Computing servers for NLP applications

这个RTI应用程序旨在为RALI计算语言学和应用实验室提供我们研究所需的足够的计算资源。RALI实验室专门研究自然语言处理（NLP）及其应用。我们的研究涉及与自然语言处理相关的基础和应用问题：我们如何理解语言以及如何在不同的应用中使用自然语言。我们的研究工作涵盖了信息检索（搜索引擎）、信息抽取、问答、对话系统、推荐系统、社交媒体挖掘、医学信息检索与问答等众多主题。所有这些应用在工业中都有很高的需求，在我们的社会中更普遍。RALI实验室一直在进行高质量的研究工作，得到同行的广泛认可。我们在该领域的顶级会议和期刊上发表文章，我们的研究工作在社区中产生了重大影响（例如，Test of Time在信息检索领域获得ACM SIGIR荣誉奖）。然而，我们目前的设备已经不足以支持我们在自然语言处理和应用方面的研究。近年来，该领域朝着基于深度学习和深度神经网络的方法迈出了巨大的一步，这些方法利用了大量的文本数据和大型预训练模型（从BERT的0.34亿个参数到GPT-3的1750亿个参数）。这些模型需要在GPU服务器上进行密集计算。NLP领域的竞争比以往任何时候都更加激烈，不仅需要创新的想法，还需要强大的计算资源。虽然大多数顶级NLP实验室已经更新了他们的计算机服务器以满足需求，但RALI实验室远远落后：我们只有少数带有GPU内核的工作站，由3名教职员工，3名研究助理和40多名研究生共享，其中大多数人使用神经模型并需要GPU。简陋的设备严重延误了我们的研究和出版工作。所要求的设备- GPU服务器和存储服务器，将使我们在NLP研究中保持竞争力，并为HQP培训提供足够的环境。这些设备还将帮助我们吸引更多的优秀学生，特别是女学生和少数族裔学生，对他们来说，计算环境是选择实验室学习的重要标准。

This RTI application aims to equip the RALI lab on Computational Linguistics and Applications (Recherche appliquée en linguistique informatique) with adequate computation resources necessary for our research. The RALI lab is specialized in natural language processing (NLP) and applications. Our research addresses both fundamental and application questions related to NLP: how can we understand languages and how can we use natural languages in different applications. Our research work covers a large number of topics such as information retrieval (search engine), information extraction, question answering, dialogue systems, recommendation systems, social media mining, medical information retrieval and question answering, and so on. All these applications are highly in demand in industry, and more generally in our society. The RALI lab has been performing high-quality research work, widely recognized by the peers. We publish at the very top conferences and journals in this domain and our research work has made significant impact in the community (e.g. Test of Time honorable mention of ACM SIGIR in information retrieval). However, our current equipment is no longer adequate to support our research in NLP and applications. The domain has made a drastic move in recent years toward methods based on deep learning and deep neural networks, that exploit huge amounts of textual data and large pre-trained models (from 0.34 billions parameter for BERT to 175 billion for GPT-3). Intensive computation on GPU servers is required to benefit from these models. The NLP domain is becoming more competitive than ever, in the sense that not only innovative ideas, but also powerful computation resources, are required. While most top NLP labs have updated their computer servers to meet the need, the RALI lab is well behind: we only have a small number of workstations with GPU cores shared by 3 faculty members, 3 research associates and over 40 graduate students, most of whom use neural models and require GPUs. The poor equipment is causing serious delays in our research and publications. The requested equipment - GPU server and storage server, will allow us to remain competitive in NLP research and offer adequate environment for HQP training. The equipment will also help us attract more good students, especially female students and students from visible minorities, for whom the computation environment is an important criterion when choosing a lab for their studies.