Blelloch, G.E., Chatterjee, S., Hardwick, J.C., Sipelstein, J., Zagha, M. Implementation of a Portable Nested Data-Parallel Language. Proceedings of the fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), San Diego, CA, May 1993.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....associative properties in order to restructure into divideand conquer style parallelism. A traditional approach to this problem is to identify a set of useful program skeletons with program restructuring properties that allow parallelism to be provided. As an example, Blelloch s NESL language [2] supports two important parallel skeletons, namely scan and segmented scan, that together cover a wide range of parallel programs. However, these # School of Computing, National University of Singapore. Email: xun,khoosc,chinwn comp.nus.edu.sg University of Tokyo, PRESTO 21, Japan Science and ....

....type check each of them individually to see if they are well PTyped before we type check the body of f . p #p i e i : # i # 1, n (f x) f x e1 . en ) RS (para) Function pack takes a list of integers and converts them to a single integer. For example, pack [1, 2, 3] 0 will return 123. From [5] we know that function like pack can be parallelized. pack [ c = c pack (a : x ) c = pack x (a 10 c) We can see from pack x (a 10 c) that there is no cyclic dependency between c and a . With the typing rule para, we need to type check parameter c before ....

G.E. Blelloch, S Chatterjee, J.C. Hardwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. In 4th Principles and Practice of Parallel Programming, pages 102--111, San Diego, California (ACM Press), May 1993.

....limitations in the array syntax and the overall generality. For instance, they are all first order and they do not have full type inference. There are some other array extensions of Haskell. Chakravarty et al. 5] have extended Haskell with nested data parallel arrays, much in the style of NESL [1]. Hill [12] extended Haskell with lazy, unbounded data parallel structures that can be seen as a kind of sparse arrays. Both these attempts focus on expressing parallelism and achieving efficiency of implementation, rather than on arrays as a high level concept for tasks like spreadsheet ....

G. E. Blelloch, S. Chatterjee, J. C. Hardwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. J. Parallel Distrib. Comput., 21(1):4--14, Apr. 1994.

....concurrency model all the accesses are local, and some of them may indirectly result in remote reads. Therefore, these can be found at run time without any restriction on how a reference is computed. An important body of work on irregular applications addresses the class of sparse matrix codes [22, 23, 45, 79, 105, 111]. These approaches make specific assumptions about the applications they address. Thus, the discretization schemes do not fit well in this context. Finally, existing work on object models for distributed memory architectures also focuses on the virtual shared memory concept [16,54] While this ....

Guy E. Blelloch, Siddhartha Chatterjee, Jonathan C. Hardwick, Jay Sipelstein, and Marco Zagha. Implementation of a portable nested data-parallel language. Journal of Parallel and Distributed Computing, 21(1):4--14, April 1994.

....algorithms for finding piecewise linear models are implemented as a CAR TOGRAPHER module. The module can use any linear estimation algorithms that are available in CARTOGRAPHER. 4. 8 BENCHCVL s Software Organization Portability of NESL programs is achieved by porting the CVL runtime system [19]. Dataparallel NESL programs are translated by the compiler into a representation called VCODE, for vector code. The VCODE representation is interpreted by a VCODE interpreter. The VCODE interpreter uses the CVL runtime system to perform operations on vectors, the only data structures in NESL. ....

Guy E. Blelloch, Siddhartha Chatterjee,Jonathan C. Hardwick, Jay Sipelstein, and Marco Zagha. Implementation of a portable nested data-parallel language. In Proceedings of the 4th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 102--111, San Diego, CA,May 1993. Also available as Carnegie Mellon University Technical Report CMU-CS-93-112.

....i.e. for nesting of supersteps. Thus, programs can only exploit one dimensional parallelism or must apply a flattening transformation that converts nested parallelism to flat parallelism. However, automatic flattening by the compiler has only been achieved for SIMD parallelism, as e.g. in NESL [2]. Instead, NestStep introduces static and dynamic nesting of supersteps, and thus directly supports nested parallelism. There are good reasons for exploiting nested parallelism: ffl Global barrier synchronization is an inflexible mechanism for structuring parallel programs [11] ffl For very ....

....pre) Parallel quicksort. The following NestStep routine applies a parallel recursive quicksort algorithm to a distributed array a. void qs( sh int[ a ) a cyclically distributed a is a distributed array of n shared integers sh = int l, e, u; sh int pivot; sh float weight[2]; replicated shared array int j; int n = a.length( if (n =THRESHOLD) seq seqsort( a ) return; if (#= 1) seqsort( a ) return; while (true) look for a good pivot element in a[ step l = e = u = 0; forall(j, a, 0, # 1, 1) 1 local iteration pivot = a[j] ....

