Additional analytical perspective
Another perspective on why we need more computing power, goes as follows: let us supposed we want to predict the weather over an area covering the United States, and Canada. We cover the region with a cubical grid that extends 20 km over sea level, and examine the weather at each vertex of the grid. Now, suppose we use a cubic component of the grid that is 0.1 kilometer on the sides, and since the area of the United States, and Canada is approximately 20 million square kilometers. We therefore would need at least
let us also assume we need a minimum of 100 calculations to ascertain the weather condition at a grid point, then the weather, one hour hence, will require 
calculations. Now, to predict the weather hourly for the next 48 hours, we will need 
. Assuming our serial computer can process
(one billion) calculations per second, it will take approximately 
. Clearly, this is not going to work to our benefit if the computer is going to predict the weather at a grid point in 23 days. Now, suppose we can flip a switch that turbocharges our computer to perform 
(1 trillion) calculations per second; it would now take approximately half an hour to determine the weather at a grid point, and the weather guy can now make a complete prediction in the next 48 hours.
Outside of weather prediction, there are numerous other conditions/events occurring in the real world/universe that require a higher rate of calculation. This would require our computer to have a much higher processing speed in order to solve a given problem in a reasonable time. It is this nettling fact that is driving engineers and scientist to pursue higher and higher computational power. This is where parallel computing/supercomputing, with its associated MPI codes, excels. However, obstacles to widespread adaption of parallelism abound, courtesy of the usual suspects: hardware, algorithms, and software.
With regards to hardware, the network intercommunication pathway, aka switches, are falling behind the technology of the modern processor, in terms of communication speed. Slow switches negatively impact the theoretical upper computational speed limit of a supercomputer. You will observe this phenomenon when running your Pi supercomputer, as you bring successive nodes online that transition the input ports on the HP switch. Switch technology is improving, though not fast enough.
Processing speed is dependent on how fast a parallel code executes on the hardware, hence software engineers are designing faster and more efficient parallel algorithms that will boost the speed of supercomputing. Faster algorithms will boost the popularity of parallelism.
Finally, the most impactful obstacle to widespread adoption of parallelism is inadequate software. To date, compilers that can automatically parallelize sequential algorithms are limited in their applicability, and programmers are resigned to providing their own parallel algorithm.
