Abstract
This article motivates the usage of graphics and visualization for efficient utilization of High Performance Fortran's (HPF's) data distribution facilities. It proposes a graphical toolkit consisting of exploratory and estimation tools which allow the programmer to navigate through complex distributions and to obtain graphical ratings with respect to load distribution and communication. The toolkit has been implemented in a mapping design and visualization tool which is coupled with a compilation system for the HPF predecessor Vienna Fortran. Since this language covers a superset of HPF's facilities, the tool may also be used for visualization of HPF data structures.
Highlights
Di~trihuted memory multicomputer~ are increasingly being nsC'd in scil'ntiflc computing for high-pcrforrrumce calculations as they offer many ath-antages over shared mPmory architectures concerning scalabilit ~ and costs
According to thr previous critf'ria, this task can be defined more preeisdy: All data involved in a sine.de computation should be located on the same pron'ssor or neighboring processors such that hops and network contention can lw avoided wllrnevcr possible. llnsuitable distributions eanse HHH:h elmmmnieation wlwre the locations of Iwrformance bottlf'nf'cks cannot hP determined from the HPF source text
Vienna Fortran's language extensions for data-parallel programming can be regarded as a superset of those provided by HPF
Summary
Di~trihuted memory multicomputer~ are increasingly being nsC'd in scil'ntiflc computing for high-pcrforrrumce calculations as they offer many ath-antages over shared mPmory architectures concerning scalabilit ~ and costs. Vendors and rest'archers have introduced language extensions to Fortran 77 and Fortran 90 which allow explicit srwcitication of distribution layouts for data arrays. The nse of graphics and visualization is expected to increase the tool's value. Fn its current form our graphical data distriburion tool (GDDT) aims at the Fortran 77 extension Vierma Fortran [5]. Rvaluates distribution srwcifieations, and creates a range of graphical displays including view;-rs for data arrays. In Sf·ction 2 we motivate tlw advantages of visual support for data distribution and point out tiH~ benefit:-. Furtherrnon·, a dear separation betwren compile-time and post-mortem support is made This separation is essential in order to defirw the seope of visualization data, which affects the tool's eapabilitiPs. Section 4 surveys related work and segregates our tool from other research systen1s by means of a short classification.
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