Concurrent programming in ERLANG (2nd ed.)
Concurrent programming in ERLANG (2nd ed.)
AXD 301: a new generation ATM switching system
Computer Networks: The International Journal of Computer and Telecommunications Networking
A high performance Erlang system
Proceedings of the 2nd ACM SIGPLAN international conference on Principles and practice of declarative programming
Heap architectures for concurrent languages using message passing
Proceedings of the 3rd international symposium on Memory management
Open Sources: Voices from the Open Source Revolution
Open Sources: Voices from the Open Source Revolution
Gprof: A call graph execution profiler
SIGPLAN '82 Proceedings of the 1982 SIGPLAN symposium on Compiler construction
Optimising TCP/IP connectivity
ERLANG '07 Proceedings of the 2007 SIGPLAN workshop on ERLANG Workshop
Engineering scalable, cache and space efficient tries for strings
The VLDB Journal — The International Journal on Very Large Data Bases
Towards an efficient verification approach on network configuration
Proceedings of the 8th International Conference on Network and Service Management
On the scalability of the Erlang term storage
Proceedings of the twelfth ACM SIGPLAN workshop on Erlang
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The viability of implementing an in-memory database, Erlang ETS, using a relatively-new data structure, called a Judy array, was studied by comparing the performance of ETS tables based on four data structures: AVL balanced binary trees, B-trees, resizable linear hash tables, and Judy arrays. The benchmarks used workloads of sequentially- and randomly-ordered keys at table populations from 700 keys to 54 million keys.Benchmark results show that ETS table insertion, lookup, and update operations on Judy-based tables are significantly faster than all other table types for tables that exceed CPU data cache size (70,000 keys or more). The relative speed of Judy-based tables improves as table populations grow to 54 million keys and memory usage approaches 3GB. Term deletion and table traversal operations by Judy-based tables are slower than the linear hash table-based type, but the additional cost of the deletion operation is smaller than the combined savings of the other operations.Resizing a hash table to 232 buckets, managed by a Judy array, creates the most consistent performance improvements and uses only about 6% more memory than a regular hash table. Other applications could benefit substantially by this application of Judy arrays.