The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Ahmedur Rahman Shovon (Argonne National Laboratory (ANL)), Sidharth Kumar (University of Illinois Chicago)
Supervisor:
Abstract: Exascale computing, powered by GPUs, is reshaping high-performance computing. Declarative languages such as Datalog naturally benefit from this shift, as recursive rules can be compiled into GPU-optimized relational operations. Unlike SQL, Datalog executes queries iteratively until a fixed point is reached, making it ideal for graph mining, deductive database, and symbolic AI. Existing engines (SLOG, LogicBlox, and Soufflé) target multi-core architectures and lack support for distributed multi-GPU systems. We address this gap with MNMGDatalog, the first multi-node, multi-GPU Datalog engine, which combines CUDA for intra-node parallelism with MPI for inter-node communication. Our design introduces GPU-parallel joins, scalable recursive aggregation, and iterative all-to-all communication strategies. To assess performance and efficiency, we developed Powerlog, the first GPU-based Datalog engine energy profiler. Experiments on Argonne’s Polaris supercomputer show up to 32× speedups over state-of-the-art distributed engines and reveal tradeoffs between scaling and energy use, establishing a foundation for energy-aware declarative analytics at scale.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF