'Data locality' diaporamas de présentation

Parallel Programming Using the Global Arrays Toolkit

Parallel Programming Using the Global Arrays Toolkit. Bruce Palmer , Jeff Daily, Abhinav Vishnu, Sriram Krishnamoorthy, Daniel Chavarria Pacific Northwest National Laboratory. Global Arrays. Developed over 20 years

1.57k views • 135 slides

(Mis) Understanding the NUMA Memory System Performance of Multithreaded Workloads

944 views • 76 slides

Windows Kernel Internals User-mode Heap Manager

Windows Kernel Internals User-mode Heap Manager. David B. Probert, Ph.D. Windows Kernel Development Microsoft Corporation. Topics. Common problems with the NT heap LFH design Benchmarks data Heap analysis. Default NT Heap. Unbounded fragmentation for the worst scenario:

857 views • 55 slides

Hadoop Overview

786 views • 52 slides

High Performance Integration of Data Parallel File Systems and Computing

High Performance Integration of Data Parallel File Systems and Computing. Zhenhua Guo. Outline. Introduction and Motivation Literature Survey Research Issues and Our Approaches Contributions. Traditional HPC Architecture vs. the Architecture of Data Parallel Systems. HPC Arch.

578 views • 50 slides

WINTER

Kyonggi University. DBLAB. Haesung Lee. WINTER. Template. Distributed Computing at Web Scale. Introduction. Data analysis at a large scale. Very large data collections (TB to PB) stored on distributed file systems Query logs Search engine indexed Sensor data

652 views • 49 slides

Matchmaking: A New MapReduce Scheduling Technique

Matchmaking: A New MapReduce Scheduling Technique. Chen He Dr. Ying Lu Dr. David Swanson. Problem Statement. MapReduce cluster scheduling algorithm becomes increasingly important Efficient MapReduce scheduler must avoid unnecessary data transmission

804 views • 48 slides

SIMD Parallelization of Applications that Traverse Irregular Data Structures

SIMD Parallelization of Applications that Traverse Irregular Data Structures. Bin Ren , Gagan Agrawal , James R. Larus , Todd Mytkowicz , Tomi Poutanen , Wolfram Schulte Microsoft Research ? Redmond The Ohio State University. Contributions.

643 views • 33 slides

Matei Zaharia , Dhruba Borthakur * , Joydeep Sen Sarma * ,

Delay Scheduling. A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling. Matei Zaharia , Dhruba Borthakur * , Joydeep Sen Sarma * , Khaled Elmeleegy + , Scott Shenker , Ion Stoica. UC Berkeley, * Facebook Inc, + Yahoo! Research. Motivation.

381 views • 26 slides

An Introduction to Sector/Sphere

Sector & Sphere. An Introduction to Sector/Sphere. Yunhong Gu Univ. of Illinois at Chicago and VeryCloud LLC @CHUG, June 22, 2010. What is Sector/Sphere?. Sector: Distributed File System Sphere: Simplified Parallel Data Processing Framework Goal: handling big data on commodity clusters

366 views • 24 slides

Optimal Biomolecular simulations in heterogeneous computing architectures

Optimal Biomolecular simulations in heterogeneous computing architectures. Scott Hampton, Pratul Agarwal. Funding by:. 10 -15 s. 10 -12. 10 -9. 10 -6. 10 -3. 10 0 s. Motivation: Better time-scales. Molecular dynamics (MD). 2010. 2005. 10 -15 s. 10 -12. 10 -9. 10 -6. 10 -3.

419 views • 22 slides

Bi-Hadoop: Extending Hadoop To Improve Support For Binary-Input Applications

Bi-Hadoop: Extending Hadoop To Improve Support For Binary-Input Applications. Xiao Yu and Bo Hong School of Electrical and Computer Engineering Georgia Institute of Technology. Hadoop.

394 views • 21 slides

Shuai Ma , Yang Cao, Jinpeng Huai , Tianyu Wo

Distributed Graph Pattern Matching. Shuai Ma , Yang Cao, Jinpeng Huai , Tianyu Wo. Graphs are everywhere , and quite a few are huge graphs!. Social Networks. File systems. Databases. World Wide Web. Graph searching is a key to social searching engines!. Graph Pattern Matching.

367 views • 20 slides

Improving Parallelism and Locality with Asynchronous Algorithms

Lixia Liu, Zhiyuan Li Purdue University, USA PPOPP 2010, January 2009. Improving Parallelism and Locality with Asynchronous Algorithms. Background. Multicore architecture Multiple cores per chip Modest on-chip caches Memory bandwidth issue Increasing gap between CPU speed and

292 views • 20 slides

MapReduce : Simplified Data Processing on Large Clusters

Jonathan Light. MapReduce : Simplified Data Processing on Large Clusters. Contents. Abstract Introduction Locality Task Granularity Backup Tasks Questions. Abstract: MapReduce. MapReduce processes and generates large data sets

238 views • 13 slides

Execution Framework

Execution Framework. Based on the text by Jimmy Lin and Chris Dryer; and on the yahoo tutorial on mapreduce at http://developer.yahoo.com/hadoop/tutorial/index.html. Execution Framework: HDFS. Namenode responsibilities: Namespace management: file name, locations, access privileges etc.

195 views • 10 slides

PROOF - Parallel ROOT Facility

PROOF - Parallel ROOT Facility. Maarten Ballintijn, Rene Brun, Fons Rademakers, Gunter Roland http://root.cern.ch. Bring the KB to the PB not the PB to the KB. Slave. Slave. Master. Slave. Slave. Internet. PROOF in a Slide.

167 views • 8 slides

GGF10 Workflow Workshop Summary

207 views • 7 slides

Increasing DC Efficiency by 4x

Increasing DC Efficiency by 4x. Berkeley RAD Lab 2008-06-04 JamesRH@microsoft.com. Agenda. Background, Definitions, & Goal Power Distribution Optimization Mechanical Optimization Critical Load (the servers) Optimization. Background, Definitions & Goal. PUE: Power Utilization Efficiency

146 views • 7 slides

194 views • 6 slides