FastFlip: Compositional Error Injection Analysis
CoRR(2024)
摘要
Instruction-level error injection analyses aim to find instructions where
errors often lead to unacceptable outcomes like Silent Data Corruptions (SDCs).
These analyses require significant time, which is especially problematic if
developers wish to regularly analyze software that evolves over time.
We present FastFlip, a combination of empirical error injection and symbolic
SDC propagation analyses that enables fast, compositional error injection
analysis of evolving programs. FastFlip calculates how SDCs propagate across
program sections and correctly accounts for unexpected side effects that can
occur due to errors. Using FastFlip, we analyze five benchmarks, plus two
modified versions of each benchmark. FastFlip speeds up the analysis of
incrementally modified programs by 3.2× (geomean). FastFlip selects a
set of instructions to protect against SDCs that minimizes the runtime cost of
protection while protecting against a developer-specified target fraction of
all SDC-causing errors.
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