| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
|
| Initial Amendment Date: | August 4, 2011 |
| Latest Amendment Date: | August 4, 2011 |
| Award Number: | 1117895 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Almadena Chtchelkanova
achtchel@nsf.gov (703)292-7498 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2011 |
| End Date: | August 31, 2015 (Estimated) |
| Total Intended Award Amount: | $425,000.00 |
| Total Awarded Amount to Date: | $425,000.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
110 INNER CAMPUS DR AUSTIN TX US 78712-1139 (512)471-6424 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
110 INNER CAMPUS DR AUSTIN TX US 78712-1139 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | Software & Hardware Foundation |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The face of emerging computing systems is changing rapidly. On one hand computers are looking smaller than ever. The smart phone is nothing but a pocket computer which also has the ability to make a phone call. On the other hand, computers are getting bigger than ever, as in a cloud computing server. These cloud systems are distributed systems with a large number of processors, abundant memory and disks and plentiful network resources and bandwidth, and are huge. This project is on workload characterization and benchmark synthesis for emerging computing systems such as the embedded computer and the cloud computer.
The first component of this project consists in characterizing and understanding workloads on emerging computing platforms. Characteristics of embedded computing software in embedded Java or paradigms such as the .NET, or cloud applications such as Hadoop are not well-understood. It is also unclear whether next generation cloud servers should use simple energy efficient processors or more powerful processors geared towards performance. The characterization component of our project is geared towards understanding these issues.
The second component of this project consists of benchmark synthesis to create workload proxies that have equivalent performance and energy characteristics as the original applications but without the functionality. These proxy workloads will enable efficient pre-silicon design exploration and sharing of proprietary applications.
At a broader level, the research creates mechanisms that enable hardware and software developers to collaborate on joint product development without fear of loss of intellectual property. It also trains several graduate and undergraduate students in an important research area, and influences courses on embedded and cloud computing.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Big data is everywhere and mining big data has become critical for many businesses, healthcare and the economy. On another front embedded computing is more pervasive than ever since humans are increasingly relying on their mobile phones for various applications other than telephone communication. As part of this project, a team of graduate students and the principal investigator analyzed various workloads in the embedded, and big data/cloud domains. Power and energy are prime constraints for both cloud computing and embedded computing. This project resulted in better understanding of the performance and energy efficiency of emerging workloads on emerging platforms. Several emerging big data frameworks such as MongoDB and Cassandra were analyzed. Distributed frameworks such as MapReduce and Hive were analyzed too. While being popular, these new frameworks have many inefficiencies in them. On the embedded front, we have characterized many smart phone applications in heterogeneous many-core processors. Our study has given important insights that can be leveraged to improving the energy efficiency of embedded and cloud computing. The research had resulted in several publications in the workload characterization
Another outcome of the project was the creation of workload proxies that have equivalent performance and energy characteristics as the original applications but without the functionality. The workload characterization process illustrated the various layers of software needed for running these applications. It will be impossible to run such a deep software stack on many pre-silicon models.
The research done under the project has helped us to develop industry collaborations with many US and international companies. We are leveraging these collaborations to get further research support in the form of cash, infrastructure and student internships.
The project has trained more than half a dozen graduate students and one undergrad student with goal of contributing to the workforce required to maintain the technological leadership of our country in computer technologies. The undergraduate student (minority Hispanic student) became very well-versed with emerging workloads and got trained in computer architecture simulation and modeling methodologies. When he graduated, he got several job offers and accepted one at Amazon.com. One graduate student who was partially supported by the project finished his Ph. D and is working at Intel. Two graduate students who have been supported by the project have finished their course requirements, obtained M.S degrees and passed Ph. D candidacy exams. Two graduate students who have been supported by the project are near finishing their M. S degrees. But they will be advancing to the Ph. D program. All of the students who worked on the project have done industry internships and have already become very valuable to the computer industry. The companies project participants have interned include IBM, AMD, Intel, ARM, and Samsung. In summary, the project has led to several important research collaborations.
Last Modified: 12/03/2015
Modified by: Lizy K John
Please report errors in award information by writing to: awardsearch@nsf.gov.
