PPT-Achieving High Performance and Fairness

at Low Cost Lavanya Subramanian Donghyuk Lee Vivek Seshadri Harsha Rastogi Onur Mutlu 1 The Blacklisting Memory Scheduler Main Memory Interference Problem Causes interference between applications requests. edu Dhruba Borthakur Facebook Inc dhrubafacebookcom Joydeep Sen Sarma Facebook Inc jssarmafacebookcom Khaled Elmeleegy Yahoo Research khaledyahooinccom Scott Shenker University of California Berkeley shenkercsberkeleyedu Ion Stoica University of Cali 6 94 319 539 634 736 264 5038 Agriculture 07 25 62 264 486 661 339 3180 Arabic 183 302 498 648 761 880 120 6010 Art 04 10 104 493 814 956 44 5090 Biology 75 186 417 623 752 862 138 7115 Business Studies 22 80 206 393 551 705 295 5070 Chemistry 93 190 The causes of the segments pending demise according to the experts include the growing popularity of specialty stores and lowcost retailers industry consolidation and changing consumerspending patterns It has been assumed that these forces would con edu Dhruba Borthakur Facebook Inc dhrubafacebookcom Joydeep Sen Sarma Facebook Inc jssarmafacebookcom Khaled Elmeleegy Yahoo Research khaledyahooinccom Scott Shenker University of California Berkeley shenkercsberkeleyedu Ion Stoica University of Cali “Computing . is changing more rapidly than ever before, and scientists have the unprecedented opportunity to change computing . directions” . “In . 20 or 30 years, you'll be able to hold in your hand as much computing knowledge as exists now in the whole city, or even the whole world. in . Large-Scale Service Systems. Mor. . Armony. Stern . School of Business, . NYU. *Joint work with . Amy Ward. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . Robert Grandl, Mosharaf Chowdhury, . Aditya Akella, Ganesh Ananthanarayanan. Carbyne. Performance of Cluster Schedulers. We observe that:. Existing cluster schedulers focus on. . instantaneous. . fairness. Facility . Management.”. January 22, 2015 . The Journey Begins……. What does HP/world class. mean to you? . How do . y. ou . g. et . t. here?. How do . y. ou . p. rove . i. t?. High-Performance . The Fairness Quotient and Why it Matters Fran Sepler, Sepler & Associates for South Dakota SHRM Think of a Time You Were Treated Unfairly At Work What made the experience fair? How did you react? Achieving High Performance and Fairness at Low Cost Lavanya Subramanian , Donghyuk Lee, Vivek Seshadri , Harsha Rastogi , Onur Mutlu 1 The Blacklisting Memory Scheduler Main Memory Interference Problem FLIN: Enabling Fairness and Enhancing Performance in Modern NVMe Solid State Drives August 7, 2019 Santa Clara, CA Saugata Ghose Carnegie Mellon University Executive Summary Modern solid-state drives (SSDs) use new storage protocols Mojtaba . Malekpourshahraki. Brent Stephens. Balajee. . Vamanan. Modern . datacenter. Datacenters host multiple . applications with different requirements. Memcache. (delay). Web search (delay) . Spark (throughput). Fairness is becoming a paramount consideration for data scientists. Mounting evidence indicates that the widespread deployment of machine learning and AI in business and government is reproducing the same biases we\'re trying to fight in the real world. But what does fairness mean when it comes to code? This practical book covers basic concerns related to data security and privacy to help data and AI professionals use code that\'s fair and free of bias.Many realistic best practices are emerging at all steps along the data pipeline today, from data selection and preprocessing to closed model audits. Author Aileen Nielsen guides you through technical, legal, and ethical aspects of making code fair and secure, while highlighting up-to-date academic research and ongoing legal developments related to fairness and algorithms.Identify potential bias and discrimination in data science modelsUse preventive measures to minimize bias when developing data modeling pipelinesUnderstand what data pipeline components implicate security and privacy concernsWrite data processing and modeling code that implements best practices for fairnessRecognize the complex interrelationships between fairness, privacy, and data security created by the use of machine learning modelsApply normative and legal concepts relevant to evaluating the fairness of machine learning models Anupam. . Datta. With many slides from Moritz . Hardt. Fall . 2017. 18734: Foundations of Privacy. Fairness in Classification. . Advertising. ✚. Health Care. Education. many more.... $. Banking.