1. Miraj Kheni Authors: Toyotaro Suzumura, Koji Ueno
ScaleGraph:A High-Performance Library for Billion-Scale Graph Analytics
Miraj Kheni
Authors: Toyotaro Suzumura, Koji Ueno

2. ScaleGraph: Motivations
Growing interest in the field of large-scale graph mining
Achieving high performance and low software complexity at the same time
Harnessing performance and productivity of X10

3. ScaleGraph: Related Work
Parallel Boost Graph Library (PBGL)
Google’s Pregel
Apache Giraph
GraphLab

4. ScaleGraph: X10 Overview
A type-safe, imperative, concurrent, object-oriented language
Asynchronous partitioned global address space (APGAS) programming model
Key concepts:
place: analogous to process
activity: analogous to lightweight thread
activity can access remote data using at keyword or X10.util.Team class
Seamless migration of an activity to different place
Supports Clik-style fork-join programming
Async, Finish

5. ScaleGraph: Improved X10 Team Class
X10 Team: contains routines for collective communication
ScaleGraph proposes a new X10 Team class that optimizes collective communication by not emulating X10RT
Implements both blocking and non-blocking collective communication routines that directly uses MPI collective communication routines.

6. ScaleGraph: New Memory Management System
X10 back-end uses Boehm-Demers-Weiser conservative garbage collector (BDWGC)
Does not differentiate small and large memory allocation requests
Authors proposes EMM (Explicitly Managed Memory)
For small memory allocation- use BDWGC and for large memory allocation- explicitly use malloc and free

7. ScaleGraph 2: Overview
Implemented new readers and writes for parallel IO
Implemented new X10 Team
Implemented XPregel framework
Three main components:
XPregel framework
Basic linear Algebra Subprograms (BLAS)
File IO
Provides a number of graph algorithms

8. ScaleGraph: Graph Representation
Distributed Graph Object
Distributed mutable lists of edges, vertex attributes and edge attributes
Does not support query operations on graph
Distributed Sparse Matrix
Efficient way for storing immutable, sparse, adjacency matrix
Supports querying vertex neighbors, vertex attributes and edge attributes

9. ScaleGraph: Graph Representation
Distributions of distributed sparse
Matrix on four places:
1D Row wise (R=4, C=1)
1D Column wise (R=1, C=4)
2D Block (R=2, C=2)

10. ScaleGraph: XPregel Framework
Pregel Computation Model
Supersteps- during each superstep, a user-defined function is invoked for each vertex
Three main interfaces- Message Passing, Combiner and Aggregator
XPregel interface:

11. ScaleGraph: More Optimizations
SendAll Optimization Technique
Aims at reducing number of messages when a vertex sends the same massage to all of its neighbors
If enabled, the vertex will send only one message to destination place, which in turn would duplicate the message before passing it to respective vertices
BLAS for Sparse Matrix
For graph problems that can be expressed using repeated matrix-vector multiplication

12. ScaleGraph: Evaluation
Updated X10 Team:

13. ScaleGraph: Evaluation
Weak Scaling:
Strong Scaling:

14. ScaleGraph: Evaluation
Strong Scaling: ScaleGraph vs Giraph vs PBGL

15. Conclusion Use of X10 – helps in productivity and performance
Use of optimized X10 Team – optimizes collective communications
Introduces EMM – better memory management
XPregel’s SendAll – Reduces duplication of messages
These optimizations helps ScaleGraph in achieving high productivity, scalability and productivity.

16. Thank You