Many applications in a variety of domains including digital signal processing, image processing, and computer vision are composed of a sequence of tasks that act on a stream of input data sets in a pipelined manner. R...
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ISBN:
(纸本)9780897917001
Many applications in a variety of domains including digital signal processing, image processing, and computer vision are composed of a sequence of tasks that act on a stream of input data sets in a pipelined manner. Recent research has established that these applications are best mapped to a massively parallel machine by dividing the tasks into modules and assigning a subset of the available processors to each module. this paper addresses the problem of optimally mapping such applications onto a massively parallel machine. We formulate the problem of optimizing throughput in task pipelines and present two new solution algorithms. the formulation uses a general and realistic model for inter-task communication, takes memory constraints into account, and addresses the entire problem of mapping which includes clustering tasks into modules, assignment of processors to modules, and possible replication of modules. the first algorithm is based on dynamic programming and finds the optimal mapping of k tasks onto P processors in O(P(4)k(2)) time. We also present a heuristic algorithm that is linear in the number of processors and establish withtheoretical and practical results that the solutions obtained are optimal in practical situations. the entire framework is implemented as an automatic mapping tool for the Fx parallelizing compiler for High Performance Fortran. We present experimental results that demonstrate the importance of choosing a good mapping and show that the methods presented yield efficient mappings and predict optimal performance accurately.
the goal of the Olden project is to build a system that provides parallelism for general purpose C programs with minimal programmer annotations. We focus on programs using dynamic structures such as trees, lists, and ...
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ISBN:
(纸本)9780897917001
the goal of the Olden project is to build a system that provides parallelism for general purpose C programs with minimal programmer annotations. We focus on programs using dynamic structures such as trees, lists, and DAGs. We demonstrate that providing both software caching and computation migration can improve the performance of these programs, and provide a compile-time heuristic that selects between them for each pointer dereference. We have implemented a prototype system on the thinking Machines CM-5. We describe our implementation and report on experiments with ten benchmarks.
We have developed compiler algorithms that analyze explicitly parallel programs and restructure their shared data to reduce the number of false sharing misses. the algorithms analyze per-process shared data accesses, ...
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ISBN:
(纸本)9780897917001
We have developed compiler algorithms that analyze explicitly parallel programs and restructure their shared data to reduce the number of false sharing misses. the algorithms analyze per-process shared data accesses, pinpoint the data structures that are susceptible to false sharing and choose an appropriate transformation to reduce it. the transformations either group data that is accessed by the same processor or separate individual data items that are shared. this paper evaluates that technique. We show through simulation that our analysis successfully identifies the data structures that are responsible for most false sharing misses, and then transforms them without unduly decreasing spatial locality. the reduction in false sharing positively impacts both execution time and program scalability when executed on a KSR2. Both factors combine to increase the maximum achievable speedup for all programs, more than doubling it for several. Despite being able to only approximate actual inter-processor memory accesses, the compiler-directed transformations always outperform programmer efforts to eliminate false sharing.
this paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and connected components. Our general framework is a single-address space, distributed ...
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this paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and connected components. Our general framework is a single-address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. Our connected components algorithm uses a novel approach for parallel merging which performs drastically limited updating during iterative steps, and concludes with a total consistency update at the final step. the algorithms have been coded in Split-C and run on a variety of platforms. Our experimental results are consistent withthe theoretical analysis and provide the best known execution times for these two primitives, even when compared with machine-specific implementations. More efficient implementations of Split-C will likely result in even faster execution times.
the symposium materials contain 26 papers covering the spectrum from models of parallel computing to implementation techniques, and from compilation algorithms to application development tools and case studies, thus s...
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ISBN:
(纸本)0897915895
the symposium materials contain 26 papers covering the spectrum from models of parallel computing to implementation techniques, and from compilation algorithms to application development tools and case studies, thus satisfying the goal of broadly covering the active areas of parallelprogramming research.
the proceedings contain 26 papers. the topics discussed include: LogP: towards a realistic model of parallel computation;exploiting task and data parallelism on a multicomputer;ActorSpace: an open distributed programm...
