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Accelerating Iterative Relational Algebra Operations with WebGPU (poster)
Authors: Lu, J., Kumar, S.
Publication: The 12th Greater Chicago Area Systems Research Workshop (GCASR), Chicago, IL URL: https://gcasr.org/2025/posters Bottom‐up logic programming (e.g., Datalog) runs via repeated relational‐algebra kernels - selection, projection, join, and reorder - until no new facts emerge. While GPUs promise massive parallelism for these primitives, existing engines remain CPU-bound (8–16 threads) and struggle with deduplication and growing result sets. We introduce a WebGPU-based hash-join pipeline that: - Maps each RA primitive (hash‐join, GPU radix sort, deduplication, set difference, insertion into the full relation) to its own WGSL compute shader - Uses dynamic, growable GPU buffers and load-factor-tuned hash tables for memory management - Drives a delta-only fixed-point loop, processing only newly discovered tuples each iteration Date: May 8, 2025 Document: View PDF |
