| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | August 25, 2016 |
| Latest Amendment Date: | April 13, 2018 |
| Award Number: | 1629450 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Matt Mutka
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2016 |
| End Date: | August 31, 2020 (Estimated) |
| Total Intended Award Amount: | $875,000.00 |
| Total Awarded Amount to Date: | $875,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1001 EMMET ST N CHARLOTTESVILLE VA US 22903-4833 (434)924-4270 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
P.O. BOX 400195 Charlottesville VA US 22904-4195 |
| 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): | Exploiting Parallel&Scalabilty |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
As society collects more and more data about the world around us, and digitizes more and more artifacts, "big data" promises unparalleled potential, but also poses new and unique computational challenges. Turning data into useful knowledge at or near real-time can have significant impacts, such as enabling timely intervention in healthcare and fast response in cybersecurity. As technology constraints limit CPU performance, researchers and practitioners are increasingly looking to specialized processors to accelerate data analytics. The ability to extract patterns from unstructured data is an especially important task. This research project carries out a cross-stack investigation to evaluate the effectiveness of the automata computing paradigm to accelerate pattern mining of unstructured data.
Specifically, by leveraging the industry's new Automata Processor (developed by Micron Technology), this project is (1) developing benchmark suites of truly diverse automata for performance comparison of real and simulated, existing and future automata engines, (2) developing new tools, including programming languages, systems, and architectural enhancement to make automata computing intuitive and easy to adopt, (3) evaluating automata computing solutions to address real-world big-data applications, and (4) developing a set of educational and community-building activities to maximize the broader impact of the project outcome. Successful implementation of this project enable new automata-based abstractions to shed light on the performance of AP technology for various applications, such as pattern mining. This project will build the the intellectual foundations to support and catalyze research, education, training, and adoption of automata-based solutions to address big-data challenges in industry, government, and society.
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 project, “XPS: FULL: New abstractions and applications for Automata Computing”, successfully carried out a full-stack investigation to evaluate the automata computing paradigm for accelerating sophisticated pattern matching and pattern mining. Automata computing has recently been successfully implemented using hardware acceleration (e.g., on the FPGA or specially designed memory-based processor). Leveraging these advances, this project aimed to develop new automata research tools and benchmarks, new automata-based processing engines, evaluate the automata approach on real-world big data applications, and catalyze a community of automata computing research. Over the performance period, the project has delivered outcomes that made significant contributions to domain knowledge as well as broader societal impact.
On developing automata tools to explore and advance automata computing, the project has delivered an end-to-end development environment to enable automata research and development in the community. The MNCaRT ecosystem includes new tools such as a new programming language (RAPID); a highly flexible automata representation (MNRL); simulators (VASim, AutomataLab); and hardware specific automata engines (Grapefruit and REAPR for FPGAs, and DFAGE and iNFAnt2 for GPUs). The project also resulted in an automata benchmark suite (AutomataZoo) that has gained popular support in the community. Research exploration and evaluation of automata computing also led to new insights, designs, and practical ready-to-use automata-based products. Selected examples include: eAP: a scalable and efficient in-memory accelerator for automata processing; Flexamata: a universal and efficient adaption of applications to spatial automata processing accelerators; Impala: an algorithm/architecture co-design for in-memory multi-stride pattern matching; Grapefruit: an open-source, full-stack, and customizable automata processing on FPGAs; and cloud deployment of automata engines for FPGAs.
On evaluating automata computing on real-world big data applications, automata-based solutions were extensively evaluated on diverse application domains, such as genomics (accelerating DNA alignment), machine learning (accelerating decision trees), and cybersecurity (e.g., accelerating the Snort intrusion detection tool), among others. The results affirmed that hardware acceleration of automata processing is beneficial for a wide variety of tasks, and showed how to implement it as a plug-in accelerator or as an on-chip co-processor.
The intellectual merit of this project has been development of novel optimizations for automata computing. On broader impact, the project not only successfully advanced interest and progress in automata computing and developed a number of tools, but also strengthened the processing-in-memory community broadly. Automata-based solutions are being evaluated on real-world applications with promising results. One of the most evident impact of this project is the number of graduate students it successfully supported. Six students completed doctoral dissertations on automata computing, of whom two continued on to faculty appointments, three are in postdoctoral fellowships, and one is in industry.
Last Modified: 01/08/2021
Modified by: Kevin Skadron
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