Palgol: A High-Level DSL for Vertex-Centric Graph Processing with Remote Data Access
Yongzhe Zhang, Hsiang-Shang Ko, Zhenjiang Hu

TL;DR
Palgol is a high-level domain-specific language for vertex-centric graph processing that supports remote data access, enabling easier implementation of complex algorithms with dynamic communication in Pregel-like systems.
Contribution
Palgol introduces a declarative syntax with chain access for remote data, and provides a compilation algorithm to generate efficient Pregel code, addressing limitations of existing DSLs.
Findings
Palgol enables implementation of complex Pregel algorithms with dynamic communication.
The efficiency of Palgol-generated code is comparable to hand-written Pregel implementations.
Palgol simplifies programming for large-scale graph algorithms with multiple computation stages.
Abstract
Pregel is a popular distributed computing model for dealing with large-scale graphs. However, it can be tricky to implement graph algorithms correctly and efficiently in Pregel's vertex-centric model, especially when the algorithm has multiple computation stages, complicated data dependencies, or even communication over dynamic internal data structures. Some domain-specific languages (DSLs) have been proposed to provide more intuitive ways to implement graph algorithms, but due to the lack of support for remote access --- reading or writing attributes of other vertices through references --- they cannot handle the above mentioned dynamic communication, causing a class of Pregel algorithms with fast convergence impossible to implement. To address this problem, we design and implement Palgol, a more declarative and powerful DSL which supports remote access. In particular, programmers…
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Taxonomy
TopicsGraph Theory and Algorithms · Cloud Computing and Resource Management · Advanced Graph Neural Networks
