使用scigate科学门户加速科学发现

C. Jiang, David Ojika, Bhavesh Patel, A. Gordon-Ross, H. Lam
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引用次数: 2

摘要

由于最近利用人工智能(AI)的科学突破推动了各种数据中心应用的不断扩大,对计算加速器(gpu, fpga, asic等)的需求正在增长。由于人工智能的广泛影响,这些应用(如宇宙学、物理学等)在预测能力方面继续保持着破纪录的准确性,但将这些应用从研究实验室引入生产和业务用例的基础设施和工作流程仍然滞后。为了解决这些重要的基础设施挑战,我们提出了SCAIGATE,一个具有简化工作流程的原型科学网关,旨在促进大规模科学应用中的模型构建/验证工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating Scientific Discovery with SCAIGATE Science Gateway
The demand for computational accelerators (GPUs, FPGAs, ASICs, etc.) is growing due to the widening variety of datacenter applications fueled by recent scientific breakthroughs that leverage artificial intelligence (AI). As much as these applications (e.g., cosmology, physics, etc.) have continued to witness record-breaking accuracy in predictive capabilities due to AI widespread influence, the infrastructure and workflow to take these applications out of research labs into production and business use-cases continues to lag. To address these important infrastructural challenges, we present SCAIGATE, a prototype science gateway with a simplified workflow aimed at facilitating model building/validation workflows in large-scale scientific applications.
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