Alexandria Digital Research Library

Scalable graph algorithms in a high-level language using primitives inspired by linear algebra

Author:
Lugowski, Adam
Degree Grantor:
University of California, Santa Barbara. Computer Science
Degree Supervisor:
John R. Gilbert
Place of Publication:
[Santa Barbara, Calif.]
Publisher:
University of California, Santa Barbara
Creation Date:
2014
Issued Date:
2014
Topics:
Computer Science
Keywords:
Graphs
Parallel computing
Linear algebra
Genres:
Dissertations, Academic and Online resources
Dissertation:
Ph.D.--University of California, Santa Barbara, 2014
Description:

This dissertation advances the state of the art for scalable high-performance graph analytics and data mining using the language of linear algebra. Many graph computations suffer poor scalability due to their irregular nature and low operational intensity. A small but powerful set of linear algebra primitives that specifically target graph and data mining applications can expose sufficient coarse-grained parallelism to scale to thousands of processors.

In this dissertation we advance existing distributed memory approaches in two important ways. First, we observe that data scientists and domain experts know their analysis and mining problems well, but suffer from little HPC experience. We describe a system that presents the user with a clean API in a high-level language that scales from a laptop to a supercomputer with thousands of cores. We utilize a Domain-Specific Embedded Language with Selective Just-In-Time Specialization to ensure a negligible performance impact over the original distributed memory low-level code. The high-level language enables ease of use, rapid prototyping, and additional features such as on-the-fly filtering, runtime-defined objects, and exposure to a large set of third-party visualization packages.

The second important advance is a new sparse matrix data structure and set of algorithms. We note that shared memory machines are dominant both in stand-alone form and as nodes in distributed memory clusters. This thesis offers the design of a new sparse-matrix data structure and set of parallel algorithms, a reusable implementation in shared memory, and a performance evaluation that shows significant speed and memory usage improvements over competing packages. Our method also offers features such as in-memory compression, a low-cost transpose, and chained primitives that do not materialize the entire intermediate result at any one time. We focus on a scalable, generalized, sparse matrix-matrix multiplication algorithm. This primitive is used extensively in many graph algorithms such as betweenness centrality, graph clustering, graph contraction, and subgraph extraction.

Physical Description:
1 online resource (225 pages)
Format:
Text
Collection(s):
UCSB electronic theses and dissertations
ARK:
ark:/48907/f33n21jm
ISBN:
9781321349771
Catalog System Number:
990045117300203776
Rights:
Inc.icon only.dark In Copyright
Copyright Holder:
Adam Lugowski
File Description
Access: Public access
Lugowski_ucsb_0035D_12327.pdf pdf (Portable Document Format)