ex-mate: data-intensive computing with large reduction objects and its application to graph mining

Ex-MATE: Data-Intensive Computing with Large Reduction Objects and Its Application to Graph Mining

Wei Jiang and Gagan Agrawal

Outline

April 21, 20232

Background System Design of Ex-MATE Parallel Graph Mining with Ex-MATE Experiments Related Work Conclusion

Outline

April 21, 20233


April 21, 20234

Map-Reduce Simple API : map and reduce

Easy to write parallel programs Fault-tolerant for large-scale data centers

Performance? Always a concern for HPC community

Generalized Reduction First proposed in FREERIDE that was developed at Ohio

State 2001-2003 Shared a similar processing structure

The key difference lies in a programmer-managed reduction-object

Better performance?

Background (I)

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Map-Reduce Execution

Comparing Processing Structures

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• Reduction Object represents the intermediate state of the execution• Reduce func. is commutative and associative• Sorting, grouping.. .overheads are eliminated with red. func/obj.

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Our Previous Work A comparative study between FREERIDE and

Hadoop: FREERIDE outperformed Hadoop with factors of 5 to 10 Possible reasons:

Java VS C++? HDFS overheads? Inefficiency of Hadoop? API difference?

Developed MATE (Map-Reduce system with an AlternaTE API) on top of Phoenix from Stanford Adopted Generalized Reduction Focused on API differences MATE improved Phoenix with an average of 50%

Avoids large set of intermediate pairs between Map & Reduce Reduces memory requirements

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Extending MATE Main issues of the original MATE:

Only works on a single multi-core machine Datasets should reside in memory Assumes the reduction object MUST fit in memory

This paper extended MATE to address these limitations Focus on graph mining: an emerging class of apps

Require large-sized reduction objects as well as large-scale datasets

E.g., PageRank could have a 8GB reduction object! Support of managing arbitrary-sized reduction objects

Also reading disk-resident input data Evaluated Ex-MATE using PEGASUS

PEGASUS: A Hadoop-based graph mining system

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Outline

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System Design and Implementation System design of Ex-MATE

Execution overview Support of distributed environments

System APIs in Ex-MATE One set provided by the runtime

operations on reduction objects Another set defined or customized by the users

reduction, combination, etc.. Runtime in Ex-MATE

Data partitioning Task scheduling Other low-level details

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Ex-MATE Runtime Overview Basic one-stage execution

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Implementation Considerations Support for processing very large datasets

Partitioning function: Partition and distribute to a number of nodes

Splitting function: Use the multi-core CPU on each node

Management of a large reduction-object (R.O.): Reduce disk I/O! Outputs (R.O.) are updated in a demand-driven way

Partition the reduction object into splits Inputs are re-organized based on data access

patterns Reuse a R.O. split as much as possible in memory

Example: Matrix-Vector Multiplication

A MV-Multiplication Example

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Output Vector

Input Vector

Input Matrix(1, 1)

(2, 1)

(1, 2)

Outline

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GIM-V for Graph Mining (I) Generalized Iterative Matrix-Vector

Multiplication(GIM-V) Proposed at CMU at first Similar to the common MV Multiplication

MV Mul. : Three operations in

GIM-V: combine m(i, j) and v(j) :

Not have to be a multiplication combineAll n partial results for the element i :

Not have to be the sum assign v(new) to v(i) :

The previous value of v(i) is updated by a new value

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Multiplication

Sum

Assignment

GIM-V for Graph Mining (II) A set of graph mining applications can fit

into this GIM-V PageRank, Diameter Estimation, Finding

Connected Components, Random Walk with Restart, etc..

Parallelization of GIM-V: Use Map-Reduce in PEGASUS

A two-stage algorithm: two consecutive map-reduce jobs

Use Generalized Reduction in Ex-MATE A one-stage algorithm: simpler code

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GIM-V Example: PageRank PageRank is used by Google to calculate the

relative importance of web-pages: Direct implementation of GIM-V: v(j) is the ranking

value The three customized operations are:

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Multiplication

Sum

Assignment

GIM-V: Other Algorithms Diameter Estimation: HADI is an algorithm to

estimate the diameter of a given graph The three customized operations are:

Finding Connected Components: HCC is a new algorithm to find the connected components of large graphs The three customized operations are:

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Multiplication

Bitwise-or

Bitwise-or

Multiplication

Minimal

Minimal

Parallelization of GIM-V (I) Using Map-Reduce: Stage I

Map:

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Map M(i,j) and V(j) to reducer j

Parallelization of GIM-V (II) Using Map-Reduce: Stage I (cont.)

