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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2000年第6期28卷 607-631页

作者： Griebl, M Feautrier, P Lengauer, C Univ Passau FMI D-94030 Passau Germany Univ Versailles PRiSM F-78035 Versailles France

There are many algorithms for the space-time mapping of nested loops. Some of them even make the optimal choices within their framework. We propose a preprocessing phase for algorithms in the polytope model, which extends the model and yields space-time mappings whose schedule is, in some cases, orders of magnitude faster. These are cases in which the dependence graph has small irregularities. The basic idea is to split the index set of the loop nests into parts with a regular dependence structure and apply the existing space-time mapping algorithms to these parts individually. This work is based on a seminal idea in the more limited context of loop parallelization at the code level. We elevate the idea to the model level (our model is the polytope model), which increases its applicability by providing a clearer and wider range of choices at an acceptable analysis cost. Index set splitting is one facet in the effort to extend the power of the polytope model and to enable the generation of competitive tar-get code.

关键词： automatic loop parallelization scheduling polytope model

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Speculative parallelization

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COMPUTER 2006年第12期39卷 126-128页

作者： Gonzalez-Escribano, Arturo Llanos, Diego R. Univ Valladolid Dept Informat E-47002 Valladolid Spain

The most promising technique for automatically parallelizing loops when the system cannot determine dependences at compile time is speculative parallelization. Also called thread-level speculation, this technique assumes optimistically that the system can execute all iterations of a given loop in parallel. A hardware or software monitor divides the iterations into blocks and assigns them to different threads, one per processor, with no prior dependence analysis. If the system discovers a dependence violation at runtime, it stops the incorrectly computed work and restarts it with correct values. Of course, the more parallel the loop, the more benefits this technique delivers. To better understand how speculative parallelization works, it is necessary to distinguish between private and shared variables. Informally speaking, private variables are those that the program always modifies in each iteration before using them. On the other hand, values stored in shared variables are used in different iterations.

关键词： System Monitoring Multi Threading Parallelising Compilers Program Control Structures Runtime Dependence Violation Discovery Speculative parallelization automatic loop parallelization Program Compiler Thread Level Speculation Hardware Monitor Software Monitor Program Dependence Analysis Hardware Monitoring Runtime Performance Loss Concurrent Computing Speculative parallelization How Things Work

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Finding Free Schedules for RNA Secondary Structure Prediction 15th

Finding Free Schedules for RNA Secondary Structure Predictio...

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15th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Palkowski, Marek West Pomeranian Univ Technol Szczecin Fac Comp Sci & Informat Syst Zolnierska 49 PL-71210 Szczecin Poland

ISBN: (纸本)9783319393841

An approach permitting to build free schedules for the RNA folding algorithm is proposed. The statements can be executed in parallel as soon as all their operands are available. This technique requires exact dependence analysis for automatic parallelization of the Nussinov algorithm. To describe and implement the algorithm the dependence analysis by Pugh and Wonnacott was chosen where dependencies are found in the form of tuple relations. The approach has been implemented and verified by means of the islpy and CLooG tools as a part of the TRACO compiler. The experimental study presents speed-up, scalability and costs of parallelism of the output code. Related work and future tasks are described.

关键词： Computational biology Free schedules The Nussinov algorithm automatic loop parallelization RNA folding

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On Computing Solutions of Linear Diophantine Equations with One Non-linear Parameter

On Computing Solutions of Linear Diophantine Equations with ...

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10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

作者： Groesslinger, Armin Schuster, Stefan Univ Passau Dept Math & Informat D-94030 Passau Germany

ISBN: (纸本)9780769535234

We present an algorithm for solving Diophantine equations which are linear in the variables, but non-linear in one parameter. We are looking for the pointwise solutions, i.e., the solutions for the unknowns in dependence of the value of the parameter. Solving Diophantine equations is central to computing the data dependences of certain codes (loops with certain array accesses) which often occur in scientific computing. Our algorithm enables the computation of data dependences in more general situations than is possible with current algorithms.

关键词： linear diophantine equations non-linear parameter quasi-polynomials automatic loop parallelization polyhedron model

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Usage of the TRACO Compiler for Neural Network parallelization

Usage of the TRACO Compiler for Neural Network Parallelizati...

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13th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Palkowski, Marek Bielecki, Wlodzimierz West Pomeranian Univ Technol Szczecin Fac Comp Sci & Informat Syst PL-71210 Szczecin Poland

ISBN: (纸本)9783319071725;9783319071732

Artificial neural networks (ANNs) are used often to solve a wide variety of problems using high performance computing. The paper presents automatic loop parallelization for selected ANNs programs by means of the TRACO compiler that permits us to extract loop dependences and produce synchronization-free slices including loop statement instances. Coarse-grained parallelism of nested program loops is obtained by creating a thread of computations on each processor to be executed independently. Program loops of recurrent and back-propagation networks are analysed. The speed-up and efficiency of parallel programs produced by means of TRACO are studied. Related compilers and ANNs parallelization techniques are considered. Future work is outlined.

关键词： artificial neural networks automatic loop parallelization iteration space slicing multi-core processing

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automatic DATA AND COMPUTATION DECOMPOSITION FOR DISTRIBUTED-MEMORY MACHINES

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Parallel Processing Letters 1995年第4期5卷 539-550页

作者： QI NING VINCENT VAN DONGEN GUANG R. GAO Qi Ning is now affiliated with Convex Computer Corporation 3000 Waterview Parkway Richardson TX 75083-3851. Centre de Recherche Informatique de Montréal 1801 McGill Avenue Bureau 800 Montreal Quebec Canada H3A 2N4 Canada School of Computer Science McGill University Montreal Quebec Canada H3A 2A7 Canada

In this paper, we develop an automatic compile-time computation and data decomposition technique for distributed-memory machines. Our method handles complex programs containing perfect and non-perfect loop nests with or without loop-carried dependences. Applying our algorithms, a program will be divided into collections (called clusters) of loop nests, such that data redistributions are allowed only between the clusters. Within each cluster of loop nests, decomposition and data locality constraints are formulated as a system of homogeneous linear equations which is solved by polynomial time algorithms. Our algorithm can selectively relax data locality constraints within a cluster to achieve a balance between parallelism and data locality. Such relaxations are guided by exploiting the hierarchical program nesting structures from outer to inner nesting levels to keep the communications at a outer-most level possible.

关键词： automatic loop parallelization parallelizing compiler data computation decomposition for distributed-memory machines

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