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I propose a new concurrent skip list algorithm distinguished by a combination of simplicity and calability. Superreview requested: mail installer version text needs to be bumped from 1.0 to 1.0.1 : Bump Tb Windows installer to version 1.0.1Ĭhase Phillips has asked Scott MacGregor Additional Comments from Scott MacGregor Scott MacGregor has granted Chase Phillipsīug 283224: mail installer version text needs to be bumped from 1.0 to 1.0.1Īttachment 175211: Bump Tb Windows installer to version 1.0.1 Superreview granted: mail installer version text needs to be bumped from 1.0 to 1.0.1 : Bump Tb Windows installer to version 1.0.1 Superreview requested: installer version text needs to be upgraded from 1.0 to 1.0.1 : Bump version in installers to 1.0.1/1.7.6 Superreview granted: installer version text needs to be upgraded from 1.0 to 1.0.1 : Bump version in installers to 1.0.1/1.7.6īug 283181: installer version text needs to be upgraded from 1.0 to 1.0.1Īttachment 175188: Bump version in installers to 1.0.1/1.7.6
CHM READER SEAMONKEY PATCH
This is the 1.8.0 branch patch for bumping SeaMonkey version to 1.0.7 Additional Comments from Robert Kaiser Superreview requested: bump SeaMonkey versions tođ.0.7/1.1 on 1.8.0/1.8 branches, localeVersion to 1.8.1 on 1.8 branch : 1.8.0 patch: SeaMonkey version -> 1.0.7 Superreview granted: bump SeaMonkey versions tođ.0.7/1.1 on 1.8.0/1.8 branches, localeVersion to 1.8.1 on 1.8 branch : 1.8.0 patch: SeaMonkey version -> has granted Robert Kaiserīug 362139: bump SeaMonkey versions to 1.0.7/1.1 on 1.8.0/1.8 branches,Īttachment 246921: 1.8.0 patch: SeaMonkey version -> 1.0.7 Precision Helper is designed for advanced developers and a.
CHM READER SEAMONKEY PDF
Precision Helper works natively with the Microsoft HTML Help projects format (HHP)Īnd allows to publish resulting help to the CHM, WebHelp, PDF It focuses on the organization of existing html files, xml files, scripts, imagesĪnd other resources so that the help author has the best overview of his project. Precision Helper is a tool for creating and managing help projects. We would like to announce the first release of "Precision Helper", This parallel algorithm employs one distributed reader-writer mutex that makes the search() method scales to 250X on NUMA architecture and on multicores, unlike some other concurrent skip list algorithms, this algorithm.
This parallel algorithm makes the search() method scalable and it makes the insert() method of a decent throughput. I propose a new concurrent skip list algorithm distinguished by a combination of simplicity and scalability. Of my concurrent SkipList so that you can learn more from it.Įdited by: Amine Moulay Ramdane on 1:17 PMĪNN: A concurrent SkipList version 1.0
CHM READER SEAMONKEY HOW TO
Please look at the test_insert.pas and test_search.pas examples inside the zip file, you have to know how to use the search() an next() methods, with the search() method you will receive a scalable distributed reader-writer mutex into a variable and you will receive an id into a variable, and of course you will receive the pointer to your returned object, but when you receive your object that you want to search for, you have to work with object if you want to work with it and after that you have to releĪse the distributted reader-writer mutex by calling RUnlock(id), the id is the id that will be returned by the search method, why you have to do that ? because the object returned can be an object's pointer that can be released concurrently and this can cause an exception and can cause problems, this is why you have to work first with the object data and after than release the scalable distributed reader-writer mutex, you have to do the same for the next() method, anyway, i have given you the source code I want to speak more about my concurrent SkipList: The defines options inside defines1.inc are: Required FPC switches: -O3 -Sd -dFPC -dFreePascal
CHM READER SEAMONKEY MAC
Operating Systems: Windows, Mac OSX, Linux. Language: FPC Pascal v2.2.0+ / Delphi 7+:
CHM READER SEAMONKEY DOWNLOAD
You can download concurrent SkipList from: Experimental evidence shows that this parallel algorithm performs well. This parallel algorithm employs one distributed reader-writer mutex that makes the search() method scales to 250X on NUMA architecture and on multicores, unlike some other concurrent skip list algorithms, this algorithm preserves the skiplist properties at all times, which facilitate This parallel algorithm makes the search() method scalable and it makes the insert() method of a descent throughput.
Authors: Amine Moulay Ramdane (Based on Duncan Murdoch's sequential Skiplist)
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