LoopIn: A Loop-Based Simulation Sampling Mechanism

Understanding program behavior is at the heart of general-purpose architecture design. Whether we are testing a new design offline or making a design adapt to changing behavior online, a central assumption is that the test cases represent real workload in steady state. Typical computer programs have been known to exhibit patterns of runtime behavior that repeat during the course of their execution. Simulation and adaptation strategies all exploit this repetition to some extent. In this paper, we introduce a simple mechanism that is more explicit in identifying and exploiting behavior repetition at the granularity of (broadly defined) loops. The result is that a typical benchmark will be categorized into tens of loops. In terms of architectural simulations, this strategy will create a moderate number (on the orders of 100) of relatively short (tens of thousands of instructions) segments. There are two major benefits in our view. The first and more quantifiable benefit is that, the strategy requires less simulation and obtains increased accuracy compared to the commonly used SimPoint approach. Second, instead of depicting average statistics of an entire program, we can accurately describe intra-program behavior variation, which simple sampling strategies cannot. LoopIn produces many small simulation segments. In certain usage scenarios, microarchitectural state warm-up may be costly. In these cases, an existing tool BLRL can help create efficient warm-up arrangements.