StarPlat: A versatile DSL for graph analytics

• Published in: Journal of parallel and distributed computing Vol. 194; p. 104967
• Main Authors: Behera, Nibedita, Kumar, Ashwina, Rajadurai T, Ebenezer, Nitish, Sai, M, Rajesh Pandian, Nasre, Rupesh
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2024
• Subjects: CUDA; Domain-specific language; Graph algorithms; MPI; OpenMP; CUDA; Domain-specific language; Graph algorithms; OpenMP; MPI
• ISSN: 0743-7315
• DOI: 10.1016/j.jpdc.2024.104967

Graphs model several real-world phenomena. With the growth of unstructured and semi-structured data, parallelization of graph algorithms is inevitable. Unfortunately, due to inherent irregularity of computation, memory access, and communication, graph algorithms are traditionally challenging to parallelize. To tame this challenge, several libraries, frameworks, and domain-specific languages (DSLs) have been proposed to reduce the parallel programming burden of the users, who are often domain experts. However, existing frameworks to model graph algorithms typically target a single architecture. In this paper, we present a graph DSL, named StarPlat, that allows programmers to specify graph algorithms in a high-level format, but generates code for three different backends from the same algorithmic specification. In particular, the DSL compiler generates OpenMP for multi-core systems, MPI for distributed systems, and CUDA for many-core GPUs. Since these three are completely different parallel programming paradigms, binding them together under the same language is challenging. We share our experience with the language design. Central to our compiler is an intermediate representation which allows a common representation of the high-level program, from which individual backend code generations begin. We demonstrate the expressiveness of StarPlat by specifying four graph algorithms: betweenness centrality computation, page rank computation, single-source shortest paths, and triangle counting. Using a suite of ten large graphs, we illustrate the effectiveness of our approach by comparing the performance of the generated codes with that obtained with hand-crafted library codes. We find that the generated code is competitive to library-based codes in many cases. More importantly, we show the feasibility to generate efficient codes for different target architectures from the same algorithmic specification of graph algorithms. •Domain-specific language for graph algorithms.•Targets multiple backends (CPU, GPU, distributed systems).•Performance close to hand-tuned codes.