| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | June 11, 2013 |
| Latest Amendment Date: | June 11, 2013 |
| Award Number: | 1250886 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | June 1, 2013 |
| End Date: | May 31, 2018 (Estimated) |
| Total Intended Award Amount: | $680,916.00 |
| Total Awarded Amount to Date: | $680,916.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
21 N PARK ST STE 6301 MADISON WI US 53715-1218 (608)262-3822 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
21 North Park Street Madison WI US 53715-1218 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Big Data Science &Engineering |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
We are now in the midst of the big data revolution where enterprise services are increasingly being driven by operational and business models that are powered by data analysis. A key part in making big data successful is ensuring that basic data processing primitives can execute efficiently on large and every increasing volumes of data. However, data processing kernels today largely employ techniques that have been designed about three decades ago, and are now out of touch with modern hardware that has made a fundamental technological shift. First, driven by power consumption characteristics, modern processors now have multiple processing units (called cores) fabricated in a single chip. In contrast, processors just a few years ago were single core. Second, traditionally the storage media for data has been the magnetic hard disk. Now, data has started to move nearly permanently to higher levels of the memory hierarchy, and more specifically to main memory. The goal of this project is to rethink key aspects of data processing techniques for the modern many-core and main memory hardware environment. The research approach is to design, implement and evaluate various methods for data kernels that can be used to store and process data efficiently. In other words, the key focus is on producing data kernels that "run at the speed of modern hardware." Thus, this project aims to have a broad impact on the big data ecosystem by developing faster, cheaper and more energy-efficient data kernels.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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.
The move towards data-driven decision making in enterprises coupled with the growth in data volumes requires taking a fresh look at key mechanims that power data analytics platform. This fresh look is especially critical as the hardware used in modern servers uses compute and storage methods that have architecturally very different propoerties than those from a decade ago, which implies that traditional methods for data processing do not effectively exploit the full potential of modern hardware. The overall goal of this project was to design, implement and evaluate more efficient, and thus cost-effective, ways to carry out data analytics than existing methods.
Various data organization algoritms were developed as part of this project, contributing to the key components of the intellectual merit contributions of this project. Other intellectual contributions include the development of various algorithms for common data processing tasks such as searching though large datasets and combining informatoin from two or more differnent datasets. Methods were also developed to speed up machine learning programs by using static analysis methods. Overall the intellectual merits have lead to the creation of more efficient ways to carry out complex data analytics tasks.
The broader impacts of this work consists of taking ideas from this research and developing into an actual platform that has been open-sourced. Work under this project has been published in top-tier confereneces, and this project has contributed to the training of five Ph.D. students.
Last Modified: 07/04/2018
Modified by: Jignesh M Patel
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