In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as random-access machine. Similarly, many computer science researchers have used a so-called parallel random-access machine (PRAM) as a parallel abstract machine (shared-memory). Many parallel algorithms are executed concurrently – though in general concurrent algorithms are a distinct concept – and thus these concepts are often conflated, with which aspect of an algorithm is parallel and which is concurrent not being clearly distinguished. Further, non-parallel, non-concurrent algorithms are often referred to as "sequential algorithms", by contrast with concurrent algorithms. Analysis of parallel algorithms Algorithms vary significantly in how parallelizable they are, ranging from easily parallelizable to completely unparallelizable. Further, a given problem may accommodate different algorithms, which may be more or less parallelizable. Some problems are easy to divide up into pieces in this way – these are called embarrassingly parallel problems. Examples include many algorithms to solve Rubik's Cubes and find values which result in a given hash. Some problems cannot be split up into parallel portions, as they require the results from a preceding step to effectively carry on with the next step – these are called s. Examples include iterative numerical methods, such as Newton's method, iterative solutions to the three-body problem, and most of the available algorithms to compute pi (π). Some sequential algorithms can be converted into parallel algorithms using automatic parallelization. Parallel algorithms on individual devices have become more common since the early 2000s because of substantial improvements in multiprocessing systems and the rise of multi-core processors.