PPT-Massively Parallel/Distributed Data Storage Systems

S Sudarshan IIT Bombay Derived from an earlier talk by S Sudarshan presented at the MSRI Summer School on Distributed Systems MayJune 2012 Why Distributed Data Storage aka Cloud Storage. Gupta IBM India Research Labs Block 1 IIT Hauz Khas New Delhi India saurabhagarwal grahul meetashainibmcom Jose E Moreira IBM TJ Watson Research Center Yorktown Heights NY 10598 moreirausibmcom ABSTRACT Giventhescaleofmassivelyparallelsystemsoccurre 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. CUDA Lecture 1. Introduction to Massively Parallel Computing. A quiet revolution and potential buildup. Computation: TFLOPs . vs. . 100 GFLOPs. CPU in every PC – massive volume and potential impact. Spring 2015. Ki-. Joune. Li . http://isel.cs.pusan.ac.kr/~lik. Pusan National University. An . assignment. Choose an electronic (or electric) device . Define additional functions with. . data storage. S. Sudarshan. IIT Bombay. Derived from an earlier talk by S. . Sudarshan, presented . at the. MSRI Summer School on Distributed Systems, May/June 2012. Why Distributed Data Storage a.k.a. Cloud Storage. Andrew Lumsdaine. Indiana University. lums@osl.iu.edu. My Goal in Life. Performance with elegance. Introduction. Overview of our high-performance, industrial strength, graph library. Comprehensive features. 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 . Recall: Microprocessors are classified by how memory is organized. Tightly-coupled multiprocessor systems use the same memory. They are also referred to as . shared memory multiprocessors. .. The processors do not necessarily have to share the same block of physical memory: . Mahesh Ravishankar. 1. , John Eisenlohr. 1. , . Louis-Noël Pouchet. 1,3. , J. Ramanujam. 2. , . Atanas. Rountev. 1. , P. Sadayappan. 1. 1. Dept. of Computer Science & Engineering. The Ohio State University. Early Adopter: ASU - Intel Collaboration in Parallel and Distributed Computing Yinong Chen , Eric Kostelich , Yann -Hang Lee, Alex Mahalov , Gil Speyer, and Violet R. Syrotiuk 1 st NSF /TCPP Workshop on Parallel and Distributed Computing Education ( Prof. Nalini . Venkatasubramanian. . &. Prof. Yusuf . Sarwar. Dept. of Information & Computer Science. University of California, Irvine. Intro to Distributed Systems Middleware. 2. CS 237/. NetSys. Introduction. Parallel machines have become quite common and affordable. prices of microprocessors, memory and disks have dropped sharply. Data storage needs are growing increasingly large. user data at web-scale. Introduction. Papadakis Harris. Dept. of Engineering Informatics. ΤΕΙ . of Crete. Distributed Systems. - . Contents. Definition. Parallel. . Processing. Distributed . Computin. Clusters. Examples. Papadakis Harris. Dept. of Engineering Informatics. ΤΕΙ . of Crete. Client/Server Paradigm. Basic idea: to structure the system as a set of collaborating processes. Servers (or servers) that offer their services.