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Report
Mizan: Optimizing Graph Mining
in Large Parallel Systems
Panos Kalnis
King Abdullah University of Science and Technology (KAUST)
H. Jamjoom (IBM Watson) and Z. Khayyat, K. Awara (KAUST)
Graphs: Are they Important?
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Graphs are everywhere
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KAUST
Internet Web graph
Social networks
Biological networks
Processing graphs
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Find patterns, rules, anomalies
Rank web pages
‘Viral' or 'word-of-mouth' marketing
Identify interactions among proteins
Computer security: anomalies in email traffic
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Graph Research in InfoCloud
 FD3:
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RDF query engine
Distributed
On-the-fly placement and indexing
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Panos
isA
works
Yasser
studies
isA
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GraMi: Graph mining
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student
E.g., find frequent subgraphs
Mizan
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professor
Framework for executing graph algorithms
Distributed, large-scale
GOAL: Graph DBMS
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Existing Graph-processing Frameworks
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Map-Reduce based
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HADI, Pegasus
Message passing
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Pregel
Specialized graph engines
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Parallel Boost Graph Library (pBGL)
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PageRank with Map-Reduce
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Map-2
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Map-3
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Map-1
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Reduce-2
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Reduce-3
Map-1
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Reduce-1
Write on
HDFS
Map-2
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Write on
HDFS
Reduce-1
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2 v2
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v2
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Reduce-2
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Map-3
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Reduce-3
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Pregel[1]
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Bulk Synchronous Parallel model
Statefull model: long-lived processes compute,
communicate, and modify local state
 vs. data-flow model: process computes solely
on input data and produces output data
[1] G. Malewich et al., Pregel: a system for large scale graph processing, SIGMOD, 2010
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Pregel Example: MAX
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Example
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from [Malewich et al., SIGMOD, 2010]
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Mizan - Overview
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Min-cut partitioning of input graph
Point-to-point message passing
Good for power-law graphs
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Random partitioning of input
Ring overlay message passing
Good for non-power-law graphs
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α – Minimum-Cut Partitioning
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METIS [2]
[2] Karypis and Kumar, “Multilevel k-way Partitioning Scheme for Irregular Graphs”, JPDC, 1998
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α – Percentage of Edge Cuts
with Minimum-Cut Partitioning
Power-law
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Non-Power-law
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α – Node Replication
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α – Percentage of Edge Cuts
with Node Replication
Power-law
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Non-Power-law
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Partition
User’s code
Cost of Min-Cut Partitioning
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γ – Message-passing in a Ring
Point-to-Point communication
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Ring-based communication
Mizan-γ
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Optimizer
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α  Partitioning cost (min-cut)
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Pays off for power-law graphs
γ  Latency due to the ring
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Each message must be needed by many nodes
Good for non-power law graphs
Is the input power-law?
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Take a random sample
Use [2] to compare with theoretical
power-law distribution
Compute pValue
0.1 ≤ pValue < 0.9 Power-law
[2] A. Clauset et al., Power-Law Distributions in Empirical Data. SIAM Review, 51(4), 2009.
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Real
Synthetic
Datasets & Optimizer’s Decisions
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Example: Diameter Estimation
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Non-Power-law
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EC2 instances, Diameter estimation
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Power-law
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EC2 instances, Diameter estimation
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Cloud Computing in KAUST
KAUST
Scientific & commercial Applications
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IBM BlueGene/P – 3D Torus Network
KAUST
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IBM-BlueGene/P vs. Amazon EC2
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IBM/P: 850MHz
EC2: 2.4GHz
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Points to remember
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Mizan: Framework for graph algorithms in
large scale computing infrastructures
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α: Power-law graphs
γ: Non-power-law graphs
Runs on cloud and on supercomputers
To do list:
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Dynamic graph placement
Hybrid (alpha and gamma)
Better optimizer
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KAUST
Questions?
http://cloud.kaust.edu.sa

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