PPT-PA Man Coordinated Memory Caching for Parallel Jobs

Ganesh Ananthanarayanan Ali Ghodsi Andrew Wang Dhruba Borthakur Srikanth Kandula Scott Shenker Ion Stoica Data Analytics Clusters Data analytics frameworks are an important driver for modern Internet services. How to deal with coarsely parallel problems. Wealth from Oceans National research Flagship. Malcolm Haddon. May 2014. Computer Intensive. Many, many, many iterations:. Management Strategy Evaluation. Justin DeBrabant. Brown University . debrabant@cs.brown.edu. A bit of history…. 1974 – System R. query optimization . recovery . transaction serialization. lots of locks. Application. Primary Storage. How It Works and When You Should Use It . Shy Cohen. Principal, . Shy Cohen Consulting. AAP314. What Gurus Need to Know. What’s a cache and what it does. Core and advanced concepts. Caching on premises and in Windows Azure. Ilija Vukotic. ADC TIM - Chicago. October 28, 2014. Caching – why and where?. Straight Tier3 . Most of the disk space is devoted to input data. The input data is almost always from downloaded from the grid. Andrew Jaffe. Computing Club. 4/5/2015. Overview. Introduction. multicore . Array jobs. The rest. Introduction. Based roughly on: McCallum and Weston. Parallel R. 2012 (O’Reilly Book), so consult for the more complicated methods. Improving Computer Performance. What performance translates into:. Time taken to do computation. Improving performance . → reducing time taken. What key benefits improving performance can bring:. Can solve “now-computationally-attainable” problems in . Efficient URL Caching for World Wide Web Crawling. Presenter. Sawood. . Alam. salam@cs.odu.edu. AND. Parallel Crawlers. Hector Garcia-Molina. Stanford University. cho@cs.stanford.edu. Junghoo. Cho. Victor Zakhary, . Divyakant. Agrawal, Amr El . Abbadi. 1. The old problem of Caching. Disk. RAM. L. 2. L. 1. Larger. Slower. Cheaper. Smaller. Faster. Expensive. 2. The old problem of Caching. Smaller. and When You Should Use It . Jon Flanders. Senior Consultant. MCW Technologies. MID302. Agenda. Why Caching?. What is AppFabric Cache?. On-Premise and in Azure. Demo, demo, demo. Typical Web Architecture. Ganesh Ananthanarayanan. University of California at Berkeley. http://www.cs.berkeley.edu/~ganesha. /. Rising philosophy of . data-ism. Diagnostics and decisions backed by extensive data analytics.  . Caching . for . Data-Intensive Clusters. Ganesh Ananthanarayanan, Ali Ghodsi, Andrew Wang, Dhruba Borthakur, Srikanth Kandula, Scott Shenker, Ion Stoica. 1. Data Intensive Computation. CS-3013 . Operating Systems. Hugh C. Lauer. (Slides include materials from. Slides include materials from . Modern Operating Systems. , . 3. rd. ed. ., by Andrew . Tanenbaum. and from . Operating System Concepts. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. &. Caching. Noah Mendelsohn. Tufts University. Email: . noah@cs.tufts.edu. Web: http://www.cs.tufts.edu/~noah. COMP 150-IDS: Internet Scale Distributed Systems . (Spring 2015). 2. Goals. Explore some general principles of performance,...