393 research outputs found
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Computing for science, engineering and society: Challenges, requirement, and strategic roadmap
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FABRIC: A National-Scale Programmable Experimental Network Infrastructure
FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities
Analyzing Transatlantic Network Traffic over Scientific Data Caches
Large scientific collaborations often share huge volumes of data around the
world. Consequently a significant amount of network bandwidth is needed for
data replication and data access. Users in the same region may possibly share
resources as well as data, especially when they are working on related topics
with similar datasets. In this work, we study the network traffic patterns and
resource utilization for scientific data caches connecting European networks to
the US. We explore the efficiency of resource utilization, especially for
network traffic which consists mostly of transatlantic data transfers, and the
potential for having more caching node deployments. Our study shows that these
data caches reduced network traffic volume by 97% during the study period. This
demonstrates that such caching nodes are effective in reducing wide-area
network traffic
Assessing How Pre-requisite Skills Affect Learning of Advanced Concepts
Students often struggle with advanced computing courses, and comparatively few studies have looked into the reasons for this. It seems that learners do not master the most basic concepts, or forget them between courses. If so, remedial practice could improve learning, but instructors rightly will not use scarce time for this without strong evidence. Based on personal observation, program tracing seems to be an important pre-requisite skill, but there is yet little research that provides evidence for this observation. To investigate this, our group will create theory-based assessments on how tracing knowledge affects learning of advanced topics, such as data structures, algorithms, and concurrency. This working group will identify relevant concepts in advanced courses, then conceptually analyze their pre-requisites and where an imagined student with some tracing difficulties would encounter barriers. The group will use this theory to create instructor-usable assessments for advanced topics that also identify issues caused by poor pre-requisite knowledge. These assessments may then be used at the start and end of advanced courses to evaluate to what extent students\u2019 difficulties with the advanced course originate from poor pre-requisite knowledge
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