Parallel programming is elusive. The relative performance of different parallel implementations varies with machine architecture, system and problem size. How to compare different implementations over a wide range of machine architectures and problem sizes has not been well addressed due to its difficulty. Scalability has been proposed in recent years to reveal scaling properties of parallel algorithms and machines. In this paper, the relation between scalability and execution time is carefully studied. The concepts of crossing point analysis and range comparison are introduced. Crossing point analysis finds slow/fast performance crossing points of parallel algorithms and machines. Range comparison compares performance over a wide range of ensemble and problem size via scalability and crossing point analysis. Three algorithms from scientific computing are implemented on an Intel Paragon and an IBM SP2 parallel computer. Experimental and theoretical results show how the combination of scalability, crossing point analysis, and range comparison provides a practical solution for scalable performance evaluation and prediction. While our testings are conducted on homogeneous parallel computers, the proposed methodology applies to heterogeneous and network computing as well.