Approximate counting: a detailed analysis
BIT - Ellis Horwood series in artificial intelligence
Adaptive backoff synchronization techniques
ISCA '89 Proceedings of the 16th annual international symposium on Computer architecture
Counting large numbers of events in small registers
Communications of the ACM
Proceedings of the 3rd international symposium on Memory management
Hierarchical Backoff Locks for Nonuniform Communication Architectures
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
Approximate counting with a floating-point counter
COCOON'10 Proceedings of the 16th annual international conference on Computing and combinatorics
Lightweight parallel accumulators using C++ templates
Proceedings of the 4th International Workshop on Multicore Software Engineering
Lock cohorting: a general technique for designing NUMA locks
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
CBTree: a practical concurrent self-adjusting search tree
DISC'12 Proceedings of the 26th international conference on Distributed Computing
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Statistics counters are important for purposes such as detecting excessively high rates of various system events, or for mechanisms that adapt based on event frequency. As systems grow and become increasingly NUMA, commonly used naive counters impose scalability bottlenecks and/or such inaccuracy that they are not useful. We present both precise and statistical (probabilistic) counters that are nonblocking and provide dramatically better scalability and accuracy properties. Crucially, these counters are competitive with the naive ones even when contention is low.