
NSF Org: |
CCF Division of Computing and Communication Foundations |
Recipient: |
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Initial Amendment Date: | May 15, 2013 |
Latest Amendment Date: | August 4, 2015 |
Award Number: | 1302557 |
Award Instrument: | Continuing Grant |
Program Manager: |
Tao Li
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
Start Date: | July 1, 2013 |
End Date: | June 30, 2017 (Estimated) |
Total Intended Award Amount: | $800,000.00 |
Total Awarded Amount to Date: | $800,000.00 |
Funds Obligated to Date: |
FY 2015 = $0.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
201 OLD MAIN UNIVERSITY PARK PA US 16802-1503 (814)865-1372 |
Sponsor Congressional District: |
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Primary Place of Performance: |
354A IST Building University Park PA US 16802-7000 |
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): |
Software & Hardware Foundation, COMPUTER ARCHITECTURE |
Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT |
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
Almost all application segments today experience data explosion, meaning that they need to store, access, manipulate and transform extremely large amounts of data stored in different mediums in a fashion that is simultaneously performance-aware and energy-aware. These data-hungry market segments include (i) consumer applications in the mobile and home electronics segment, (ii) desktop applications that are providing rich content and user experience, (iii) scientific applications that generate petabytes of data for analyzing experiments and real-world phenomena on temporal and spatial scales unheard of before, (iv) enterprise applications which tirelessly store all kinds of data/knowledge for auditability, analytics, and optimization, (v) datacenters and cloud platforms which use storage to hold large virtual machine images of the workloads for consolidation across different servers, (vi) Internet services and social networking platforms which need to store, track and manage user patterns, and (vii) cyber-physical applications which continuously sense and store physical world data for real-time analytics and control. Current computer infrastructures are poorly equipped to cope with this data demand. The primary reason for this is the inherent physical divide between computation and storage. While both computation and storage technologies have undergone tremendous improvements in the last decades, the interactions and interfaces between them have not, thereby limiting the performance of critical data-intensive applications. If not addressed in a timely fashion, this problem has the potential to slow down scientific discoveries and engineering breakthroughs.
This project addresses the data management problem by breaking the physical divide between computation and NAND-flash storage. Doing so can potentially allow the communication bandwidth between computation and storage to scale together with the parallelism-driven scaling of both computation resources and storage resources. It can also allow each to become more aware of the intentions and operations of the other, opening a wide spectrum of possibilities in more efficiently managing storage. This will in turn allow better co-design, co-management, and co-evolution of the two for better scalability in the future, as applications start imposing even more stringent computing and storage demands. Specifically, this project investigates three main strategies for bridging the physical divide between compute and NAND-flash storage. The first strategy enables better cooperation between flash storage and host; the second strategy elevates NAND-flash storage to directly interface with the processors, similar to main memory DIMMs (dual inline memory modules) interfacing to the on-chip cores through memory controllers; and the last strategy explores different placement options for tighter integration of NAND-flash storage with computational resources. The broader impacts of this research include student training, participation of under-represented groups, recruiting workshops, incorporation of educational modules into existing and future courses, and public domain simulation tools. Further, through the Visit In Engineering Weekend (VIEW) program, the project fosters interest in computer science and engineering. The project provides hands-on-design activities to motivate the VIEW participants in new areas of computer science and engineering related to storage system and data management.
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.
Flash memories are being increasingly employed as a mainstream storage medium in a wide variety of systems, ranging from embedded computing platforms that run handheld applications to high performance computing systems that run large scale simulations of applications of national importance (e.g., astrophysics and computational chemistry). Their superiority over disk based storage, in terms of both performance and power consumption, makes them a highly attractive option for both designers and practitioners.
Observing that most prior works in flash memories so far considered mostly ad-hoc methods and point solution, this NSF research targeted a systematic exploration of flash memory systems from loosely-coupled ones (where compute units and flash storage are located separately from each other) to tightly-coupled ones (where compute units and flash storage are integrated in the same chip). More specifically, this project investigated various correlated schemes that make flash storage faster, more power efficient, more reliable and more durable. These schemes included both hardware level approaches such as designing ultra-scale SSD architectures and integrating GPUs with NAND flash storage, as well as firmware/software level approaches such as workload consolidation for SSDs and novel error correction schemes. At the end, our schemes collectively were able to make flash systems 2x faster, 2x more reliable, and 3x more energy efficient, compared to the state-of-the-art. The project generated research publications in top computer science and engineering conferences and journals (e.g., ISCA, ASPLOS, and HPCA).
Throughout this project, the PIs developed various software packages and have either put them in the public domain or are in the process of doing so. These packages are expected to be useful, not just in the context of research, but also in the context of teaching. In fact, the PIs have already used the select contents from this research material to enhance some of the courses they regularly teach at Penn State.
Apart from these research and teaching outcomes, the project helped 2 PhD and 1 MS students complete their thesis, and also helped with the training of another 4 other PhD students. The project also undertook several outreach activities, including one of the PIs taking part in a summer workshop at Penn State for high school teachers. This workshop, called “Computers and the Universe”, included two modules prepared and presented by the PI on computing and storage systems as well as software.
Last Modified: 11/25/2017
Modified by: Mahmut T Kandemir
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