Technical Report 2014-005

Full Speed Ahead: Detailed Architectural Simulation at Near-Native Speed

Andreas Sandberg, Erik Hagersten, and David Black-Schaffer

March 2014


Popular microarchitecture simulators are typically several orders of magnitude slower than the systems they simulate. This leads to two problems: First, due to the slow simulation rate, simulation studies are usually limited to the first few billion instructions, which corresponds to less than 10% the execution time of many standard benchmarks. Since such studies only cover a small fraction of the applications, they run the risk of reporting unrepresentative application behavior unless sampling strategies are employed. Second, the high overhead of traditional simulators make them unsuitable for hardware/software co-design studies where rapid turn-around is required.

In spite of previous efforts to parallelize simulators, most commonly used full-system simulations remain single threaded. In this paper, we explore a simple and effective way to parallelize sampling full-system simulators. In order to simulate at high speed, we need to be able to efficiently fast-forward between sample points. We demonstrate how hardware virtualization can be used to implement highly efficient fast-forwarding in the standard gem5 simulator and how this enables efficient execution between sample points. This extremely rapid fast-forwarding enables us to reach new sample points much quicker than a single sample can be simulated. Together with efficient copying of simulator state, this enables parallel execution of sample simulation. These techniques allow us to implement a highly scalable sampling simulator that exploits sample-level parallelism.

We demonstrate how virtualization can be used to fast-forward simulators at 90% of native execution speed on average. Using virtualized fast-forwarding, we demonstrate a parallel sampling simulator that can be used to accurately estimate the IPC of standard workloads with an average error of 2.2% while still reaching an execution rate of 2.0 GIPS (63% of native) on average. We demonstrate that our parallelization strategy scales almost linearly and simulates one core at up to 93% of its native execution rate, 19,000x faster than detailed simulation, while using 8 cores.

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