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Parallel symbolic computing in Cid

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Parallel Symbolic Languages and Systems (PSLS 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1068))

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Abstract

We have designed and implemented a language called Cid for parallel applications with recursive linked data structures (e.g., lists, trees, graphs) and complex control structures (data dependent, recursion). Cid is unique in that, while targeting distributed memory machines, it attempts to preserve the traditional “MIMD threads plus lock-protected shared data” programming model that is standard on shared memory machines.

Cid is a small extension to C because although Lisp, functional and logic programming languages are attractive for symbolic computing, it appears nearly impossible to sway the momentum of C. Cid uses only a simple, platform-independent preprocessor for localized expansion of its few extensions; the preprocessed code is compiled using a standard C compiler, and linked with the Cid runtime system, which is written in standard C. By relying on standard C compilers, Cid exploits the continuing advances in optimizing C compilers and programming tools, and remains completely compatible with existing, even pre-compiled, C code.

Cid is designed for distributed address spaces because most truly scalable machines are likely to be multicomputers (even if individual nodes are shared-memory multiprocessors, or SMPs). However, Cid is not a message-passing language; it adheres to the traditional MIMD threads model with a shared, global, synchronized heap of objects. Cid has numerous design features to accomodate the relatively high communication costs of multicomputers, such as latency-tolerant multithreading, automatic load balancing and granularity control, automatic coherent object-caching, combined synchronization with object access, and asynchronous object pre-fetching, all of which we believe are necessary in any symbolic language implementation.

In this paper, we present an overview of Cid with examples using linked recursive data structures, explaining some of our design choices and aspects of its implementation. We present measurements of the cost of various Cid operations, and some preliminary observations about the effectiveness of Cid's automatic load balancing mechanisms. We also compare Cid with other approaches to developing parallel versions of C and C++ for distributed memory machines.

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Authors and Affiliations

  1. Cambridge Research Laboratory, Digital Equipment Corporation, One Kendall Square, Bldg. 700, 02139, Cambridge, MA, USA

    Rishiyur S. Nikhil

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  1. Rishiyur S. Nikhil
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Takayasu Ito Robert H. Halstead Jr. Christian Queinnec

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© 1996 Springer-Verlag Berlin Heidelberg

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Nikhil, R.S. (1996). Parallel symbolic computing in Cid. In: Ito, T., Halstead, R.H., Queinnec, C. (eds) Parallel Symbolic Languages and Systems. PSLS 1995. Lecture Notes in Computer Science, vol 1068. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0023064

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  • DOI: https://doi.org/10.1007/BFb0023064

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  • Print ISBN: 978-3-540-61143-1

  • Online ISBN: 978-3-540-68332-2

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