PPT-Algorithmic Improvements for Fast Concurrent Cuckoo Hashing

Xiaozhou Li Princeton David G Andersen CMU Michael Kaminsky Intel Labs Michael J Freedman Princeton How to build a fast concurrent hash table a lgorithm and data structure engineering. In building concurrent FIFO queues this reasoning has led re searchers to propose combiningbased concurrent queues This paper takes a different approach showing how to rely on fetchandadd FA a less powerful primitive that is available on x86 process harvardedu Abstract The purpose of this brief note is to describe recent work in the area of cuckoo hashing including a clear description of several open problems with the hope of spurring further research 1 Introduction Hashbased data structures and The analysis uses only very basic and intuitively understandable concepts of probability theory and is meant to be accessible even for undergraduates taking their 64257rst algorithms course 1 Introduction dictionary is a data structure for storing a Up to this point the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically signicant probability that a failure occurs during the insertion of an item requiring an expensive rehashing of all items in th Constant Worst-Case Operations with a Succinct Representation. Yuriy. . Arbitman. . Moni. Naor Gil . Segev. Dynamic Dictionary. Data structure representing a set of words . S. From a Universe . Practically Better Than Bloom. Bin Fan . (CMU/Google). David Andersen (CMU). Michael . Kaminsky. (Intel Labs). Michael . Mitzenmacher. (Harvard). 1. What is Bloom Filter? A Compact Data Structure . Guest lecture: Cuckoo Hashing. Shannon Larson. March 11, 2011. Learning Goals. Describe the cuckoo hashing principle. Analyze the space and time complexity of cuckoo hashing. Apply the insert and lookup algorithms in a cuckoo hash table. Uri . Zwick. January 2014. Hashing. 2. Dictionaries. D .  . Dictionary() . – Create an empty dictionary. Insert(. D. ,. x. ) . – Insert item . x. into . D. Find(. D. ,. k. ) . – Find an item with key . H. ashing: . Scalable and Flexible Hashing on GPUs. Farzad Khorasani, Mehmet E. . Belviranli. , Rajiv Gupta, . Laxmi. N. . Bhuyan. University of California Riverside. What Stadium hashing addresses. Haim Kaplan . and. Uri . Zwick. January 2013. Hashing. 2. Dictionaries. D .  . Dictionary() . – Create an empty dictionary. Insert(. D. ,. x. ) . – Insert item . x. into . D. Find(. D. ,. k. H. ashing: . Scalable and Flexible Hashing on GPUs. Farzad Khorasani, Mehmet E. . Belviranli. , Rajiv Gupta, . Laxmi. N. . Bhuyan. University of California Riverside. What Stadium hashing addresses. Plan. I spent the last decade advising on numerous cases where hash tables/functions were used. A few observations on . What data structures I’ve seen implemented and where. What do developers think, were they need help. In static hashing, function . h. maps search-key values to a fixed set of . B. . buckets, that contain a number of (K,V) entries.. . . Problem: d. atabases . grow . (or shrink) . with time. . If initial number of buckets is too small, and file grows, performance will degrade due to too much overflows.. Nhan Nguyen. & . Philippas. . Tsigas. ICDCS 2014. Distributed Computing and Systems. Chalmers University of Technology. Gothenburg, Sweden. Our contributions: a concurrent hash table. Nhan D. Nguyen.