Detailed modeling of the performance of commercial applications is difficult. The applications can take a very long time to run on real hardware and it is impractical to simulate them to completion on performance models. Furthermore, these applications have complex execution environments that cannot easily be reproduced on a simulator, making porting the applications to simulators difficult. We attack these problems using the well-known SimPoint methodology to find representative portions of an application to simulate, and a dynamic instrumentation framework called Pin to avoid porting altogether. Our system uses dynamic instrumentation instead of simulation to find representative portions - called Pin-Points - for simulation. We have developed a toolkit that automatically detects PinPoints, validates whether they are representative using hardware performance counters, and generates traces for large Itanium? programs. We compared SimPoint-based selection to random selection of simulation points. We found for 95% of the SPEC2000 programs we tested, the PinPoints prediction was within 8% of the actual whole-program CPI, as opposed to 18% for random selection. We measure the end-to-end error, comparing real hardware to a performance model, and have a simple and efficient methodology to determine the step that introduced the error. Finally, we evaluate the system in the context of multiple configurations of real hardware, commercial applications, and industrial-strength performance models to understand the behavior of a complete and practical workload collection system. We have successfully used our system with many commercial Itanium? programs, some running for trillions of instructions, and have used the resulting traces for predicting performance of those applications on future Itanium processors.