PPT-Scheduling Parallel Task

By Atena Daneshmandi Outline Introduction Typology of Parallel Tasks Task Graphs A Deterministic module A Deterministic module by Gantt Chart C ExampleThree Tasks in Parallel Complexity . Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr se Abstract Global 64257xedpriority scheduling of constrained deadline sporadic tasks systems is important not only for CPU scheduling but also in other domains for example scheduling realtime 64258ows in WirelessHART networks designed for industrial Main-Memory Workloads. Iraklis. . Psaroudakis. (EPFL). , Tobias Scheuer (SAP AG), Norman May (SAP AG), Anastasia Ailamaki (EPFL). 1. Scheduling for high concurrency. 2. Queries >> H/W contexts. Software. Steve Teixeira. Product Unit Manager. Microsoft Corporation. Session Code. : DEV401. My Assumptions About You. You have some knowledge of parallel development issues. You’d like to focus on the new stuff: Visual Studio 2010 and .NET 4.0. Reference:. Mesos. : A Platform for Fine-Grained Resource Sharing in the Data Center NSDI’2011. Multi-agent Cluster Scheduling for Scalability and. Flexibility. . Berkerly. . techdoc. EECS-2012-273. (doctoral dissertation). Multi-core Processors. Dawei Li . and Jie . Wu. Department of Computer and Information Sciences. Temple University, Philadelphia, . USA. The 43. rd. International . C. onference on Parallel . P. rocessing. Smart . Power . Grid. : Scheduling . of Power . Demands for . Optimal Energy Management. Authors:. Iordanis Koutsopoulos . Leandros Tassiulas. Presentation by:. Sanjana. . Hangal. Introduction. Consider a . Task Scheduling in Heterogeneous . Computing . Environments . Senior Design Students: Christopher . Blandin. . and Dylan . Machovec. Post-doctoral Scholar: Bhavesh Khemka. Faculty Advisor: H. J. Siegel. Analysis for Weakly Hard Real-. Time Tasks in Partitioning Scheduling on. Multiprocessor . Systems. Energy Reduction in Weakly Hard Real . Time Systems. Singh Y, . Popli. M, Shukla . Jaypee. . University of Information Technology. Async. . and . Await. ”. Joe Hummel, PhD. joe@joehummel.net. Technical Staff: . Pluralsight. Adjunct Professor: UIC, LUC. http://www.joehummel.net/downloads.html. Motivation. Execution model. Parallel programming with Tasks. Achim Lösch. and Marco . Platzner. {. achim.loesch. , . platzner. }@upb.de. Heterogeneous Compute Node. Contribution:. Novel energy-optimizing list scheduling approach for single heterogeneous compute nodes based on real measurements. Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. Or multiple packets to send, or web requests to serve, or …. Definitions. response time, throughput, predictability. Uniprocessor. policies. FIFO, round robin, optimal. multilevel feedback as approximation of optimal. * istributed E nergy-E fficient cheduler (or DEES for short) that aims to seamlessly integrate the process of scheduling tasks with data placement strategies to provide energy savings. DEES is distr