Scalable concurrent hash tables via relativistic programming

Abstract

We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow ReadCopy Update (RCU) hash tables to maintain constanttime performance as the number of entries grows, and reclaim memory as the number of entries decreases, without delaying or disrupting readers. We call the resulting data structure a relativistic hash table.Benchmarks of relativistic hash tables in the Linux kernel show that lookup scalability during resize improves 125x over reader-writer locking, and 56% over Linux's current state of the art. Relativistic hash lookups experience no performance degradation during a resize. Applying this algorithm to memcached removes a scalability limit for get requests, allowing memcached to scale linearly and service up to 46% more requests per second.Relativistic hash tables demonstrate the promise of a new concurrent programming methodology known as relativistic programming. Relativistic programming makes novel use of existing RCU synchronization primitives, namely the wait-for-readers operation that waits for unfinished readers to complete. This operation, conventionally used to handle reclamation, here allows ordering of updates without read-side synchronization or memory barriers.

Intrusion Detection and Defense Implementation (IDDI) in Cuckoo Hashing

“…This is, up to the preliminary randomness, each set of elements has a completely unique memory representation. Practical records-unbiased dynamic dictionary based on cuckoo hashing [7]. In a records-unbiased records structure, the reminiscence representation at any factor in time yields no statistics at the specific sequence of insertions and deletions that caused its contemporary content material, apart from the content material itself.…”

“…In a records-unbiased records structure, the reminiscence representation at any factor in time yields no statistics at the specific sequence of insertions and deletions that caused its contemporary content material, apart from the content material itself. This type of property is significant while stopping unintended leakage of information, and was also found beneficial in several algorithmic settings [8]. M. Zukowski, S. H´eman, and P. Boncz, et al in their architecture conscious hashing explains rapid hash features, ways to efficiently deal with multi-column keys and endorse the usage of a currently brought hashing scheme called Cuckoo Hashing over the normally used bucket-chained hashing [9].…”

Asynchronized Concurrency

“…A large body of work has been dedicated to the design of efficient CSDSs [7,15,17,20,25,29,32,41,43,44,46,49,50,52,53]. These efforts usually aim at optimizing a specific CSDS (e.g., BST) in terms of throughput, for a specific set of workloads and platforms.…”

An Evaluation of Strict Timestamp Ordering Concurrency Control for Main-Memory Database Systems

“…Optimizing index structures for concurrency is an active and complex topic of research. Transactional memory implementations [DFGG11,DGK09], as well as relativistic programming [TMW10,HW10] provide promising results on modern hardware.…”