AI 中文总结
研究针对递归数据库查询的GPU加速，提出基于WebGPU计算着色器的WGLog引擎，利用无原子排序数组连接和异步执行管道两项创新技术，在代表性工作负载上比原生GPU系统加速1.48至4.68倍，远超CPU和WebAssembly实现。
AI 中文摘要
递归查询计算是图算法和关系数据库的核心，因其固有的计算强度需要GPU加速。虽然之前有大量工作解决了需要定点评估的递归查询的GPU实现，但现有系统仅限于原生执行环境。我们引入了WGLog，这是首个用于计算密集型递归数据库查询的原生于网页浏览器的GPU引擎。WGLog完全基于WebGPU计算着色器构建，这是一种跨平台API，可在网页浏览器中实现GPU加速。WGLog利用了两项关键技术创新。首先，我们用无原子排序数组连接取代基于哈希表的连接，消除了哈希表在倾斜图上所遭受的序列化瓶颈。其次，我们使用WebGPU的间接调度功能开发了一个异步执行管道，消除了否则会在每次迭代开销中占主导地位的GPU与主机同步。在代表性工作负载上，WGLog比原生GPU系统实现了1.48至4.68倍的加速，并且相对于CPU和WebAssembly实现有数量级的提升。
英文摘要
Recursive query computation, central to graph algorithms and relational databases, demands GPU acceleration due to its inherent computational intensity. While substantial prior work addresses GPU implementations of recursive queries that require fixed-point evaluation, existing systems are restricted to native execution environments. We introduce WGLog, the first web-browser-native GPU engine for compute-bound recursive database queries. WGLog is built entirely on WebGPU compute shaders, a cross-platform API that enables GPU acceleration in web browsers. WGLog leverages two key technical innovations. First, we replace hash-table-based joins with atomic-free sorted-array joins, eliminating the serialization bottleneck that hash tables suffer on skewed graphs. Second, we develop an asynchronous execution pipeline using WebGPU's indirect dispatch capability, which eliminates GPU-host synchronizations that would otherwise dominate per-iteration overhead. On representative workloads, WGLog delivers a 1.48--4.68x speedup over native GPU systems and orders-of-magnitude improvement over CPU and WebAssembly implementations.
