BLUME, AND EIGENMANN. Nonlinear and symbolic data dependence testing. IEEE Transactions on Parallel and Distributed Systems 9, 12 (Dec 1998), 1180--1194. Home/Search Document Details and Download
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Parallel Programming using the Iteration Space Visualizer - Yu, D'Hollander (2000) (Correct)
.... an interactive programming tool to study data dependences and program transformations [7] During the past decades, many techniques in the area of data dependence tests [2, 14, 13] and program transformations have provided the programmer with much useful material, e.g. the Banerjee, Range [3, 4] and Omega [15, 13] tests, the unimodular [1, 5, 20] and non singular [16] loop transformations and recently statement reordering transformations [9, 12, 11] for non perfectly nested loops. Most techniques are illustrated by dependence graphs, such as the program dependence graph(PDG) and the ....
W. Blume and R. Eigenmann. Nonlinear and symbolic data dependence testing. IEEE Transactions on Parallel and Distributed Systems, 9(12):1180--1194, Dec. 1998.
Compilation Techniques for Parallel Systems - Gupta, Pande, Psarris, Sakar (1999) (2 citations) (Correct)
....with statically unknown information. The existing dependence tests can not handle loop bounds or array subscripts that are symbolic or nonlinear expressions. In the presence of symbolic or nonlinear expressions, dependence is usually assumed. In certain cases, symbolic program analysis techniques [28, 73] can help to overcome this problem and enable the effective parallelization of a larger class of applications. But even symbolic analysis has its limitations, simply because the necessary information can not be obtained or inferred at compile time. In order to realize the full potential of ....
W. Blume, R. Eigenmann, "Nonlinear and Symbolic Data Dependence Testing", IEEE Transactions on Parallel and Distributed Systems, Vol. 9, No. 12, December 1998.
Multiplex: Unifying Conventional and Speculative.. - Ooi, Kim, Park.. (2001) (3 citations) Self-citation (Eigenmann) (Correct)
....threads, eliminate speculation overhead, and realize the raw hardware speeds of multiple CMP cores. Multiplex relies on a state of the art parallelizing compiler to analyze programs and generate explicit threads. These compilers (e.g. Polaris [4] SUIF [14] use a myriad of techniques to test [5,25,11] and eliminate data dependence in program segments [2,32,24,13] Moreover, these compilers increase thread performance in analyzable program segments through code transformations to optimize for memory hierarchy locality and communication latency [14] Once the compiler selects appropriate ....
W. Blume and R. Eigenmann. Non-linear and symbolic data dependence testing. IEEE Transactions on Parallel and Distributed Systems, 9(12):1180--1194, Dec. 1998.
Multiplex: Unifying Conventional and Speculative.. - Kim, Ooi, Park.. Self-citation (Eigenmann) (Correct)
....can direct thread dispatch using a fork primitive, and obviate the need for hardware prediction and eliminating any potential misprediction overhead. Similarly, there are many scenarios where an advanced parallelizing compiler can either detect and guarantee no data dependences among threads exist [7,27,14] or can eliminate the data dependences (e.g. through array privatization [34,16] In such scenarios, the hardware unnecessarily tracks data dependences, limiting the scope of parallelism to the buffering capacity in the L1 caches. 3 Multiplex: Unifying Explicit Implicit TLP on a CMP In this ....
....eliminate hardware speculation overhead, and realize the raw hardware speeds of multiple CMP cores. Multiplex relies on a state of the art parallelizing compiler to analyze programs and generate explicit threads. These compilers (e.g. Polaris [6] SUIF [17] use a myriad of techniques to test [7,27,14] and eliminate data dependence in program segments [3,34,26,16] Moreover, these compilers increase thread performance in analyzable program segments through code transformations to optimize for memory hierarchy locality and communication latency [17] In explicit threading, thread size plays a ....
W. Blume and R. Eigenmann. Non-linear and symbolic data dependence testing. IEEE Transactions on Parallel and Distributed Systems, 9(12):1180--1194, Dec. 1998.
Value Range Analysis of Conditionally Updated Variables .. - Birch, van Engelen.. (2004) (Correct)
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BLUME, AND EIGENMANN. Nonlinear and symbolic data dependence testing. IEEE Transactions on Parallel and Distributed Systems 9, 12 (Dec 1998), 1180--1194.
A Symbolic Approach to Bernstein Expansion for Program.. - Clauss, Tchoupaeva (2004) (Correct)
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W. Blume and R. Eigenmann. Non-linear and symbolic data dependence testing. IEEE Transactions on Parallel and Distributed Systems, 9(12):1180--1194, December 1998.
Loop Parallelization using the 3D Iteration Space Visualizer - Yu, D'Hollander (2001) (Correct)
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William Blume and Rudolf Eigenmann, Nonlinear and symbolic data dependence testing, IEEE Transactions on Parallel and Distributed Systems 9(1998), no. 12, 1180-1194.
Parallel Programming using the Iteration Space Visualizer - Yu, D'Hollander (Correct)
No context found.
W. Blume and R. Eigenmann. Nonlinear and symbolic data dependence testing. IEEETransactions onParallel andDistributed Systems, 9(12):1180 1194, Dec. 1998.
Index-Association Based Dependence Analysis and Its Application .. - Song, Kong (Correct)
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William Blume and Rudolf Eigenmann. Non-linear and symbolic data dependence testing. IEEE Transactions of Parallel and Distributed Systems, 9(12):1180--1194, December 1998.
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