Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite

Conference · Thu Nov 19 00:00:00 EST 2020

OSTI ID:1755113

Azad, Md Ariful ^[1]; Aznaveh, Mohsen M. ^[2]; Beamer, Scott ^[3]; Blanco, Mark ^[4]; Chen, Jinhao ^[2]; D'Alessandro, Luke ^[5]; Dathathri, Roshan ^[6]; Davis, Tim ^[2]; Deweese, Kevin ^[7]; Firoz, Jesun S. ^[8]; Gabb, Henry ^[9]; Gill, Gurbinder ^[6]; Hegyi, Balint ^[10]; Kolodziej, Scott ^[2]; Low, Tze M. ^[4]; Lumsdaine, Andrew  ^[8]; Manlaibaatar, Tugsbayasgalan ^[11]; Mattson, Timothy G. ^[9]; Mcmillan, Scott ^[4]; Peri, Ramesh ^[9] ; Pingali, Keshav ^[6]; Sridhar, Upasana ^[4]; Szarnyas, Gabor ^[10]; Zhang, Yunming ^[11]; Zhang, Yongzhe ^[12]

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1. VISITORS
2. Texas A & M University
3. University of California, Santa Cruz
4. Carnegie Mellon University
5. Indiana University-Bloomington
6. University of Texas at Austin
7. University of Washington
8. BATTELLE (PACIFIC NW LAB)
9. Intel
10. Budapest Univ of Econ Sci and Pub Admin
11. Massachusetts Institute of Technology
12. The Graduate University for Advanced Studies, SOKENDAI

The analysis of connected data is an increasingly important application in high-performance computing. Such analyses can reveal fraudulent patterns in financial transactions, optimize telecommunications networks, predict information flow in social networks, etc. However, the landscape of graph analytics is highly diverse. Graph algorithms stress processor architectures differently, and no one graph can represent all topologies. Consequently, no single approach or framework is expected to be optimal for all graph analytics problems. To help make sense of this diverse landscape, we evaluated four approaches to graph analytics: GraphBLAS, Galois, BGL17, GraphIt; and compare them against hand-tuned implementations that take advantage of hardware features on our test platform. Graph- BLAS formulates graph analytics as sparse linear algebra. Galois provides syntactic constructs for data parallelism over irregular data structures. BGL17 is a generic C++ template library for implementing graph algorithms. GraphIt provides a domain- specific language to describe and optimize graph algorithms. We use the GAP Benchmark Suite to establish baseline performance and guide the side-by-side evaluation of each framework. GAP consists of 30 tests: six graph analytics algorithms (breadth- first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) run on five graphs, each with different topological characteristics (e.g., high diameter, skewed degree distribution, high average degree). High-performance reference implementations are included for each benchmark algorithm. Because a graph can be loaded into memory a number of ways (e.g., flat file on disk, compressed sparse format, data frames, retrieved from SQL or NoSQL databases), our evaluation focused on computational performance rather than I/O. Our results show the relative strengths of each framework.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1755113
• Report Number(s): PNNL-SA-154466
• Resource Relation: Conference: IEEE International Symposium on Workload Characterization (IISWC 2020), October 27-30, 2020, Beijing, China
• Country of Publication: United States
• Language: English

Related Subjects

Graph algorithms
Benchmarking
shared-memory programming