Recommender systems are ubiquitous; they are foundational to a wide variety of industries ranging from media companies such as Netflix to e-commerce companies such as Amazon. As recommender systems continue to permeate the marketplace, we observe two major shifts which must be addressed. First, the amount of data used to provide quality recommendations grows at an unprecedented rate. Secondly, modern computer architectures display great processing capabilities that significantly outpace memory speeds. These two trend shifts must be taken into account in order to design recommendation systems that can efficiently handle the amount of available data by distributing computations in order to take advantage of modern parallel architectures. In this chapter, we present ways to scale popular collaborative recommendation methods via parallel computing.