Elba Garza will present her Generals on Monday, January 19, 2015 at 8:30 am in Rm 401. The members of her committee are David August, Margaret Martonosi, Kelly Shaw (University of Richmond). Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His/her abstract and reading list follow below. Abstract The emerging popularity of heterogeneous architectures results in a necessary and complex selection of runtime hardware/software parameters. As these heterogeneous systems have grown to offload computation to graphics processing units (GPUs), various Application-specific integrated circuits (ASICs) and other compute units, there is a higher need to optimize these units’ software and hardware settings to reach better power, performance, and area goals. Exhaustive exploration techniques are impractical since the design spaces grow exponentially with the addition of new parameters. Design spaces can easily reach thousands or millions of configurations to simulate or test. Manual and inference-based information can be flawed due to individual knowledge and bias, which may lead to unsearched design areas. Using statistical learning techniques, we have been able to optimize design space exploration for parameters of heterogeneous architecture components such as GPUs and Accelerator ASICs. These techniques allow for efficient and tractable design space assessment and can output optimal or near-optimal parameter settings for given applications automatically. Reading List [Cas+10] C. Cascaval et al. “A taxonomy of accelerator architectures and their programming models”. In: IBM Journal of Research and Development 54.5 (Sept. 2010), 5:1–5:10. issn: 0018-8646. doi: 10. 1147/JRD.2010.2059721. [Con+14] Jason Cong et al. “Accelerator-Rich Architectures: Opportunities and Progresses”. In: Proceedings of the 51st Annual Design Automation Conference. DAC ’14. San Francisco, CA, USA: ACM, 2014, 180:1–180:6. isbn: 978-1-4503-2730-5. doi: 10 . 1145 / 2593069 . 2596667. url: http://doi.acm.org/10.1145/2593069.2596667. [Esm+11] Hadi Esmaeilzadeh et al. “Dark Silicon and the End of Multicore Scaling”. In: Proceedings of the 38th Annual International Symposium on Computer Architecture. ISCA ’11. San Jose, California, USA: ACM, 2011, pp. 365–376. isbn: 978-1-4503-0472-6. doi: 10. 1145/2000064.2000108. url: http://doi.acm.org/10.1145/ 2000064.2000108. [Lyo+12] Michael J. Lyons et al. “The Accelerator Store: A Shared Memory Framework for Accelerator-based Systems”. In: ACM Trans. Archit. Code Optim. 8.4 (Jan. 2012), 48:1–48:22. issn: 1544-3566. doi: 10. 1145/2086696.2086727. url: http://doi.acm.org/10.1145/ 2086696.2086727. [Sha+14] Yakun Sophia Shao et al. “Aladdin: A Pre-RTL, Power-performance Accelerator Simulator Enabling Large Design Space Exploration of Customized Architectures”. In: Proceeding of the 41st Annual International Symposium on Computer Architecuture. ISCA ’14. Minneapolis, Minnesota, USA: IEEE Press, 2014, pp. 97–108. isbn: 1lba Garza January 19, 2015 978-1-4799-4394-4. url: http://dl.acm.org/citation.cfm?id= 2665671.2665689. [Ham+10] Rehan Hameed et al. “Understanding Sources of Inefficiency in General-purpose Chips”. In: Proceedings of the 37th Annual International Symposium on Computer Architecture. ISCA ’10. SaintMalo, France: ACM, 2010, pp. 37–47. isbn: 978-1-4503-0053-7. doi: 10.1145/1815961.1815968. url: http://doi.acm.org/10.1145/ 1815961.1815968. [JSM12] Wenhao Jia, Kelly A. Shaw, and Margaret Martonosi. “Stargazer: Automated Regression-based GPU Design Space Exploration”. In: Proceedings of the 2012 IEEE International Symposium on Performance Analysis of Systems & Software. ISPASS ’12. Washington, DC, USA: IEEE Computer Society, 2012, pp. 2–13. isbn: 978-1- 4673-1143-4. doi: 10 . 1109 / ISPASS . 2012 . 6189201. url: http : //dx.doi.org/10.1109/ISPASS.2012.6189201. [JSM13] Wenhao Jia, Kelly A. Shaw, and Margaret Martonosi. “Starchart: Hardware and Software Optimization Using Recursive Partitioning Regression Trees”. In: Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques. PACT ’13. Edinburgh, Scotland, UK: IEEE Press, 2013, pp. 257– 268. isbn: 978-1-4799-1021-2. url: http://dl.acm.org/citation. cfm?id=2523721.2523757. [JVT06a] P. J. Joseph, K. Vaswani, and Matthew J. Thazhuthaveetil. “Construction and use of linear regression models for processor performance analysis”. In: High-Performance Computer Architecture, 2006. The Twelfth International Symposium on. Feb. 2006, pp. 99– 108. doi: 10.1109/HPCA.2006.1598116. [JVT06b] P. J. Joseph, Kapil Vaswani, and Matthew J. Thazhuthaveetil. “A Predictive Performance Model for Superscalar Processors”. In: Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture. MICRO 39. Washington, DC, USA: IEEE Computer Society, 2006, pp. 161–170. isbn: 0-7695-2732-9. doi: 10.1109/MICRO.2006.6. url: http://dx.doi.org/10.1109/ MICRO.2006.6. [LB06] Benjamin C. Lee and David M. Brooks. “Accurate and Efficient Regression Modeling for Microarchitectural Performance and Power Prediction”. In: SIGPLAN Not. 41.11 (Oct. 2006), pp. 185–194. issn: 0362-1340. doi: 10 . 1145 / 1168918 . 1168881. url: http : //doi.acm.org/10.1145/1168918.1168881. 2lba Garza January 19, 2015 Book List [HP12] J.L. Hennessy and D.A. Patterson. Computer Architecture: A Quantitative Approach. Morgan Kaufmann. Morgan Kaufmann/Elsevier, 2012. isbn: 9780123838728. Nicki Gotsis Graduate Coordinator Room 310, Computer Science (609) 258-5387 ngotsis@cs.princeton.edu