G. E. Blelloch, J. C. Hardwick, J. Sipelstein, M. Zagha, S. Chatterjee. Implementation of a portable nested data-parallel language. J. of Parallel and Distributed Computing, 21:4--14, 1994.

....of control that creates all of the parallel tasks. None of the applications uses nested task creation to generate a tree like pattern of concurrency. This is an interesting result, especially in light of the fact that other researchers have found it useful to exploit this form of concurrency [Blelloch et al. 1993; Blumofe et al. 1995] On the other hand, four of the six applications use the pipelined form of concurrency discussed in Section 2.6.6. Three of the applications (Water, String and Volume Rendering) use this form of concurrency in a stylized way to perform a parallel reduction of values ....

....sequence of operations on large aggregate data structures such as sets or arrays. The system can execute each aggregate operation in parallel by performing the operation on the individual elements concurrently. The more general data parallel language NESL supports nested data parallel computations [Blelloch et al. 1993]. NESL preserves the basic spirit of the data parallel paradigm (regular operations on large aggregate data structures) while supporting a more general class of computations on aggregate data structures. The data parallel approach preserves the advantages of the sequential programming paradigm ....

Blelloch, G., Chatterjee, S., Hardwick, J., Sipelstein, J., and Zagha, M. 1993. Implementation of a portable nested data-parallel language. In Proceedings of the 4th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM, New York.

....of ffl as small as e GammaT 1 , the space complexity of our algorithm is at most S1 O(PT1 log(PT1 ) with probability at least 1 Gamma e GammaT 1 . These bounds include all time and space costs for both the computation and the scheduler. 1 Introduction Many parallel programming languages [3, 7, 11, 12, 14, 18, 20, 22, 28] support dynamic threads. The multithreaded model of parallel computation is a general approach to model This work was done while the author was affiliated with the MaxPlanck Institute fur Informatik, Saarbrucken, Germany and was partially supported by the IST Programme of the European Union under ....

G. E. Blelloch, S. Chattejee, J. C. Hardwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. Journal of Parallel and Distributed Computing, 21(1):4--14, April 1994.

....Finally, Section 6 brie y reviews related work and concludes. 2 The Architecture Space Previous work addressed the implementation of nested data parallelism on a range of di erent machines, such as vector processors [3, 10] shared memory multiprocessors [9, 10] and distributed memory machines [6, 14]. However, the implementations, while being based on attening, used di erent optimisation techniques and enjoyed various levels of success. We are investigating to which extend we can target the full range of architectures with a uniform compilation system controlled by a set of parameters ....

Guy E. Blelloch, Siddhartha Chatterjee, Jonathan C. Hardwick, Jay Sipelstein, and Marco Zagha. Implementation of a portable nested data-parallel language. In 4th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 1993.

....operations of the source language. Consequently, the library is both high level and complex, but carefully optimized for the targeted parallel hardware; furthermore, it encapsulates most machine dependencies of the implementation. We call this the library approach for Nesl s implementation, see [4]; Fortran 90 95 s collection oriented operations are usually directly realized by a library, and even, FORALL loops can benefit from a collection oriented implementation [8] The library approach, while simplifying the compiler, blocks optimizations by the rigid interface imposed by the ....

....library implementing the parallel aggregate structure. As an example, consider the implementation of Nesl [1] In the case of Nesl, the simplifier implements the flattening transformation [5, 20, 16] which transforms all nested into flat data parallelism. In CMU s implementation of the language [4], the library language is called VCODE [2] it implements a variety of operations on simple and segmented vectors, which are always uniformly distributed over the available processing elements. In fact, CMU s implementation does not generate an executable, but instead emits VCODE and interprets ....

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Guy E. Blelloch, Siddhartha Chatterjee, Jonathan C. Hardwick, Jay Sipelstein, and Marco Zagha. Implementation of a portable nested data-parallel language. In 4th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 1993.

.... are all or in part data parallel: there exist parallel extensions of FORTRAN, like High Performance Fortran [43] and PTRAN [3, 24] parallel extensions of C, like C [69] C [45] of Lisp, like CM List [39] and Paralation Lisp [60, 51] and applicative parallel programming languages, like NESL [8, 10, 11], Sisal [31, 62, 30] Crystal [21] Proteus [54, 34, 59] and Data parallel ML [32, 37, 38] None of these have been concerned with query constructs and their integration in such languages. In this paper we propose a new framework for parallel processing of collections. We define a highlevel ....

Guy E. Blelloch and Siddhartha Chatterjee. Implementation of a portable nested data-parallel language. In Proceedings of the 4th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 102--112, San Diego, May 1993.

