While private information retrieval (PIR) enables private database services by fully concealing access patterns, it simultaneously requires high computational throughput, large memory capacity, and substantial memory bandwidth. We introduce VIPIR, a versatile GPU framework that co-designs PIR protocols with GPU acceleration. We develop a unified analytic model showing that state-of-the-art PIR protocols fall into two categories with complementary limitations, and propose two protocols that flexibly combine techniques across these categories, overcoming the limitations of both classes. These protocols incorporate a GPU-friendly data compression method called expansion-based ring packing (ExpPack), which offers a high degree of parallelism and minimal communication cost. VIPIR applies further optimizations to core operations, including number-theoretic transforms (NTTs) and various matrix-matrix multiplications (GEMMs). Notably, we develop a tensor-core-based execution method for database multiplication by interpreting it as a mixed-integer-type GEMM. We also design memory-efficient scheduling methods that minimize intermediate buffers and enable multi-GPU scaling under memory capacity constraints. Overall, VIPIR achieves orders-of-magnitude higher throughput than prior PIR systems while reducing communication and memory overheads, making large-scale PIR practical.

翻译：尽管隐私信息检索（PIR）通过完全隐藏访问模式实现了隐私数据库服务，但它同时要求高计算吞吐量、大内存容量和充足的内存带宽。我们提出了VIPIR，一种将PIR协议与GPU加速协同设计的通用GPU框架。我们开发了一个统一的分析模型，表明现有最先进的PIR协议可分为两类，且各自存在互补的局限性，并提出了两种协议，灵活地结合了这两类协议的技术，克服了两者的缺陷。这些协议采用了一种GPU友好的数据压缩方法——基于扩展的环打包（ExpPack），该方法具有高并行度和极低的通信成本。VIPIR进一步优化了核心操作，包括数论变换（NTT）和多种矩阵乘法（GEMM）。特别地，我们通过将数据库乘法解释为混合整数类型的GEMM，开发了一种基于张量核心的执行方法。我们还设计了内存高效的调度方案，以最小化中间缓冲区，并在内存容量限制下实现多GPU扩展。总体而言，VIPIR在降低通信和内存开销的同时，实现了比先前PIR系统高出数个数量级的吞吐量，使得大规模PIR变得实用。