论文标题
高维散列：稳健有效的动态哈希表
Hyperdimensional Hashing: A Robust and Efficient Dynamic Hash Table
论文作者
论文摘要
大多数云服务和分布式应用程序都依赖于哈希算法，这些算法允许稳健有效的哈希表的动态缩放。示例包括AWS，Google Cloud和Bittorrent。一致和集合的哈希是算法，随着哈希表大小的大小，将键重映射最小化。尽管大规模云部署中的内存错误很常见，但算法都不提供效率和鲁棒性。高维计算是一种具有固有效率，鲁棒性的新兴计算模型，非常适合向量或硬件加速度。我们提出了高度（HD）的哈希，并表明它具有在大型系统中部署的效率。此外，现实的内存错误导致一致的散列不匹配超过20％，而HD哈希仍然不受影响。
Most cloud services and distributed applications rely on hashing algorithms that allow dynamic scaling of a robust and efficient hash table. Examples include AWS, Google Cloud and BitTorrent. Consistent and rendezvous hashing are algorithms that minimize key remapping as the hash table resizes. While memory errors in large-scale cloud deployments are common, neither algorithm offers both efficiency and robustness. Hyperdimensional Computing is an emerging computational model that has inherent efficiency, robustness and is well suited for vector or hardware acceleration. We propose Hyperdimensional (HD) hashing and show that it has the efficiency to be deployed in large systems. Moreover, a realistic level of memory errors causes more than 20% mismatches for consistent hashing while HD hashing remains unaffected.
