By Thomas Fahringer
Automatic functionality Prediction of Parallel Programs offers a unified method of the matter of instantly estimating the functionality of parallel computing device courses. the writer focuses totally on disbursed reminiscence multiprocessor structures, even if huge parts of the research may be utilized to shared reminiscence architectures besides.
the writer introduces a singular and extremely useful technique for predicting one of the most vital functionality parameters of parallel courses, together with paintings distribution, variety of transfers, volume of information transferred, community competition, move time, computation time and variety of cache misses. This technique relies on complicated compiler research that conscientiously examines loop generation areas, technique calls, array subscript expressions, conversation styles, information distributions and optimizing code ameliorations on the software point; and an important laptop particular parameters together with cache features, verbal exchange community indices, and benchmark info for computational operations on the computing device point.
the cloth has been absolutely carried out as a part of P3T, that's an built-in computerized functionality estimator of the Vienna Fortran Compilation procedure (VFCS), a state of the art parallelizing compiler for Fortran77, Vienna Fortran and a subset of excessive functionality Fortran (HPF) courses.
loads of experiments utilizing lifelike HPF and Vienna Fortran code examples exhibit hugely actual functionality estimates, and the facility of the defined functionality prediction method of effectively advisor either programmer and compiler in parallelizing and optimizing parallel courses.
A graphical person interface is defined and displayed that visualizes every one software resource line including the corresponding parameter values. P3T makes use of color-coded functionality visualization to right away establish sizzling spots within the parallel application. functionality facts will be filtered and displayed at a variety of degrees of element. shades displayed by means of the graphical consumer interface are visualized in greyscale.
Automatic functionality Prediction of Parallel Programs additionally contains assurance of primary difficulties of automated parallelization for allotted reminiscence multicomputers, an outline of the elemental parallelization method and a wide number of optimizing code variations as integrated below VFCS.
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Additional info for Automatic Performance Prediction of Parallel Programs
2 Iteration Count INPUT: Non-instrumented loop L: DO I=LB,UB ENDDO OUTPUT: Instrumented loop S1: S2: S3: S4: L: $LB = LB $UB = UB $Slb = $Slb + $LB $Sub = $Sub + $UB DO I=$LB,$UB ENDDO S1 and S2 are instrumentation statements. For the sake of simplicity we assume that L cannot be a labeled statement.
Our approach enables the user to find these program parts by runtime profiling. A second problem deals with program unknowns for which characteristic values must be obtained in order to derive reasonably accurate and relevant performance information. Usually statement execution and loop iteration counts are of crucial importance for the performance of a given program. This information either depends on the problem size, for instance, loops frequently sweep across the main arrays of a program, or a convergence condition, which is commonly expressed by a conditional exit GOTO in a loop.
2 49 SEQUENTIAL PROGRAM PARAMETERS In order to guide the parallelizer to the computation intensive program parts (see also ) we use mainly profile times l . This means, that for specific program statements or regions of interest, the Weight Finder actually measures accumulated times - which depend on the program input data - on a sequential processor. 1 Let S E S of a program Q, then the profile time is defined by a partial function ptime : S ---+ Rt, with Rt, the set of positive real numbers including zero, which is the accumulated measured runtime for S during a single execution of Q.