Large and/or computationally expensive optimization problems sometimes require parallel or high-performance computing systems to achieve reasonable running times. This chapter gives an introduction to parallel computing for those familiar with serial optimization. We present techniques to assist the porting of serial optimization codes to parallel systems and discuss more fundamentally parallel approaches to optimization. We survey the state-of-the-art in distributedand shared-memory architectures and give an overview of the programming models appropriate for efficient algorithms on these platforms. As concrete examples, we discuss the design of parallel branch-and-bound algorithms for mixed-integer programming on a distributed-memory system, quadratic assignment problem on a grid architecture, and maximum parsimony in evolutionary trees on a sharedmemory system.