Cilk (pronounced `silk') is a C-based runtime system for multi-threaded parallelprogramming. In this paper, we document the efficiency of the Cilk work-stealing scheduler, both empirically and analytically. We sh...
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Cilk (pronounced `silk') is a C-based runtime system for multi-threaded parallelprogramming. In this paper, we document the efficiency of the Cilk work-stealing scheduler, both empirically and analytically. We show that on real and synthetic applications, the `work' and `critical path' of a Cilk computation can be used to accurately model performance. Consequently, a Cilk programmer can focus on reducing the work and critical path of his computation, insulated from load balancing and other runtime scheduling issues. We also prove that for the class of `fully strict' (well-structured) programs, the Cilk scheduler achieves space, time, and communication bounds all within a constant factor of optimal. the Cilk runtime system currently runs on the Connection Machine CM5 MPP, the Intel Paragon MPP, the Silicon Graphics Power Challenge SMP, and the MIT Phish network of workstations. Applications written in Cilk include protein folding, graphic rendering, backtrack search, and the Socrates chess program, which won third prize in the 1994 acm International Computer Chess Championship.
Irregular computation problems underlie many important scientific applications. Although these problems are computationally expensive, and so would seem appropriate for parallel machines, their irregular and unpredict...
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Irregular computation problems underlie many important scientific applications. Although these problems are computationally expensive, and so would seem appropriate for parallel machines, their irregular and unpredictable run-time behavior makes this type of parallel program difficult to write and adversely affects run-time performance. this paper explores three issues - partitioning, mutual exclusion, and data transfer - crucial to the efficient execution of irregular problems on distributed-memory machines. Unlike previous work, we studied the same programs running in three alternative systems on the same hardware base (a thinking Machines CM-5): the CHAOS irregular application library, Transparent Shared Memory (TSM), and extensible Shared Memory (XSM). CHAOS and XSM performed equivalently for all three applications. Both systems were somewhat (13%) to significantly faster (991%) than TSM.
Irregular loop nests in which the loop bounds are determined dynamically by indexed arrays are difficult to compile into expressive parallel constructs, such as segmented scans and reductions. In this paper, we descri...
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ISBN:
(纸本)9780897917001
Irregular loop nests in which the loop bounds are determined dynamically by indexed arrays are difficult to compile into expressive parallel constructs, such as segmented scans and reductions. In this paper, we describe a suite of transformations to automatically parallelize such irregular loop nests, even in the presence of recurrences. We describe a simple, general loop flattening transformation, along with new optimizations which make it a viable compiler transformation. A robust recurrence parallelization technique is coupled to the loop flattening transformation, allowing parallelization of segmented reductions, scans, and combining-sends over arbitrary associative operators. We discuss the implementation and performance results of the transformations in a parallelizing Fortran 77 compiler for the Cray C90 supercomputer. In particular, we focus on important sparse matrix-vector multiplication kernels, for one of which we are able to automatically derive an algorithm used by one of the fastest library routines available.
An increasing number of programming languages, such as Fortran 90 and APL, are providing a rich set of intrinsic array functions and array expressions. these constructs which constitute an important part of data paral...
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An increasing number of programming languages, such as Fortran 90 and APL, are providing a rich set of intrinsic array functions and array expressions. these constructs which constitute an important part of data parallel languages provide excellent opportunities for compiler optimizations. In this paper, we present a new approach to combine consecutive data access patterns of array constructs into a composite access function to the source arrays. Our scheme is based on the composition of access functions, which is similar to a composition of mathematic functions. Our new scheme can handle not only data movements of arrays of different numbers of dimensions and segmented array operations but also masked array expressions and multiple sources array operations. As a result, our proposed scheme is the first synthesis scheme which can synthesize Fortran 90 RESHAPE, EOSHIFT, MERGE, and WHERE constructs together. Experimental results show speedups from 1.21 to 2.95 for code fragments from real applications on a Sequent multiprocessor machine by incorporating the proposed optimizations.
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.
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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