Syracuse PhD Thesis: Scaling Logical Reasoning on GPUs to Break CPU Bottlenecks
moyix · x · 2026-08-10
A PhD dissertation from Syracuse University explores leveraging modern High-Performance Computing (HPC) hardware, particularly GPUs, for massive deductive logic reasoning workloads.
The thesis highlights that traditional logic query languages like Datalog face architectural ceilings when handling industrial-scale tasks such as static program analysis and reverse engineering. Single-node CPUs struggle with memory bandwidth and parallel throughput, while traditional join algorithms often exhaust device memory.
To overcome these limitations, the author introduces four novel Datalog engines. By co-designing storage layouts, indexing strategies, and join algorithms specifically for the GPU's SIMT execution model, the research demonstrates that lock-free, GPU-native evaluation can decisively outperform state-of-the-art CPU baselines.
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