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A provable time and space efficient implementation of nesl
 In International Conference on Functional Programming
, 1996
"... In this paper we prove time and space bounds for the implementation of the programming language NESL on various parallel machine models. NESL is a sugared typed Jcalculus with a set of array primitives and an explicit parallel map over arrays. Our results extend previous work on provable implementa ..."
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Cited by 71 (7 self)
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In this paper we prove time and space bounds for the implementation of the programming language NESL on various parallel machine models. NESL is a sugared typed Jcalculus with a set of array primitives and an explicit parallel map over arrays. Our results extend previous work on provable implementation bounds for functional languages by considering space and by including arrays. For modeling the cost of NESL we augment a standard callbyvalue operational semantics to return two cost measures: a DAG representing the sequential dependence in the computation, and a measure of the space taken by a sequential implementation. We show that a NESL program with w work (nodes in the DAG), d depth (levels in the DAG), and s sequential space can be implemented on a p processor butterfly network, hypercube, or CRCW PRAM usin O(w/p + d log p) time and 0(s + dp logp) reachable space. For programs with sufficient parallelism these bounds are optimal in that they give linew speedup and use space within a constant factor of the sequential space. 1
A Provably TimeEfficient Parallel Implementation of Full Speculation
 In Proceedings of the 23rd ACM Symposium on Principles of Programming Languages
, 1996
"... Speculative evaluation, including leniency and futures, is often used to produce high degrees of parallelism. Existing speculative implementations, however, may serialize computation because of their implementation of queues of suspended threads. We give a provably efficient parallel implementation ..."
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Cited by 17 (5 self)
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Speculative evaluation, including leniency and futures, is often used to produce high degrees of parallelism. Existing speculative implementations, however, may serialize computation because of their implementation of queues of suspended threads. We give a provably efficient parallel implementation of a speculative functional language on various machine models. The implementation includes proper parallelization of the necessary queuing operations on suspended threads. Our target machine models are a butterfly network, hypercube, and PRAM. To prove the efficiency of our implementation, we provide a cost model using a profiling semantics and relate the cost model to implementations on the parallel machine models. 1 Introduction Futures, lenient languages, and several implementations of graph reduction for lazy languages all use speculative evaluation (callbyspeculation [15]) to expose parallelism. The basic idea of speculative evaluation, in this context, is that the evaluation of a...
A Parallel Complexity Model for Functional Languages
 IN: PROC. ACM CONF. ON FUNCTIONAL PROGRAMMING LANGUAGES AND COMPUTER ARCHITECTURE
, 1994
"... A complexity model based on the calculus with an appropriate operational semantics in presented and related to various parallel machine models, including the PRAM and hypercube models. The model is used to study parallel algorithms in the context of "sequential" functional languages, and to relate ..."
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Cited by 5 (2 self)
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A complexity model based on the calculus with an appropriate operational semantics in presented and related to various parallel machine models, including the PRAM and hypercube models. The model is used to study parallel algorithms in the context of "sequential" functional languages, and to relate these results to algorithms designed directly for parallel machine models. For example, the paper shows that equally good upper bounds can be achieved for merging two sorted sequences in the pure calculus with some arithmetic constants as in the EREW PRAM, when they are both mapped onto a more realistic machine such as a hypercube or butterfly network. In particular for n keys and p processors, they both result in an O(n=p + log 2 p) time algorithm. These results argue that it is possible to get good parallelism in functional languages without adding explicitly parallel constructs. In fact, the lack of random access seems to be a bigger problem than the lack of parallelism. This research...