Randomized parallel algorithms for many fundamental problems achieve optimal linear work in expectation, but upgrading this guarantee to hold with high probability (whp) remains a recurring theoretical challenge. In this paper, we address this gap for several core parallel primitives. First, we present the first parallel semisort algorithm achieving $O(n)$ work and $O(\text{polylog } n)$ depth whp, improving upon the $O(n)$ expected work bound of Gu et al. [SPAA 2015]. Our analysis introduces new concentration arguments based on simple tabulation hashing and tail bounds for weighted sums of geometric random variables. As a corollary, we obtain an integer sorting algorithm for keys in $[n]$ matching the same bounds. Second, we introduce a framework for boosting randomized parallel graph algorithms from expected to high probability linear work. The framework applies to \emph{locally extendable} problems -- those admitting a deterministic procedure that extends a solution across a graph cut in work proportional to the cut size. We combine this with a \emph{culled balanced partition} scheme: an iterative culling phase removes a polylogarithmic number of high-degree vertices, after which the remaining graph admits a balanced random vertex whp via a bounded-differences argument. Applying work-inefficient whp subroutines to the small pieces and deterministic extension across cuts yields overall linear work whp. We instantiate this framework to obtain $O(m)$ work and polylogarithmic depth whp algorithms for $(Δ+1)$-vertex coloring and maximal independent set.