Saraiva Bruno M, Cunha Inês, Brito António D, Follain Gautier, Portela Raquel, Haase Robert, Pereira Pedro M, Jacquemet Guillaume, Henriques Ricardo
Instituto Gulbenkian de Ciência, Oeiras, Portugal.
Gulbenkian Institute for Molecular Medicine, Oeiras, Portugal.
Nat Methods. 2025 Feb;22(2):283-286. doi: 10.1038/s41592-024-02562-6. Epub 2025 Jan 2.
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.
显微镜图像数据集规模的不断扩大和复杂性的日益增加,需要加速分析工作流程。NanoPyx通过一个为高速分析而增强的自适应框架满足了这一需求。在NanoPyx的核心部分,Liquid Engine动态生成优化的中央处理器和图形处理器代码变体,根据输入数据和硬件学习并预测最快的代码。这种数据驱动的优化实现了显著更快的处理速度,在需要效率的反应显微镜和计算领域具有广泛的相关性。