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Thesis
High-performance architectures for accelerating sparse LU computation
Master of Science (M.S.), Drexel University
Jun 2011
DOI:
https://doi.org/10.17918/etd-3718
Abstract
Sparse Lower-Upper (LU) Triangular Decomposition is important to many different applications, including power system analysis. High-performance sparse linear algebra software packages, executing on general-purpose processors, experience lower performance when processing power system matrices. This observation motivated previous work on the design of custom hardware, implemented, in FPGA, to improve performance of sparse LU. While improved performance was obtained, significant effort was required to design and implement the hardware. This thesis investigates the combination of general purpose architectures and a hardware accelerator, for a crucial component of sparse LU, to achieve similar performance results without the design overhead. One architecture, combining a general-purpose processor with a hardware accelerator, achieves a 1.29X speedup over software for a 26K-Bus power system. The second architecture, a modification of the Data Pump Architecture, provides a 2.27X speedup over software on the 26K-bus power system. These results show that speedup for sparse LU is possible, without designing a complete custom hardware solution, using a small hardware accelerator, provided a tightly coupled architecture is available to feed data to the accelerator.
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Details
- Title
- High-performance architectures for accelerating sparse LU computation
- Creators
- Kevin Cunningham - DU
- Contributors
- Prawat Nagvajara (Advisor) - Drexel University (1970-)Jeremy Russell Johnson (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Electrical (and Computer) Engineering [Historical]; Drexel University
- Other Identifier
- 3718; 991014632305404721