This roadmap explains parallel programming concepts, how parallel programming relates to high performance computing, and how to design programs that can effectively use many cores. It introduces the concepts and considerations necessary for designing, writing, and optimizing parallel programs, without delving into the specifics of particular programs.

Parallel programming is increasingly relevant for all computing platforms; most personal computers (and even cell phones) sold today include multiple processing cores and require parallel programs to yield the best performance. Though parallel programming requires more time and effort than serial programming, parallel computation is the only way to leverage the power of supercomputer installations like Frontera.

You may be motivated to develop a parallel program because the serial version of your program runs too slowly. Parallelization can spread the computation across the cores of your personal workstation, reducing run times from days to hours — or it can spread your computation across the nodes of a supercomputer, reducing the runtime from years to hours.

Another reason you might parallelize your code is to use more memory than is available on a single machine. Parallel processing allows programs to use the massive amounts of memory available on supercomputing clusters.

But Frontera's most important feature is that its compute nodes have 56 or 112 cores for concurrent computation. By leveraging parallel computation on a machine like Frontera, a person can complete many months' worth of serial computation in days.