, Ramon, Moreto Planas, Miquel, Salami, Behzad, Unsal, Osman Sabri

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

This document aims to spearhead a Europe-wide discussion on HPC system resilience and to help the European HPC community define best practices for resilience. We analyse a wide range of state-of-the-art resilience mechanisms and recommend the most ef

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3e45e3309e35a2378d499558f1be92d3
http://hdl.handle.net/2117/330695

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MetH: A family of high-resolution and variable-shape image challenges

Autor: Parés Pont, Ferran, Garcia Gasulla, Dario|||0000-0001-6732-5641, Servat, Harald, Labarta Mancho, Jesús José|||0000-0002-7489-4727, Ayguadé Parra, Eduard|||0000-0002-5146-103X

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

High-resolution and variable-shape images have not yet been properly addressed by the AI community. The approach of down-sampling data often used with convolutional neural networks is sub-optimal for many tasks, and has too many drawbacks to be consi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::69697628f7574305d23d07d3b6255e34
http://hdl.handle.net/2117/193028

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Random forest as a tumour genetic marker extractor

Autor: Pérez Arnal, Raquel Leandra|||0000-0002-0041-8146, Garcia Gasulla, Dario|||0000-0001-6732-5641, Torrents Rodas, David, Pares, Ferran, Cortés García, Claudio Ulises|||0000-0003-0192-3096, Labarta Mancho, Jesús José|||0000-0002-7489-4727, Ayguadé Parra, Eduard|||0000-0002-5146-103X

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development. In this paper, we explore a recently released dataset of chromosome rearrangements in 2,586 cancer patients, where differe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::8fa5fd1c7318cd56fc2ee8ba880bb192
https://hdl.handle.net/2117/186309

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Full-Network Embedding in a Multimodal Embedding Pipeline

Autor: Vilalta Arias, Armand, Garcia Gasulla, Dario|||0000-0001-6732-5641, Parés Pont, Ferran, Moreno Vázquez, Jonatan, Ayguadé Parra, Eduard|||0000-0002-5146-103X, Labarta Mancho, Jesús José|||0000-0002-7489-4727, Cortés García, Claudio Ulises|||0000-0003-0192-3096, Suzumura, Toyotaro

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

The current state-of-the-art for image annotation and image retrieval tasks is obtained through deep neural networks, which combine an image representation and a text representation into a shared embedding space. In this paper we evaluate the impact

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d281f020c9ad0066bd66f00939530df
http://arxiv.org/abs/1707.09872

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