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The recent TAU 2018 contest was seeking novel idea for efficient generation of timing reports. When the timing graph is updated, users query different forms of timing reports that happen subsequently and sequentially. This process is computationally expensive and inherently complex. Therefore, we introduce in this paper a general cache framework for efficient generation of timing critical paths. Our framework efficiently supports (1) a cache scheme to minimize duplicate calculation, (2) graph contraction to reduce the search space, and (3) multi-threading. We evaluated our framework on the TAU 2018 contest benchmarks and demonstrated promising performance over the top performer.

A General Cache Framework for Efficient Generation of Timing Critical Paths