The prefix super in the moniker supercomputer may, to the ill-informed, conjure up images of the electronic supervillain HAL 9000 in Arthur C. Clarke's classic movie 2001: A Space Odyssey or the benevolent superhero Clark Kent, aka Superman, who inhabits the DC comic universe. However, the tag supercomputer is, in fact, associated with a non-fictional entity that has no ill-will towards humans - at least not at this moment in time. Supercomputing and supercomputers are the subject matter of this book.

In this chapter, we introduce the reader to the basics of supercomputing, or more precisely, parallel processing. Parallel processing is a computational technique that is currently enjoying widespread usage at many research institutions, including universities and government laboratories, where machines routinely carry out computation in the teraflops (1 billion floating point operations per second) and petaflops (1,000 teraflops) domain. Parallel processing significantly reduces the time needed to analyze and/or solve complex and difficult mathematical and scientific problems, such as weather prediction, where a daunting myriad of physical atmospheric conditions must be considered and processed simultaneously in order to obtain generally accurate weather forecasting.

Supercomputing is also employed in analyzing the extreme physical conditions extant at the origin of the much-discussed Big Bang event, in studying the dynamics of galaxy formation, and in simulating and analyzing the complex physics of atomic and thermonuclear explosions - data that is crucial to the military for maintaining and designing even more powerful weapons of mass destruction - holy crap!! This doesn't bode well for humanity. Anyway, these are just a few examples of tasks germane to parallel processing. Following are images of events that are being studied/simulated by scientist employing supercomputers:

The enhanced processing speed, so central to parallel computing, is achieved by assigning chunks of data to different processors in a computer network, where the processors then concurrently execute similar, specifically designed code logic on their share of the data. The partial solution from each processor is then gathered to produce a final result. You will indeed be exploring this technique later when you run example codes on your PC, and then on your Pi2 or Pi3 supercomputer.

The computational time compression associated with parallel computing is typically on the order of a few minutes or hours, rather than weeks, months, or years. The sharing of tasks among processors is facilitated by a communication protocol for programming parallel computers called Message Passing Interface (MPI). The MPI standard, which came to fruition between the 1980s and early 1990s, was finally ratified in 2012 by the MPI Forum, which has over 40 participating organizations.

You can visit https://computing.llnl.gov/tutorials/mpi/ for a brief history and tutorial on MPI, and https://computing.llnl.gov/tutorials/parallel_comp/ for parallel computing. You can also visit http://mpitutorial.com/tutorials/ for additional information and a tutorial on MPI programming.

In this chapter, you will learn about the following topics: