PHYS: Profiled-HYbrid Sampling for soft error reliability benchmarking

In this paper, we introduce PHYS (Profiled-HYbrid Sampling), a sampling framework for soft-error benchmarking of caches. Reliability simulations of caches are much more complex than performance simulations and therefore exhibit large simulation slowdowns (two orders of magnitude) over performance simulations. The major problem is that the reliability lifetime of every accessed block must be tracked from beginning to end, on top of simulating the benchmark, in order to track the total number of vulnerability cycles (VCs) between two accesses to the block. Because of the need to track SDCs (silent error corruption) and to distinguish between true and false DUEs (detected but unrecoverable errors) vulnerability cycles cannot be truncated when data is written back from cache to main memory. Vulnerability cycles must be maintained even during a block's sojourn in main memory to track whether corrupted values in a block are used by the processor, until program termination. PHYS solves this problem by sampling intervals between accesses to each memory block, instead of sampling the execution of the processor in a time interval as is classically done in performance simulations. At first a statistical profiling phase captures the distribution of VCs for every block. This profiling step provides a statistical guarantee of the minimum sampling rate of access intervals needed to meet a desired FIT error target with a given confidence interval. Then, per cacheset sampling rates are dynamically adjusted to sample VCs with higher merit. We compare PHYS with many other possible sampling methods, some of which are widely used to accelerate performance-centric simulations but have also been applied in the past to track reliability lifetime. We demonstrate the superiority of PHYS in the context of reliability benchmarking through exhaustive evaluations of various sampling techniques.