An automatic data distribution generator for distributed memory machines

Crooks, P.; Perrott, Ronald H.

doi:10.1002/(sici)1096-9128(199807)10:8<607::aid-cpe330>3.0.co;2-g

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Most of the ideas presented in this paper, can be also used to extend this approach to dynamic data mappings, where a set of computational intensive phases are detected, the mapping for each of them is obtained, and remapping actions (realignment and redistribution) between phases are introduced. This topic is also investigated by other research groups [4,8,9,17].…”

Section: Namementioning

confidence: 92%

Using a 0–1 Integer Programming Model for Automatic Static Data Distribution

García

Ayguadé

Labarta

1996

Parallel Process. Lett.

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This paper describes an automatic data distribution method which deal with both the alignment and the distribution problems in a single optimization phase, as opposed to sequentially solving these two inter-dependent approaches as done by previous work. The core of this work is called the Communication-Parallelism Graph, which describes the relationships among array dimensions of the same and different array references regarding communication and parallelism. The overall data distribution problem is then formulated as a linear 0–1 integer programming problem, where the objective function to be minimized is the total execution time. The solution is static in the sense that the layout of the arrays does not change during the execution of the program. We also show the feasibility of using this approach to solve the problem in terms of compilation time and quality of the solutions generated.

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Section: Namementioning

confidence: 92%

Using a 0–1 Integer Programming Model for Automatic Static Data Distribution

García

Ayguadé

Labarta

1996

Parallel Process. Lett.

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“…Some of the proposals presented in the literature about array r~mapping [:'i. [13][14][15][16][17][18][19][20] art> ~umm:uized in the rest of this section.…”

Section: Introductionmentioning

confidence: 99%

DDT: A Research Tool for Automatic Data Distribution in High Performance Fortran

Ayguadé

García

Gironès

et al. 1996

Scientific Programming

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This article describes the main features and implementation of our automatic data distribution research tool. The tool (DDT) accepts programs written in Fortran 77 and generates High Performance Fortran (HPF) directives to map arrays onto the memories of the processors and parallelize loops, and executable statements to remap these arrays. DDT works by identifying a set of computational phases (procedures and loops). The algorithm builds a search space of candidate solutions for these phases which is explored looking for the combination that minimizes the overall cost; this cost includes data movement cost and computation cost. The movement cost reflects the cost of accessing remote data during the execution of a phase and the remapping costs that have to be paid in order to execute the phase with the selected mapping. The computation cost includes the cost of executing a phase in parallel according to the selected mapping and the owner computes rule. The tool supports interprocedural analysis and uses control flow information to identify how phases are sequenced during the execution of the application.

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“…The FCS system [CP93] considers the problem in the framework of a data distribution tool for Fortran90 source codes. A phase is basically a DO-loop containing array-syntax assignment statements or WHERE masks in its body.…”

Section: Other Proposalsmentioning

confidence: 99%

Automatic data distribution for massively parallel processors

Almiñana¹

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Massively Parallel Processor systems provide the required computational power to solve most large scale High Performance Computing applications. Machines with physically distributed memory allow a cost-effective way to achieve this performance, however, these systems are very diffcult to program and tune. In a distributed-memory organization each processor has direct access to its local memory, and indirect access to the remote memories of other processors. But the cost of accessing a local memory location can be more than one order of magnitude faster than accessing a remote memory location. In these systems, the choice of a good data distribution strategy can dramatically improve performance, although different parts of the data distribution problem have been proved to be NP-complete. The selection of an optimal data placement depends on the program structure, the program's data sizes, the compiler capabilities, and some characteristics of the target machine. In addition, there is often a trade-off between minimizing interprocessor data movement and load balancing on processors. Automatic data distribution tools can assist the programmer in the selection of a good data layout strategy. These use to be source-to-source tools which annotate the original program with data distribution directives. Crucial aspects such as data movement, parallelism, and load balance have to be taken into consideration in a unified way to efficiently solve the data distribution problem. In this thesis a framework for automatic data distribution is presented, in the context of a parallelizing environment for massive parallel processor (MPP) systems. The applications considered for parallelization are usually regular problems, in which data structures are dense arrays. The data mapping strategy generated is optimal for a given problem size and target MPP architecture, according to our current cost and compilation model. A single data structure, named Communication-Parallelism Graph (CPG), that holds symbolic information related to data movement and parallelism inherent in the whole program, is the core of our approach. This data structure allows the estimation of the data movement and parallelism effects of any data distribution strategy supported by our model. Assuming that some program characteristics have been obtained by profiling and that some specific target machine features have been provided, the symbolic information included in the CPG can be replaced by constant values expressed in seconds representing data movement time overhead and saving time due to parallelization. The CPG is then used to model a minimal path problem which is solved by a general purpose linear 0-1 integer programming solver. Linear programming techniques guarantees that the solution provided is optimal, and it is highly effcient to solve this kind of problems. The data mapping capabilities provided by the tool includes alignment of the arrays, one or two-dimensional distribution with BLOCK or CYCLIC fashion, a set of remapping actions to be performed between phases if profitable, plus the parallelization strategy associated. The effects of control flow statements between phases are taken into account in order to improve the accuracy of the model. The novelty of the approach resides in handling all stages of the data distribution problem, that traditionally have been treated in several independent phases, in a single step, and providing an optimal solution according to our model.

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An automatic data distribution generator for distributed memory machines

Cited by 3 publications

References 14 publications

Using a 0–1 Integer Programming Model for Automatic Static Data Distribution

Using a 0–1 Integer Programming Model for Automatic Static Data Distribution

DDT: A Research Tool for Automatic Data Distribution in High Performance Fortran

Automatic data distribution for massively parallel processors

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