ADT
        aaron-thesis
        arm-eh
        ast-experimental
        cleanup-dtors
        deferred_resn
        demangler
        enum
        forall-pointer-decay
        jacob/cs343-translation
        jenkins-sandbox
        new-ast
        new-ast-unique-expr
        new-env
        no_list
        persistent-indexer
        pthread-emulation
        qualifiedEnum
        resolv-new
        with_gc
      
      
        
          | Last change
 on this file since 8a62d04 was             a2ea829, checked in by Thierry Delisle <tdelisle@…>, 8 years ago | 
        
          | 
updated first 3 chapters
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| 1 | % ====================================================================== | 
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| 2 | \chapter{Introduction} | 
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| 3 | % ====================================================================== | 
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| 4 |  | 
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| 5 | This thesis provides a minimal concurrency \acrshort{api} that is simple, efficient and can be reused to build higher-level features. The simplest possible concurrency system is a thread and a lock but this low-level approach is hard to master. An easier approach for users is to support higher-level constructs as the basis of concurrency. Indeed, for highly productive concurrent programming, high-level approaches are much more popular~\cite{HPP:Study}. Examples are task based, message passing and implicit threading. The high-level approach and its minimal \acrshort{api} are tested in a dialect of C, call \CFA. Furthermore, the proposed \acrshort{api} doubles as an early definition of the \CFA language and library. This thesis also comes with an implementation of the concurrency library for \CFA as well as all the required language features added to the source-to-source translator. | 
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| 7 | There are actually two problems that need to be solved in the design of concurrency for a programming language: which concurrency and which parallelism tools are available to the programmer. While these two concepts are often combined, they are in fact distinct, requiring different tools~\cite{Buhr05a}. Concurrency tools need to handle mutual exclusion and synchronization, while parallelism tools are about performance, cost and resource utilization. | 
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