GPIR: Enabling Practical Private Information Retrieval with GPUs

• URL: http://arxiv.org/abs/2604.04696v1
• Date: Mon, 06 Apr 2026 14:04:14 GMT
• Title: GPIR: Enabling Practical Private Information Retrieval with GPUs
• Abstract summary: GPIR is a GPU-accelerated PIR system that rethinks kernel, data layout, and execution scheduling.<n>We introduce a stage-aware hybrid execution model that switches between operation-level kernels, which execute each primitive operation separately, and stage-level kernels, which fuse all operations within a protocol stage into a single kernel to maximize on-chip data reuse.
• Score: 10.979759633611133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Private information retrieval (PIR) allows private database queries but is hindered by intense server-side computation and memory traffic. Modern lattice-based PIR protocols typically involve three phases: ExpandQuery (expanding a query into encrypted indices), RowSel (encrypted row selection), and ColTor (recursive "column tournament" for final selection). ExpandQuery and ColTor primarily perform number-theoretic transforms (NTTs), whereas RowSel reduces to large-scale independent matrix-matrix multiplications (GEMMs). GPUs are theoretically ideal for these tasks, provided multi-client batching is used to achieve high throughput. However, batching fundamentally reshapes performance bottlenecks; while it amortizes database access costs, it expands working sets beyond the L2 cache capacity, causing divergent memory behaviors and excessive DRAM traffic. We present GPIR, a GPU-accelerated PIR system that rethinks kernel design, data layout, and execution scheduling. We introduce a stage-aware hybrid execution model that dynamically switches between operation-level kernels, which execute each primitive operation separately, and stage-level kernels, which fuse all operations within a protocol stage into a single kernel to maximize on-chip data reuse. For RowSel, we identify a performance gap caused by a structural mismatch between NTT-driven data layouts and tiled GEMM access patterns, which is exacerbated by multi-client batching. We resolve this through a transposed-layout GEMM design and fine-grained pipelining. Finally, we extend GPIR to multi-GPU systems, scaling both query throughput and database capacity with negligible communication overhead. GPIR achieves up to 305.7x higher throughput than PIRonGPU, the state-of-the-art GPU implementation.

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