1,672 research outputs found
REU Site: Supercomputing Undergraduate Program in Maine (SuperMe)
This award, for a new Research Experience for Undergraduates (REU) site, builds a Supercomputing Undergraduate Program in Maine (SuperMe). This new site provides ten-week summer research experiences at the University of Maine (UMaine) for ten undergraduates each year for three years. With integrated expertise of ten faculty researchers from both computer systems and domain applications, SuperMe allows each undergraduate to conduct meaningful research, such as developing supercomputing techniques and tools, and solving cutting-edge research problems through parallel computing and scientific visualization. Besides being actively involved in research groups, students attend weekly seminars given by faculty mentors, formally report and present their research experiences and results, conduct field trips, and interact with ITEST, RET and GK-12 participants. SuperMe provides scientific exploration ranging from engineering to sciences with a coherent intellectual focus on supercomputing. It consists of four computer systems projects that aim to improve techniques in grid computing, parallel I/O data accesses, high-resolution scientific visualization and information security, and five computer modeling projects that utilize world-class supercomputing and visualization facilities housed at UMaine to perform large, complex simulation experiments and data analysis in different science domains. SuperMe provides a diversity of cutting-edge research opportunities to students from under-represented groups or from universities in rural areas with limited research opportunities. Through interacting directly with the participant of existing programs at UMaine, including ITEST, RET and GK-12, REU students disseminates their research results and experiences to middle and high school students and teachers. This site is co-funded by the Department of Defense in partnership with the NSF REU Site program
Recommended from our members
On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches
The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach
Improving Response Time and Though put of Search Engine with Web Caching
Large web search engines need to be able to process
thousands of queries per second on collections of
billions of web pages. As a result, query processing is a
major performance bottleneck and cost factor in
current search engines, and a number of techniques are
employed to increase query throughput, including
massively parallel processing, index compression,
early termination, and caching. Caching is a useful
technique for Web systems that are accessed by a large
number of users. It enables a shorter average response
time, it reduces the workload on back-end servers, and
it reduces the overall amount of utilized bandwidth.
Our contribution in this paper can be split into two
parts. In the first part, we proposed Cached Search
Algorithm (CSA) on top of the multiple search engines
like Google, Yahoo and Bing and achieved the better
response time while accessing the resulting web pages.
In the second part, we design and implemented the
Cached Search Engine and the performance evaluated
based on the training data (WEPS dataset [1]) and the
test data (Mobile dataset). The Cached Search
outperforms the better by reducing the response time of
search engine and to increase response throughput of
the searched results
CHRONOS: Time-Aware Zero-Shot Identification of Libraries from Vulnerability Reports
Tools that alert developers about library vulnerabilities depend on accurate,
up-to-date vulnerability databases which are maintained by security
researchers. These databases record the libraries related to each
vulnerability. However, the vulnerability reports may not explicitly list every
library and human analysis is required to determine all the relevant libraries.
Human analysis may be slow and expensive, which motivates the need for
automated approaches. Researchers and practitioners have proposed to
automatically identify libraries from vulnerability reports using extreme
multi-label learning (XML).
While state-of-the-art XML techniques showed promising performance, their
experiment settings do not practically fit what happens in reality. Previous
studies randomly split the vulnerability reports data for training and testing
their models without considering the chronological order of the reports. This
may unduly train the models on chronologically newer reports while testing the
models on chronologically older ones. However, in practice, one often receives
chronologically new reports, which may be related to previously unseen
libraries. Under this practical setting, we observe that the performance of
current XML techniques declines substantially, e.g., F1 decreased from 0.7 to
0.24 under experiments without and with consideration of chronological order of
vulnerability reports.
We propose a practical library identification approach, namely CHRONOS, based
on zero-shot learning. The novelty of CHRONOS is three-fold. First, CHRONOS
fits into the practical pipeline by considering the chronological order of
vulnerability reports. Second, CHRONOS enriches the data of the vulnerability
descriptions and labels using a carefully designed data enhancement step.
Third, CHRONOS exploits the temporal ordering of the vulnerability reports
using a cache to prioritize prediction of...Comment: Accepted to the Technical Track of ICSE 202
Archexplorer for automatic design space exploration
Growing architectural complexity and stringent time-to-market constraints suggest the need to move architecture design beyond parametric exploration to structural exploration. ArchExplorer is a Web-based permanent and open design-space exploration framework that lets researchers compare their designs against others. The authors demonstrate their approach by exploring the design space of an on-chip memory subsystem and a multicore processor.Postprint (published version
RAIDX: RAID EXTENDED FOR HETEROGENEOUS ARRAYS
The computer hard drive market has diversified with the establishment of solid state disks (SSDs) as an alternative to magnetic hard disks (HDDs). Each hard drive technology has its advantages: the SSDs are faster than HDDs but the HDDs are cheaper. Our goal is to construct a parallel storage system with HDDs and SSDs such that the parallel system is as fast as the SSDs. Achieving this goal is challenging since the slow HDDs store more data and become bottlenecks, while the SSDs remain idle. RAIDX is a parallel storage system designed for disks of different speeds, capacities and technologies. The RAIDX hardware consists of an array of disks; the RAIDX software consists of data structures and algorithms that allow the disks to be viewed as a single storage unit that has capacity equal to the sum of the capacities of its disks, failure rate lower than the failure rate of its individual disks, and speeds close to that of its faster disks. RAIDX achieves its performance goals with the aid of its novel parallel data organization technique that allows storage data to be moved on the fly without impacting the upper level file system. We show that storage data accesses satisfy the locality of reference principle, whereby only a small fraction of storage data are accessed frequently. RAIDX has a monitoring program that identifies frequently accessed blocks and a migration program that moves frequently accessed blocks to faster disks. The faster disks are caches that store the solo copy of frequently accessed data. Experimental evaluation has shown that a HDD+SSD RAIDX array is as fast as an all-SSD array when the workload shows locality of reference
Letter from the Special Issue Editor
Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies
- …