We present our study of solving large unit commitment problems in the California ISO planning model. The model calculates hourly day-ahead unit commitments, and all instances need to be solved close to optimality within an hour. It takes CPLEX, the current state-of-the-art solver, up to 5 and 10 hours to solve the deterministic instances and the 5-scenario stochastic instances, respectively. The 20-scenario instances are practically unsolvable as no feasible solutions are found after 24hours.We consider improving solution times through distributed-memory parallelization. Prior techniques such as distributed branch-and-bound perform poorly for our problems. We propose coordinated concurrent search to solve the deterministic instances on a cluster. For stochastic instances, we propose parallelization strategy that combines scenario-based decomposition and asynchronous solves guided by intermediate results from progressive hedging. Our decomposition creates linear sub problems instead of quadratic ones that are oftentimes intractable. On a cluster of 16 IBM Power7 machines, our parallel implementation achieves on average 12.7 and 22 times speedup for the deterministic instances and the5-scenario stochastic instances, respectively. All problems are solved within an hour to near optimality including the previously unsolvable 20-scenario stochastic instances.