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Architecture and Performance of Runtime Environments for Data Intensive Scalable Computing
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Some of my Ph.D. research presented at the Doctoral Showcase of the SuperComputing2009.
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Architecture and Performance of Runtime Environments for Data Intensive Scalable Computing
1.
2.
3.
Represented as filter
pipelines
4.
5.
6.
7.
8.
K-Means Clustering
9.
Matrix Multiplication
10.
11.
12.
13.
Distinction on static
data and variable data (data flow vs. δ flow)
14.
Cacheable map/reduce tasks
(long running tasks)
15.
Combine operation
16.
17.
18.
19.
Eliminates file based
communication
20.
Cacheable map/reduce tasks
21.
Static data remains
in memory
22.
User Program is
the composer of MapReduce computations
23.
Extends the MapReduce
model to iterative computations
24.
A limitation:
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
Notes de l'éditeur
Currently uses NaradaBrokering, but it is easily extensible to use any other pub/sub message infrastructure such as Apache ActiveMQ.