Offsite Autotuning Approach : Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods

Title data

Seiferth, Johannes ; Korch, Matthias ; Rauber, Thomas:
Offsite Autotuning Approach : Performance Model Driven Autotuning Applied to Parallel Explicit ODE Methods.
In: Sadayappan, Ponnuswamy ; Chamberlain, Bradford L. ; Juckeland, Guido ; Ltaief, Hatem (ed.): High Performance Computing. - Cham : Springer , 2020 . - pp. 370-390 . - (Lecture Notes in Computer Science ; 12151 )
ISBN 978-3-030-50743-5
DOI: https://doi.org/10.1007/978-3-030-50743-5_19

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Abstract in another language

Autotuning (AT) is a promising concept to minimize the often tedious manual effort of optimizing scientific applications for a specific target platform. Ideally, an AT approach can reliably identify the most efficient implementation variant(s) for a new platform or new characteristics of the input by applying suitable program transformations and analytic models. In this work, we introduce Offsite, an offline AT approach that automates this selection process at installation time by rating implementation variants based on an analytic performance model without requiring time-consuming runtime tests. From abstract multilevel description languages, Offsite automatically derives optimized, platform-specific and problem-specific code of possible variants and applies the performance model to these variants.