A VLSI Architecture for Concurrent Data Structures by William J. Dally (auth.)

By William J. Dally (auth.)

Concurrent information constructions simplify the advance of concurrent courses via encapsulating popular mechanisms for synchronization and commu­ nication into information constructions. This thesis develops a notation for describing concurrent facts buildings, provides examples of concurrent info constructions, and describes an structure to help concurrent facts constructions. Concurrent Smalltalk (CST), a by-product of Smalltalk-80 with extensions for concurrency, is constructed to explain concurrent facts constructions. CST permits the programmer to specify gadgets which are allotted over the nodes of a concurrent computing device. those disbursed items have many constituent items and therefore can procedure many messages concurrently. they're the root upon which concurrent info constructions are outfitted. The balanced dice is a concurrent facts constitution for ordered units. The set is sent by way of a balanced recursive partition that maps to the subcubes of a binary 7lrcube utilizing a grey code. A seek set of rules, VW seek, in accordance with the gap homes of the grey code, searches a balanced dice in O(log N) time. since it doesn't have the basis bottleneck that limits all tree-based information buildings to 0(1) concurrency, the balanced dice achieves 0C.:N) con­ forex. contemplating graphs as concurrent facts constructions, graph algorithms are pre­ sented for the shortest direction challenge, the max-flow challenge, and graph parti­ tioning. those algorithms introduce new synchronization ideas to accomplish higher functionality than present algorithms.

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Extra resources for A VLSI Architecture for Concurrent Data Structures

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Message-passing concurrent computers are communication limited. The time required to pass messages dominates the processing time, which we will ignore. 24 A VLSI Architecture for Concurrent Data Structures In sharp contrast, most existing concurrent algorithms have been developed assuming an ideal shared-memory multiprocessor. In the shared-memory model, communication cost is ignored. Processes can access any memory location with unit cost, and an unlimited number of processes can access a single memory location simultaneously.

In many applications these objects are records and the linear order is defined by the value of a key field in each record. In this context the ordered set is used to store a database of relations associating the key field with the other fields of the record. The order relation defined on the keys of the records is implicit in the structure. A data structure implementing the ordered set must efficiently support the following operations. at: key return the object associated with a key. at: key put: object delete: key add an object to the set remove the object associated with key from the set.

Myld = anld) ifTrue: [requester reply: newSum] if False: [(self upperNeighbor) localTally: aKey sum: new$um return From: anld]. other instance methods ... 1: Distributed Object Class Tally Collection can receive messages at the same time, the distributed object can process many messages simultaneously. 1 shows an example CST class definition. The definition begins with a header that identifies the name of the class, Tally Collection. the superclass from which Tally Collection inherits behavior, Distributed Collection, and the instance variables and locks that make up the state of each instance of the class.

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