PPT-ICICLES: Self-tuning Samples for Approximate Query Answerin

By Venkatesh Ganti Mong Li Lee and Raghu Ramakrishnan CSE6339 Data exploration Raghavendra Madala In this presentation Introduction Icicles Icicle Maintenance IcicleBased Estimators. 49 BelAir 103 Levis Qc Canada G6V 6K9 mrueltopcontrolcom Keywords Process Control Optimization Oscilla tion Model PID Control Closed L oop Open Loop Stiction Backlash Non linearity Pseudorandombinary Sequence Generalized Binary Noise ABSTRACT Tradit JIM TURNERLOOK AT ME NOW...WRITTEN BY: CHAS SMITHEXCEPTIONS TO...PHOTOGRAPHED BY: JOHN CAREYWRITTEN BY: JOANNA PRISCOA WOMAN AS...WRITTEN BY: ALICE PFEIFFER Sulabha Balan. ICICLES: Self-tuning Samples for Approximate Query Answering. By . Venkatesh. . Ganti. , . Mong. Li Lee, and . Raghu. . Ramakrishnan. CSE 6339 . Introduction. Icicles. Icicle Maintenance. 1 1 .functionPoint(x,y){varself={};self.x=x;self.y=y;self.setX=function(d:number){self.x=d;};returnsetTag(point)(self);}ThefunctionPointcreatesanewpoint.Itallocatesanewemptyrecordandstoresitinthelocal By . Venkatesh. . Ganti. , . Mong. Li Lee, and . Raghu. . Ramakrishnan. Shruti. P. . Gopinath. CSE 6339 . Outline. Introduction . and background. Icicles. Icicle Maintenance. Icicle-Based Estimators. 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 (. 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” . 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 . By Hilary . Janysek. What’s the difference?. Intonation: . . (noun) The correct or accurate pitching (placement) of intervals; the capacity to play or sing in tune.. Tuning: . . (verb) to adjust to the correct or given standard of pitch. . + . 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 . 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. Ulya. . R. . Karpuzcu. ukarpuzc@umn.edu. . 12/01/2015. Outline. Background. Pitfalls & Fallacies. Practical Guidelines. 2. 12/01/2015. On Quantification of Accuracy Loss in Approximate Computing. 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 . Computation Circuits. Wei-Ting Jonas Chan. 1. , Andrew B. Kahng. 1. , . Seokhyeong Kang. 1. , . Rakesh. Kumar. 2. , and John Sartori. 3. 1. VLSI . CAD LABORATORY, . UC San Diego. 2. PASSAT GROUP, Univ. of Illinois.