The realization of parallel language systems that offer high-level programming paradigms to reduce the complexity of application development, scalable runtime mechanisms to support variable size problem sets, and portable compiler platforms to provide access to multiple parallel architectures, places additional demands on the tools for program development and analysis. The need for integration of these tools into a comprehensive programming environment is even more pronounced and will require more sophisticated use of the language system technology (i.e., compiler and runtime system). Furthermore, the environment requirements of high-level support for the programmer, large-scale applications, and portable access to diverse machines also apply to the program analysis tools. In this paper, we discuss ø (TAU, Tuning and Analysis Utilities), a first prototype for an integrated and portable program analysis environment for pC++ , a parallel object-oriented language system. ø is integrated w...

nd lines of code. There are major hurdles in taking advantage of existing legacy codes. It is time consuming to manually parallelize (transform from sequential to parallel program) these codes. Also, the manual approach is prone to errors and debugging those errors is very difficult. As a result, the magnitude of manual efforts required for parallelizing legacy codes is prohibitively large. For example, the parallelization of the non-hydrostatic version of MM5 took about three and half years for a team of scientists at Argonne National Laboratory (ANL). Recently, we (in collaboration with ANL scientists) completed the parallelization of the hydrostatic version of MM5 and it took us two years. Difficulties in manual parallelization clearly point to a need for some type of automation. Several automatic and semi-automatic tools have been developed. Doreen Cheng has published an extensive survey [Cheng, 1993 ] with 94 entries for parallel programming tools out of which 9 are identified as

Applications programming for High Performance Computing is notoriously difficult. Although Parallel Programming is intrinsically complex, the principal reason why High Performance Computing is difficult is the lack of effective software tools. We believe that the lack of tools in turn is largely due to market forces rather than our inability to design and build such tools. Unfortunately, the poor availability and utilization of parallel tools hurts the entire supercomputing industry and the US High Performance Computing initiative which is focused on applications. A disproportionate amount of resources are being spent on faster hardware and architectures, while tools are being neglected. This paper introduces a taxonomy of tools, analyzes the major factors that contribute to this situation, and suggest ways that the imbalance could be redressed and the likely evolution of tools. 1 Received November 1994 Revised October 1995 Many attendees at the May 1993 Workshop on Parallel Compu...