PPT-Concurrent Hashing and Natural Parallelism

Presented by Orit Mussel December 2016 Art of Multiprocessor Programming by Herlihy amp Shavit 1 Advanced Topics in Concurrent Programming Instructor Erez Petrank Outline. 1 Overview Hashing is a great practical tool with an interesting and su btle theory too In addition to its use as a dictionary data structure hashing also comes up in many di 64256erent areas including cryptography and complexity theory In this lectu 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 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 COL 106. Shweta Agrawal, . Amit. Kumar. Slide Courtesy : Linda Shapiro, . Uwash. Douglas W. Harder, . UWaterloo. 12/26/03. Hashing - Lecture 10. 2. The Need for Speed. Data structures we have looked at so far. Approximate Near Neighbors. Ilya Razenshteyn (CSAIL MIT). Alexandr. . Andoni. (Simons Institute). Approximate Near Neighbors (ANN). Dataset:. . n. points in . d. dimensions. Query:. a point within . Koen Lindström Claessen. Chalmers University. Gothenburg, Sweden. Ulf Norell. Expressing. Parallelism. In a pure, lazy language. Evaluation is done when needed. Evaluation order does not affect meaning of program. Presented . by A. Craik . (. 5. -Jan-12). Research supported by funding . from . Microsoft Research and the Queensland State Government. 1. Introduction. 2. Procedural Algorithm. Sequential Implementation w/ Injected Parallelism. 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. Consider a set of data with N data items stored in some data structure. We must be able to insert, delete & search for items. What are possible ways to do this? What is the complexity of each structure & method ?. 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.. What is a hashing function?. Fingerprint for a given piece of data. Typically generated by a mathematical algorithm. Produces a fixed length string as its . output. Hashes are sometimes . called a . checksum or message digests. 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. Amjad. . Daoud. , Ph.D.. http://iswsa.acm.org/mphf. Practical Perfect Hashing for very large Key-Value Databases . Abstract. This presentation describes a practical algorithm for perfect hashing that is suitable for very large KV (key, value)...