Reduce:

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Map “combine2(M(i,j) , V(j)) “to reducer i

Parallelization of GIM-V (III) Using Map-Reduce: Stage II

Map:

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Parallelization of GIM-V (IV) Using Map-Reduce: Stage II (cont.)

Reduce:

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Parallelization of GIM-V (V) Using Generalized Reduction in Ex-MATE:

Reduction:

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Parallelization of GIM-V (VI) Using Generalized Reduction in Ex-MATE:

Finalize:

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Outline

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Applications: Three graph mining algorithms:

PageRank, Diameter Estimation, and Finding Connected Components

Evaluation: Performance comparison with PEGASUS

PEGASUS provides a naïve version and an optimized version

Speedups with an increasing number of nodes Scalability speedups with an increasing size of

datasets Experimental platform:

A cluster of multi-core CPU machines Used up to 128 cores (16 nodes)

Experiments Design

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Results: Graph Mining (I) PageRank: 16GB dataset; a graph of 256

million nodes and 1 billion edgesA

vg

. Tim

e P

er

Itera

tion

(m

in)

# of nodes

10.0 speedup

April 21, 202328

Results: Graph Mining (II) HADI: 16GB dataset; a graph of 256 million

nodes and 1 billion edgesA

vg

. Tim

e P

er

Itera

tion

(m

in)

# of nodes

11.0 speedup

April 21, 202329

Results: Graph Mining (III) HCC: 16GB dataset; a graph of 256 million


vg

. Tim

e P

er

Itera

tion

(m

in)

# of nodes

9.0 speedup

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Scalability: Graph Mining (IV) HCC: 8GB dataset; a graph of 256 million

nodes and 0.5 billion edgesA

vg

. Tim

e P

er

Itera

tion

(m

in)

# of nodes

1.7 speedup

1.9 speedup

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Scalability: Graph Mining (V) HCC: 32GB dataset; a graph of 256 million


vg

. Tim

e P

er

Itera

tion

(m

in)

# of nodes

1.9 speedup

2.7 speedup

April 21, 202332

Scalability: Graph Mining (VI) HCC: 64GB dataset; a graph of 256 million


vg

. Tim

e P

er

Itera

tion

(m

in)

# of nodes

1.9 speedup

2.8 speedup

Observations

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Performance trends are similar for all three applications Consistent with the fact that all three applications

are implemented using the GIM-V method Ex-MATE outperforms PEGASUS significantly

for all three graph mining algorithms Reasonable speedups for different datasets Better scalability for larger datasets with a

increasing number of nodes

Outline

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Related Work: Academia

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Evaluation of Map-Reduce-like models in various parallel programming environments: Phoenix-rebirth for large-scale multi-core machines Mars for a single GPU MITHRA for GPGPUs in heterogeneous platforms Recent IDAV for GPU clusters

Improvement of Map-Reduce API: Integrating pre-fetch and pre-shuffling into Hadoop Supporting online queries Enforcing a less restrictive synchronization

semantics between Map and Reduce

Related Work: Industry

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Google’s Pregel System: Map-reduce may not so suitable for graph

operations Proposed to target graph processing Open source version: HAMA project in Apache

Variants of Map-Reduce: Dryad/DryadLINQ from Microsoft Sawzall from Google Pig/Map-Reduce-Merge from Yahoo! Hive from Facebook

Outline

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Conclusion Ex-MATE supports the management of

reduction objects of arbitrary sizes Deals with disk-resident reduction objects

Outperforms PEGASUS for both the naïve and optimized implementations for all three graph mining application Has a simpler code

Offers a promising alternative for developing efficient data-intensive applications, Uses GIM-V for parallelizing graph mining

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Thank You, and Acknowledgments Questions and comments

Wei Jiang - [email protected] Gagan Agrawal - [email protected]

This project was supported by:

ex-mate: data-intensive computing with large reduction objects and its application to graph mining

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