In order to scale with modern processors, planning algorithms must become multi-threaded. In this paper, we present parallel shared-memory algorithms for two problems that underlie many planning systems: suboptimal and anytime heuristic search. We extend a recently-proposed approach for parallel optimal search to the suboptimal case, providing two new pruning rules for bounded suboptimal search. We also show how this new approach can be used for parallel anytime search. Using temporal logic, we prove the correctness of our framework, and in an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8-core machine, we show that it yields faster search performance than previous proposals.