Detalhes bibliográficos
| Resumo: | The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency. |
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| Autores principais: | Saraiva, Bruno M. |
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| Outros Autores: | Cunha, Inês; Brito, António D.; Follain, Gautier; Portela, Raquel; Haase, Robert; Pereira, Pedro M.; Jacquemet, Guillaume; Henriques, Ricardo |
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| Assunto: | Biotechnology Biochemistry Molecular Biology Cell Biology |
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| Ano: | 2025 |
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| País: | Portugal |
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| Tipo de documento: | artigo |
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| Tipo de acesso: | acesso aberto |
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| Instituição associada: | Universidade Nova de Lisboa |
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| Idioma: | inglês |
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| Origem: | Repositório Institucional da UNL |
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