PPT-Code Tuning and Optimization

Author : tatiana-dople | Published Date : 2018-03-19

Kadin Tseng Boston University Scientific Computing and Visualization Outline Introduction Timing Example Code Profiling Cache Tuning Parallel Performance Code Tuning

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Code Tuning and Optimization: Transcript


Kadin Tseng Boston University Scientific Computing and Visualization Outline Introduction Timing Example Code Profiling Cache Tuning Parallel Performance Code Tuning and Optimization 2 Introduction. Paolo Romano. Based on ICAC’14 paper. N. . . Diegues. and Paolo Romano. Self-Tuning Intel Transactional Synchronization Extensions. 11. th . USENIX International Conference . on Autonomic Computing (. Part 1: Scaling Up. Sander Temme <sander@temme.net>. 2. “Apache is a general webserver, which is designed to be correct first, and fast second. Even so, its performance is quite satisfactory. Most sites have less than 10Mbits of outgoing bandwidth, which Apache can fill using only a low end Pentium-based webserver.”. When and How to Improve Code Performance?. Ivaylo Bratoev. Telerik Corporation. www.telerik.com. Actual . vs. Perceived Performance. Example: . “Vista's file copy performance is noticeably worse than Windows XP” . CMOS. Advisor: Dr. . Adit D. Singh. Committee members: Dr. . Vishwani. D. . Agrawal. and Dr. . . Victor P. Nelson. Department of Electrical and Computer Engineering. Masters P. roject . D. efense . Kadin Tseng. Boston University. Scientific Computing and Visualization. Serial Performance gain. Due to memory access. Due to caching. Due to vector . representations. Due to compiler. Due to other . + . Also Affiliated with NSF Center for High-Performance Reconfigurable Computing . Tosiron Adegbija. 1. , Ann Gordon-Ross. 1+. ,. . and . Arslan. Munir. 2. 1. Department . of Electrical and Computer . Photoinduced. Fields. L. C. Basset, F. . J. . . Heremans. , C. G. Yale, B. B. Buckley, and A. . Awschalom. PRL 107, 266403 (2011). Motivation. NV centers are sensitive to their local environment -> sample . Chairs. IETF 79. 1. IGMP/MLD tuning milestone. Are we ready to adopt one of these drafts?. draft-asaeda-multimob-igmp-mld-optimization-04.txt. . draft-wu-multimob-igmp-mld-tuning-03.txt . More specific questions follow. The Tuning Process. Benefits of Tuning. Why Tuning is Different. What is Tuning?. A collaborative, faculty-driven . process that . “harmonizes” curricula around defining what a . student should know and be able to do in a chosen . Meeting of the. Indiana Commission for Higher Education. March 11, 2011. University Place Conference Center, IUPUI. 1. Broad Context for Discussion. Access. Completion/Competitiveness. Low graduation rates. process that identifies what a student should know and be able to do in a chosen . discipline at the completion of a degree by:. Defining areas of competency. Identifying learning outcomes. Scaling competencies and outcomes to degree level. Puspita. . Majumdar. , . Mayank . Vatsa. , . Richa. Singh. Indraprastha. Institute of Information Technology Delhi (IIIT-D), India . Saheb Chhabra. Richa. Singh. Mayank. . Vatsa. Puspita. . Majumdar. Tim . Birtwistle. 2. Why ……………………….?. Tuning is a five step process comprised of: . defining the discipline core; . mapping employability; . surveying stakeholders; . honing core competencies and learning outcomes; . regarded in Australia for his tuning mastery, Eric has been evolving the performance of vehicles for over 15 years. He was passionate about cars from a very early age, and had an innate curiosity to

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