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Paradigm | multi-paradigm: object-oriented, imperative, parallel |
---|---|
Designed by | Thinking Machines |
Developer | Thinking Machines |
First appeared | 1987 |
Stable release | 6.x (August 27, 1993 ) / August 27, 1993 |
Typing discipline | static, weak, manifest |
OS | Connection Machine |
Filename extensions | .cs |
Influenced by | |
ANSI C, *Lisp | |
Influenced | |
Dataparallel-C |
C* (or C-star) is an object-oriented, data-parallel superset of ANSI C with synchronous semantics.
It was developed in 1987 as an alternative language to *Lisp and CM-Fortran for the Connection Machine CM-2 and above. The language C* adds to C a "domain" data type and a selection statement for parallel execution in domains.
For the CM-2 models the C* compiler translated the code into serial C, calling PARIS (Parallel Instruction Set) functions, and passed the resulting code to the front end computer's native compiler. The resulting executables were executed on the front end computer with PARIS calls being executed on the Connection Machine.
On the CM-5 and CM-5E parallel C* Code was executed in a SIMD style fashion on processing elements, whereas serial code was executed on the PM (Partition Manager) Node, with the PM acting as a "front end" if directly compared to a CM-2. The latest version of C* as of 27 August 1993 is 6.x. An unimplemented language dubbed "Parallel C" (not to be confused with Unified Parallel C) influenced the design of C*. Dataparallel-C was based on C*.
Computer programming is the process of performing a particular computation, usually by designing and building an executable computer program. Programming involves tasks such as analysis, generating algorithms, profiling algorithms' accuracy and resource consumption, and the implementation of algorithms. The source code of a program is written in one or more languages that are intelligible to programmers, rather than machine code, which is directly executed by the central processing unit. The purpose of programming is to find a sequence of instructions that will automate the performance of a task on a computer, often for solving a given problem. Proficient programming thus usually requires expertise in several different subjects, including knowledge of the application domain, specialized algorithms, and formal logic.
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For several years parallel hardware was only available for distributed computing but recently it is becoming available for the low end computers as well. Hence it has become inevitable for software programmers to start writing parallel applications. It is quite natural for programmers to think sequentially and hence they are less acquainted with writing multi-threaded or parallel processing applications. Parallel programming requires handling various issues such as synchronization and deadlock avoidance. Programmers require added expertise for writing such applications apart from their expertise in the application domain. Hence programmers prefer to write sequential code and most of the popular programming languages support it. This allows them to concentrate more on the application. Therefore, there is a need to convert such sequential applications to parallel applications with the help of automated tools. The need is also non-trivial because large amount of legacy code written over the past few decades needs to be reused and parallelized.
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