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Scalable Parallel Computing on Clouds (Dissertation Proposal). Thilina Gunarathne (firstname.lastname@example.org) Advisor : Prof.Geoffrey Fox (email@example.com) Committee : Prof.Judy Qui, Prof.Beth Plale , Prof.David Leake. Research Statement.
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Advisor : Prof.Geoffrey Fox (firstname.lastname@example.org)
Committee : Prof.Judy Qui, Prof.BethPlale, Prof.DavidLeake
Cloud computing environments can be used to perform large-scale parallel computations efficiently with good scalability, fault-tolerance and ease-of-use.
(a) Pleasingly Parallel
(c) Data Intensive Iterative Computations
(d) Loosely SynchronousApplication Types
Many MPI scientific applications such as solving differential equations and particle dynamics
PolarGrid Matlab data analysis
Expectation maximization clustering e.g. Kmeans
Slide from Geoffrey Fox Advances in Clouds and their application to Data Intensive problems University of Southern California Seminar February 24 2012
* We are not focusing on these research issues in the current proposed research. However, the frameworks we develop provide industry standard solutions for each issue.
Cloud Queues for scheduling, Tables to store meta-data and monitoring data, Blobs for input/output/intermediate data storage.
Smaller Loop-Variant Data
Larger Loop-Invariant Data
Map(<key>, <value>, list_of <key,value>)
Reduce(<key>, list_of <value>, list_of <key,value>)
Merge(list_of <key,list_of<value>>,list_of <key,value>)
In-Memory/Disk caching of static data
First iteration through queues
Left over tasks
Data in cache + Task meta data history
New iteration in Job Bulleting Board
Cap3 Sequence Assembly
Input Data Set
Classic Cloud Frameworks
Smith-Waterman-GOTOH to calculate all-pairs dissimilarity
ThilinaGunarathne, Tak-lon Wu, Judy Qui, Geoffrey Fox
Overhead between iterations
First iteration performs the initial data fetch
Speedup gained using data cache
Task Execution Time Histogram
Number of Executing Map Task Histogram
Scales better than Hadoop on bare metal
Increasing number of iterations
Strong Scaling with 128M Data Points
X: Calculate invV (BX)
BC: Calculate BX
Performance adjusted for sequential performance difference
Data Size Scaling
Scalable Parallel Scientific Computing Using Twister4Azure. ThilinaGunarathne, BingJingZang, Tak-Lon Wu and Judy Qiu. Submitted to Journal of Future Generation Computer Systems. (Invited as one of the best 6 papers of UCC 2011)
BLAST Sequence Search
Short Papers / Posters
Map(Key, Value, List of KeyValue-Pairs(broadcast data) ,…)