Observing the activities of a complex parallel computer system is no small feat, and relating these observations to program behavior is even harder. In this paper, we present a general measurement approach that is applicable to a large class of scalable programs and machines, specifically SPMD and data-parallel programs executing on distributed memory computer systems. The combined instrumentation and visualization paradigm, called VISTA, is based on our experiences in programming and monitoring applications running on an nCUBE 2 computer and a MasPar MP-1 computer. The key is that performance data are treated similarly to any distributed data in the context of the programming models and presented via a hierarchy of multiple views. Because of the data-parallel mapping of program onto machine, we can view the performance as it relates to each processor, processor cluster, or the processor ensemble and as it relates to the data structures of the program. We illustrate the utility of VISTA by example.