REU Site: EXERCISE - Explore Emerging Computing in Science and Engineering

该项目是索尔兹伯里大学(SU)未来三年本科生研究体验(REU)练习(探索科学和工程中的新兴计算)网站的更新。练习是一个跨学科项目，探索并行计算中的新兴范例，以及科学和工程中的数据和计算密集型应用。该项目的目标是为学生参与者，特别是来自本科生机构(PUI)的学生提供宝贵的并行计算研究经验。该项目将促进“并行思维”，这是一种重要的计算思维技能，引导当代学生进入21世纪的计算时代。该网站将优先招收代表不足的学生和女性，并吸引来自马里兰州东海岸当地历史悠久的黑人学院和大学(HBCU)、PUI和社区学院的学生进入计算科学和工程专业以及普通科学、技术、工程和数学(STEM)领域。首席研究员将与教师导师一起监督一个为期10周的REU项目，该项目将让不同的学生品尝并行计算领域的计算思维，并了解研究生院的经验。主办机构SU将与马里兰东岸大学、HBCU和马里兰大学学院公园合作，提供多学科的教师专业知识和多样化的暑期活动，包括实地考察、社会活动、高中外展和研究生院申请信息会议。在传统的冯·诺伊曼计算机体系结构中处理复杂信息和大数据变得越来越困难。计算机正从根本上转向并发体系结构，如超线程、多核和多核体系结构。与这些并发体系结构相适应的新兴并行和分布式计算范例已经开始在广泛的应用中展示出解决大数据集和高计算复杂性问题的能力。然而，在与进程交织相关的程序语义中存在着根本的困难：由于并发任务之间不可预测的交互，并行程序可能产生不一致的结果，甚至崩溃。其次，通信、内存访问和I/O开销可能会导致运行时延迟。最后，很难确保程序以同时实现效率和满足性能目标的方式消耗资源。REU网站将重点关注并行计算的四个方面，即：算法、软件、体系结构和应用程序，以应对这些并行计算挑战。学生将与教师导师一起完成尖端研究项目，以处理强调上述四个方面的数据和计算密集型应用程序。到课程结束时，学生将获得宝贵的技能，对研究有更广泛和更深入的理解，并对自己的能力培养更大的信心。特别是，他们将接触到并行计算的新兴范例，如Map-Reduced和图形处理单元计算，并将有机会探索并行软件和多处理器体系结构，设计有效的并行算法，并处理计算机和社会网络、图像和自然语言处理、模式识别和机器学习中的数据和计算密集型问题。

The project is a renewal of the Research Experiences for Undergraduates (REU) EXERCISE (Explore Emerging Computing in Science and Engineering) site at Salisbury University (SU) for the next three years. EXERCISE is an interdisciplinary project that explores emerging paradigms in parallel computing with data and compute intensive applications in science and engineering. The goal of the project is to offer student participants, particularly from primarily undergraduate institutions (PUIs), a valuable research experience in parallel computing. The project will promote "parallel thinking", an important computational thinking skill guiding current generation students into the twenty-first century computing era. The site will prioritize recruiting under-represented students and females, and attract students from local historically black college and universities (HBCUs), PUIs, and community colleges on Maryland's Eastern Shore into computational science and engineering majors and the general Science, Technology, Engineering, and Mathematics (STEM) fields. The Principal Investigator, together with faculty mentors, will supervise a 10-week REU program that gives a diverse cohort of students a taste of computational thinking in the domain of parallel computing and also an understanding of the graduate school experience. The host institution SU will collaborate with the University of Maryland Eastern Shore, an HBCU, and the University of Maryland College Park for multi-disciplinary faculty expertise and diverse summer activities including field trips, social activities, high school outreach, and graduate school application information sessions.Processing complex information and large data in conventional von Neumann computer architectures is becoming increasingly difficult. Computers are undertaking a fundamental turn toward concurrency architectures such as hyperthreading, multi-core, and many-core architectures. Emerging parallel and distributed computing paradigms adapted to these concurrent architectures have begun to demonstrate the power of solving problems with large datasets and high computational complexity in a wide range of applications. However, there are fundamental difficulties in program semantics related to process interleaving: a parallel program can yield inconsistent answers, or even crash, due to unpredictable interactions between simultaneous tasks. Secondly, communication, memory access, and I/O overhead may result in run-time delays. Finally, it is difficult to ensure that programs consume resources in a manner that simultaneously achieves efficiency and meets performance goals. The REU Site will focus on four aspects of parallel computing, namely: algorithms, software, architecture and applications to address these parallel computing challenges. Students will work with faculty mentors in completing cutting-edge research projects to tackle data and compute intensive applications that emphasize the above four aspects. By the end of program, students will acquire valuable skills, gain a broader and deeper understanding of research, and develop greater confidence in their abilities. In particular, they will be exposed to emerging paradigms in parallel computing such as Map-Reduce and Graphical Processing Unit computing, and will have opportunities to explore concurrent software and multiprocessor architectures, and design efficient parallel algorithms, and to tackle data and compute intensive problems in computer and social networks, image and natural language processing, pattern recognition and machine learning.