The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
High performance dynamic lock-free hash tables and list-based sets
Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
Introduction to Algorithms
Linux Journal
Hazard Pointers: Safe Memory Reclamation for Lock-Free Objects
IEEE Transactions on Parallel and Distributed Systems
Exploiting deferred destruction: an analysis of read-copy-update techniques in operating system kernels
Lock-free dynamic hash tables with open addressing
Distributed Computing - Special issue: PODC 02
Split-ordered lists: Lock-free extensible hash tables
Journal of the ACM (JACM)
Performance of memory reclamation for lockless synchronization
Journal of Parallel and Distributed Computing
Scalable concurrent hash tables via relativistic programming
ACM SIGOPS Operating Systems Review
Laws of order: expensive synchronization in concurrent algorithms cannot be eliminated
Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
A relativistic enhancement to software transactional memory
HotPar'11 Proceedings of the 3rd USENIX conference on Hot topic in parallelism
Chronos: predictable low latency for data center applications
Proceedings of the Third ACM Symposium on Cloud Computing
A case for relativistic programming
Proceedings of the 2012 ACM workshop on Relaxing synchronization for multicore and manycore scalability
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We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow Read-Copy Update (RCU) hash tables to maintain constant-time 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.