AI 中文总结
该研究提出基于Project Panama Vector API的SIMD位切片框架，可直接处理压缩数据流，大幅降低内存开销并提升谓词评估与查询处理的速度。
AI 中文摘要
传统Java对象模型因对象头和内部填充会产生大量内存开销，常成为数据密集型分布式系统的性能瓶颈。本文提出一种新型框架，利用Project Panama Vector API直接对位切片压缩数据流执行谓词评估。通过将标准行导向数据转置为并行位平面，该框架无需预先解压即可使用SIMD（单指令多数据）指令评估复杂过滤器，支持整数、长整型（时间戳）、双精度浮点数（通过IEEE 754保序变换）及字符串（通过字典编码）。基准测试显示，该框架内存占用最多降低8倍，同时保持或超过未压缩标准Java集合的吞吐量；在5000万行的TPCDS建模数据上进行端到端评估，5种典型的过滤器密集型查询模式相比标量扫描实现2.4至10.8倍加速，对TPCDS列的扩展类型基准测试显示，时间戳、十进制数及字典编码字符串的加速比为1.5至43倍。
英文摘要
Traditional Java object models introduce significant memory overhead due to object headers and internal padding, often leading to performance bottlenecks in data-intensive distributed systems. This paper presents a novel framework that utilizes the Project Panama Vector API to perform predicate evaluation directly over bit-sliced, compressed data streams. By transposing standard row-oriented data into parallel bit-planes, we demonstrate a mechanism to evaluate complex filters using SIMD (Single Instruction, Multiple Data) instructions without requiring prior decompression. The framework supports integers, longs (timestamps), doubles (via IEEE 754 order-preserving transformation), and strings (via dictionary encoding). Our benchmarks indicate a reduction in memory footprint by up to 8x while maintaining or exceeding the throughput of uncompressed standard Java collections. End-to-end evaluation on TPCDS-modeled data at 50M rows demonstrates 2.4-10.8x speedup over scalar scans across five representative filter-heavy query patterns, with extended type benchmarks on TPCDS columns showing 1.5-43x speedups for timestamps, decimals, and dictionary-encoded strings.
Comments25 pages, 8 figures, 12 tables
