부산대학교 도서관

In this paper we focus on generating efficient parallel codes for solving scientific problems within large-scale performance-critical applications. More specifically we present techniques for high-level symbolic optimization for automatic generation of efficient codes that numerically solve sets of computationally expensive right-hand sides (RHSs) of systems of partial differential equations (PDEs). These type of PDE problems can be found in application areas like numerical weather prediction, ocean circulation modeling, and climate modeling. To solve the PDEs describing the physical problem, high-performance software environments and computer platforms are required. The CTADEL application driver is a software environment for generating optimized codes for serial, vector, or parallel architectures from a high-level PDE problem description of a weather forecast model. The advantage of the high-level problem description is that all of the high-level information can be exploited for parallelization, restructuring, and optimization from the highest descriptive level to the low-level generated code.