The need for greater processing speed

The world/universe is an immensely parallel entity, wherein numerous complex, interconnected events are occurring simultaneously, and within a temporal sequence. Serially analyzing the physics/mechanics of these events would indeed take a very long time. On the other hand, parallel processing is highly suited for modeling and/or simulating these types of phenomena, such as the physics of nuclear weapons testing and galaxy formation mentioned earlier. Depicted here are additional events that lend themselves to parallel processing:

We have already discussed, in general, some of the reasons for employing parallel computing. Now we will discuss the main reasons. Let's start with saving money and time. It is, by and large, a truism that applying more resources to a task tends to accelerate its completion. The technique of parallel processing does indeed buttress this notion by applying multiple nodes/cores to the problem at hand - many hands make light work, to reprise an old adage. Additionally, parallel machines can be constructed from relatively cheap components.

Next up is the issue of needing to solve large, difficult, and complicated problems. Some problems are so enormously complicated and seemingly intractable that it would be foolhardy to attempt a solution with the processing power of a single computer, considering the computer's limited memory capacity. Some of the grand challenges for supercomputing can be found at this link:https://en.wikipedia.org/wiki/Grand_Challenges. These challenges require petaflops and petabytes of storage capacity. Search engines such as Google, Yahoo, and so on employ supercomputers to aid with processing millions of Internet searches and transactions occurring every second.

Concurrency produces greater processing speed. A lone computer processes one task at a time. However, by networking several lone computers, several tasks can be performed simultaneously. An example of this ethos is Collaborative Network, which offers a global virtual space for people to meet and work.

Next, non-local resources can play an important role in parallel processing. For example, when compute resources are inadequate, one can leverage the capacity of a wide area network, or the Internet. Another example of distributive computing is SETI@home, http://setiathome.berkeley.edu/. The network has millions of users worldwide, who provide their computer resources to help in the Search for Extraterrestrial Intelligence (SETI). Incidentally, the author has been a member of SETI for several years running. Whenever he is not using his PC, it automatically lends its processing power to the organization. Another parallel computing organization is Folding@home, http://folding.stanford.edu/. Members provide their PC processor resource to help with finding a cure for Alzheimer's, Huntington's, Parkinson's, and many cancers.

Another, and final, reason for needing parallel computing is to effectively leverage the underlying parallel hardware for improved performance. The current crop of PCs, including laptops, house processors that have multiple cores that are configured in a parallel fashion. Parallel software, such as OpenMPI, is uniquely designed for processors with multiple cores, threads, and so on. You will be using OpenMPI later during the testing and running of your Raspberry Pi supercomputer. Running serial programs on your PC is essentially a waste of its computing capability. The following figure depicts a modern processor architecture by Intel:

Intel Xenon processor with six cores and six L3 cache units

What does the future hold? You might ask. Well, historical trends dictate that even more powerful supercomputers will emerge in the future - a trend that is guaranteed to continue - as chip-making technology unceasingly improves. In addition, faster network switches are evolving somewhat concurrently with the chips/processors. Switches are used for interconnecting the nodes in the supercomputer. The theoretical upper limit of processing speed on a supercomputer is impacted not only by the communication speed between the cores in the chip, but also by the communication speed between the nodes, and the network switch connecting the nodes. Faster network switching translates to faster processing. Parallelism as a concept is here to stay, and the associated technology is getting better and stronger as we go forward. The following figure depicts the supercomputing performance trend. The race for exaflop computing continues unabated:

Supercomputing performance trend

The question you might be asking now is, who is using this awesome technology? Well, the short answer is tons of organizations and people. The group that are firmly on this list are scientist and engineers. Among the problem areas being studied/modeled by these eggheads are the following:

• Environment, earth, and atmosphere
• Applied physics: nuclear, particle, condensed matter, high pressure, fusion, and photonics
• Genetics, bioscience, and biotechnology
• Molecular science and geology
• Mechanical engineering
• Microelectronics, circuit design, and electrical engineering
• Mathematics and computer science
• Weapons and defense

The preceding list represents a few additional example applications of parallel computing. The following figure depicts additional applications of parallel processing in science and engineering:

Images of engineering and scientific problems being modeled by supercomputers

The industrial sector is also a big user of supercomputing technology. Companies process huge amounts of data to improve product yield and efficiency. Some examples of this activity are as follows:

• Data mining, databases, big data
• Oil exploration
• Web-based business services and web search engines
• Medical diagnosis and medical imaging
• Pharmaceutical design
• Economic and financial modeling
• Management of national and multinational corporations
• Advanced graphics and virtual reality
• Multimedia technologies and networked video
• Collaborative work environments

The following figure depicts a few additional examples of industrial and commercial applications:

Images of commercial application of supercomputers

Globally, parallel computing is used in a wide variety of sectors; see the following graph:

Worldwide use of supercomputing resources