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Guy E. Blelloch, Siddhartha Chatterjee, Jonathan C. Hardwick, Jay Sipelstein, and Marco Zagha. Implementation of a portable nested data-parallel language. Technical Report CMUCS -93-112, School of Computer Science, Carnegie Mellon University,February 1993.

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Guy E. Blelloch, Siddhartha Chatterjee, Jonathan C. Hardwick, Jay Sipelstein, and Marco Zagha. Implementation of a portable nested data-parallel language. In Proceedings 4th ACM SIGPLAN Symposium on Principles and PracticeofParallel Programming, pages 102--111, San Diego, May 1993.

No context found.

Guy E. Blelloch, Siddhartha Chatterjee, Jonathan C. Hardwick, Jay Sipelstein, and Marco Zagha. Implementation of a portable nested data-parallel language. Journal of Parallel and Distributed Computing, 21(1):4--14, April 1994.

No context found.

Guy E. Blelloch, Jonathan C. Hardwick, Jay Sipelstein, Marco Zagha, and Siddhartha Chatterjee. Implementation of a portable nested data-parallel language. Journal of Parallel and Distributed Computing, 21(1):4--14, April 1994.

No context found.

Blelloch, G. E., Chatterjee, S., Hardwick, J. C., Sipelstein, J., and Zagha, M. implementation of a portable nested data-parallel language. Tech. Rep. CMU-CS-93-112, School of Computer Science, Carnegie Mellon University, Feb. 1993.

....splits up the points evenly into regions until there are as many region as processors, at which point a sequential algorithm can be used. Our final implementation is based on this idea. Our experiments were divided into two parts. A prototyping phase used the parallel programming language NESL [13] to experiment with algorithm variants, and to measure their work efficiency. An optimized coarse grained implementation of the final algorithm was then written in C and a toolkit based on MPI [14] and was compared with the best existing sequential implementation. For our measurements in both ....

....points are duplicated only when a Delaunay edge is found, and it does not require complicated subroutines, especially if one is willing to compromise by using components that are not theoretically optimal, as discussed below. We initially prototyped the algorithm in the programming language NESL [13], a high level parallel programming language designed for algorithm specification and teaching. NESL allowed us to develop the code quickly, try several variants of the algorithm, and run many experiments to analyze the characteristics. For such prototyping NESL has the important properties that ....

[Article contains additional citation context not shown here]

G. E. Blelloch, S. Chatterjee, J. C. Hardwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. Journal of Parallel and Distributed Computing, 21(1):4--14, April 1994.

No context found.

Blelloch, G.E., Chatterjee, S., Hardwick, J.C., Sipelstein, J., Zagha, M. Implementation of a Portable Nested Data-Parallel Language. Proceedings of the fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), San Diego, CA, May 1993.

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Blelloch, G.E., Chatterjee, S., Hardwick, J.C., Sipelstein, J., Zagha, M. Implementation of a Portable Nested Data-Parallel Language. Proceedings of the fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), San Diego, CA, May 1993.

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Blelloch, G.E., Chatterjee, S., Hardwick, J.C., Sipelstein, J., Zagha, M. Implementation of a Portable Nested Data-Parallel Language. Proceedings of the fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), San Diego, CA, May 1993.

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Blelloch, G.E., Chatterjee, S., Hardwick, J.C., Sipelstein, J., Zagha, M. Implementation of a Portable Nested Data-Parallel Language. Proceedings of the fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), San Diego, CA, May 1993.

No context found.

G. E. Blelloch, S. Chatterjee, J. C. Hardwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. In Fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming PPoPP, San Diego, CA, pages 102--112. ACM Press, New York, NY, 1993.

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G. E. Blelloch, S. Chatterjee, J. C. Hardwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. In Fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming PPoPP, San Diego, CA, pages 102--112. ACM Press, New York, NY, 1993.

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G.E. Blelloch, S. Chatterjee, J.C. Harwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. In Proc. of the 4th ACM PPOPP, pages 102--111, 1993. 72] M. Blum, R.W. Floyd, V.R. Pratt, R.L. Rivest, and R.E. Tarjan. Time bounds for selection. J. Comp. Sys. Sci., 7(4):448--461, 1972.

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G. E. Blelloch, J. C. Hardwick, J. Sipelstein, M. Zagha, and S. Chatterjee. Implementation of a portable nested data-parallel language. Journal of Parallel and Distributed Computing, 21(1):414, April 1994.

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G. E. Blelloch, S. Chatterjee, J. C. Hardwick, J. Sipelstein, and M. Zagha. Implementation of a portable nested data-parallel language. In 4th ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming, 1993.

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