Delorean: Virtualized Directed Profiling for Cache Modeling in Sampled Simulation

Current practice for accurate and efficient simulation (e.g., SMARTS and Simpoint) makes use of sampling to significantly reduce the time needed to evaluate new research ideas. By evaluating a small but representative portion of the original application, sampling can allow for both fast and accurate performance analysis. However, as cache sizes of modern architectures grow, simulation time is dominated by warming microarchitectural state and not by detailed simulation, reducing overall simulation efficiency. While checkpoints can significantly reduce cache warming, improving efficiency, they limit the flexibility of the system under evaluation, requiring new checkpoints for software updates (such as changes to the compiler and compiler flags) and many types of hardware modifications. An ideal solution would allow for accurate cache modeling for each simulation run without the need to generate rigid checkpointing data a priori. Enabling this new direction for fast and flexible simulation requires a combination of (1) a methodology that allows for hardware and software flexibility and (2) the ability to quickly and accurately model arbitrarily-sized caches. Current approaches that rely on checkpointing or statistical cache modeling require rigid, up-front state to be collected which needs to be amortized over a large number of simulation runs. These earlier methodologies are insufficient for our goals for improved flexibility. In contrast, our proposed methodology, Delorean, outlines a unique solution to this problem. The Delorean simulation methodology enables both flexibility and accuracy by quickly generating a targeted cache model for the next detailed region on the fly without the need for up-front simulation or modeling. More specifically, we propose a new, more accurate statistical cache modeling method that takes advantage of hardware virtualization to precisely determine the memory regions accessed and to minimize the time needed for data collection while maintaining accuracy. Delorean uses a multi-pass approach to understand the memory regions accessed by the next, upcoming detailed region. Our methodology collects the entire set of key memory accesses and, through fast virtualization techniques, progressively scans larger, earlier regions to learn more about these key accesses in an efficient way. Using these techniques, we demonstrate that Delorean allows for the fast evaluation of systems and their software though the generation of accurate cache models on the fly. Delorean outperforms previous proposals by an order of magnitude, with a simulation speed of 150 MIPS and a similar average CPI error (below 4%)