ISBN:
(纸本)0897915895
the proceedings contain 26 papers. the topics discussed include: LogP: towards a realistic model of parallel computation;exploiting task and data parallelism on a multicomputer;ActorSpace: an open distributed programming paradigm;experiences using the ParaScope editor: an interactive parallelprogramming tool;perturbation analysis of high level instrumentation for SPMD programs;integrating message-passing and shared-memory: early experience;using scheduler information to achieve optimal barrier synchronization performance;and a concurrent copying garbage collector for languages that distinguish (im)mutable data.
We present a solution to the reaching definitions problem for programs with explicit lexically specified parallel constructs, such as cobegin/coend or parallel-sections, both with and without explicit synchronization ...
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ISBN:
(纸本)9780897915892
We present a solution to the reaching definitions problem for programs with explicit lexically specified parallel constructs, such as cobegin/coend or parallel-sections, both with and without explicit synchronization operations, such as Post, Wait or Advance. the reaching definitions information for sequential programs is used to solve many standard optimization problems. In parallel programs, this information can also be used to explicitly direct communication and data ownership. Although work has been done on analyzing parallel programs to detect data races, little work has been done on optimizing such programs. We show how the memory consistency model specified by an explicitly parallelprogramming language can influence the complexity of the reaching definitions problem. By selecting the ''weakest'' memory consistency semantics, we can efficiently solve the reaching definitions problem for correct programs.
Efficient parallel execution of a high-level data-parallel language based on nested sequences, higher order functions and generalized iterators can be realized in the vector model using a suitable representation of ne...
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Data-parallelism provides a clean conceptual framework for parallelprogramming. We are developing two programming languages: a high level equational language, called EL*, and a low-level implementation language. Both...
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ISBN:
(纸本)9780897915892
Data-parallelism provides a clean conceptual framework for parallelprogramming. We are developing two programming languages: a high level equational language, called EL*, and a low-level implementation language. Both languages exploit data-parallelism instead of control-parallelism. EL* is a declarative data-parallel language. EL* programs are high-level equational specifications that use extensive pattern-matching and recursion. the language's syntax and semantics are intended to be clear and simple. Recursive forms are restricted to enable translation to efficient data-parallel operations. EL* programs are compiled into FP*, a variant of Backus's FP, where parallel operations are more explicit and low-level. the target language has a rich set of functions for performing communication, and computation. It also has a powerful set of combining forms that generate large highly-parallel functions from smaller program units. Prototype compilers have been implemented for both languages, and they demonstrate good performance. Several linear algebra and non-numeric problems have been programmed with relative ease using EL*. We are currently developing compilation techniques for a wider range of scientific problems that have more complex parallel solutions, and are continuing to expand the language's scope.
作者：
Siegl, Kurt
Johannes Kepler University LinzA-4040 Austria
||MAPLE|| (speak: parallel Maple) is a portable system for parallel symbolic computation. the system is built as an interface between the parallel declarative programming language Strand and the sequential computer al...
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ISBN:
(纸本)0897915895
||MAPLE|| (speak: parallel Maple) is a portable system for parallel symbolic computation. the system is built as an interface between the parallel declarative programming language Strand and the sequential computer algebra system Maple, thus providing the elegance of Strand and the power of the existing sequential algorithms in Maple. the implementation of difFerent parallelprogramming paradigms shows that it is fairly easy to parallelize even complex algebraic algorithms using this system. Sample applications (among them algorithms solving multivariate nonlinear equation systems) are implemented on various parallel architectures. For example a straightforward parallelization of the complex and important problem of real root isolation has been parallelized using a generic Strand program of fewer than 20 Unes of code and a slight modification of 5 lines in the original sequential Maple source. Even with such a simple modification we gained a speed-up of 5 times, that is better than those reported by others in the literature.
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