Several large real-world applications have been developed for distributed
and parallel architectures. We examine two different program development
approaches: First, the usage of a high-level programming paradigm which
reduces the time to create a parallel program dramatically but sometimes
at the cost of a reduced performance. A source-to-source compiler has been
employed to automatically compile programs - written in a high-level
programming paradigm - into message passing codes. Second, manual program
development by using a low-level programming paradigm - such as message
passing - enables the programmer to fully exploit a given architecture at
the cost of a time-consuming and error-prone effort.

Performance tools play a central role to support the performance-oriented
development of applications for distributed and parallel architectures.
SCALA - a portable instrumentation, measurement, and post-execution
performance analysis system for distributed and parallel programs - has
been used to analyse and to guide the application development by selectively
instrumenting and measuring the code versions, by comparing performance
information of several program executions, by computing a variety of
important performance metrics, by detecting performance bottlenecks, and
by relating performanceinformation back to the input program. We show
several experiments of SCALA when applied to real-world applications. These
experiments are conducted for a NEC Cenju-4 distributed memory machine and
a cluster of heterogeneous workstationsand networks.