We all die. The goal isn’t to live forever, the goal is to create something that will.
― Chuck Palahniuk
When we achieve a modicum of success professionally it usually stems from a large degree of expertise or achievement in a fairly narrow realm. At the same time this expertise or achievement has a price; it was gained through a great degree of focus, luck and specialization. Over time this causes a lack of perspective for the importance of your profession in the broader world. It is often difficult to understand why others can’t see the intrinsic value in what you’re doing. There is a good reason for this, you have probably lost the reason why what you do is valuable.
Ultimately, the value of an activity is measured in terms of its impact in the broader world. Often times these days economic activity is used to imply value fairly directly. This isn’t perfect by any means, but useful nonetheless. For some areas of necessary achievement this can be a jarring realization, but a vital one. Many monumental achievements actually have distinctly little value in reality, or the value comes far after the discovery. In many cases the discoverer lacks the perspective or skill to translate the work into practical value. Some of these are necessary to achieve things of greater value. Achieving the necessary balance in these cases is quite difficult, and rarely, if ever achieved.
It’s always important to keep the most important things in mind, and along with quality, the value of the work is always a top priority. In thinking about computing, the place where the computers change how reality is engaged is where value resides. Computer’s original uses were confined to business, science and engineering. Historically, computers were mostly the purview of the business operations such as accounting, payroll and personnel management. They were important, but not very important. People could easily go through life without ever encountering a computer and their impact was indirect.
As computing was democratized via the personal computer, the decentralization of access to computer power allowed it to grow to an unprecedented scale, but an even greater transformation laid ahead. Even this change made an enormous impact because people almost invariably had direct contact with computers. The functions that were once centralized were at the fingertips of the masses. At the same time the scope of computer’s impact on people’s lives began to grow. More and more of people’s daily activities were being modified by what computing did. This coincided with the reign of Moore’s law and its massive growth in the power and/or the decrease in the cost of computing capability. Now computing has become the most dominant force in the World’s economy.
Why? It wasn’t Moore’s law although it helped. The reason was simply that computing began to matter to everyone in a deep, visceral way.
Nothing is more damaging to a new truth than an old error.
— Johann Wolfgang von Goethe
The combination of the Internet with telecommunications and super-portable personal computers allowed computing to obtain massive value in people’s lives. The combination of ubiquity and applicability to the day-to-day life made computing’s valuable. The value came from defining a set of applications that impact people’s lives directly and always within arm’s reach. Once these computers became the principle vehicle of communication and the way to get directions, find a place to eat, catch up with old friends, and answer almost any question at will, the money started flow. The key to the explosion of value wasn’t the way the applications were written, or coded or run on computers, it was their impact on our lives. The way the applications work, their implementation in computer code, or the computers themselves just needed to be adequate. Their characteristics had very little to do with the success.
It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.
― Richard P. Feynman
Scientific computing is no different; the true value lies in its impact on reality. How can it impact our lives, the products we have or the decisions we make. The impact of climate modeling is found in its influence on policy, politics and various economic factors. Computational fluid dynamics can impact a wide range of products through better engineering. Other computer simulation and modeling disciplines can impact the military choices, or provide decision makers with ideas about consequences for actions. In every case the ability of these things to influence reality is predicated on a model of reality. If the model is flawed, the advice is flawed. If the model is good, the advice is good. No amount of algorithmic efficiency, software professionalism or raw computer power can save a bad model from itself. When a model is good the solution algorithms and methods found in computer code, and running on computers enable its outcomes. Each of these activities needs to be competently and professionally executed. Each of these activities adds value, but without the path to reality and utility its value is at risk.
Despite this bulletproof assertion about the core of value in scientific computing, the amount of effort focusing on improving modeling is scant. Our current scientific computing program is predicated on the proposition that the modeling is good enough already. It is not. If the scientific process were working, our models would be improving from feedback. Instead they are stagnant and the entire enterprise is focused almost exclusively on computer hardware. The false proposition is that the computers simply need to get faster and the reality will yield to modeling and simulation.
So we have a national program that is focused on the least valuable thing in the process, and ignores the most valuable piece. What is the likely outcome? Failure, or worse than that abject failure. The most stunning thing about the entire program is the focus is absolutely orthogonal to the value of the activities. Software is the next largest focus after hardware. Methods and algorithms are the next highest focus. If one breaks out this area of work into its two pieces, the new-breakthroughs or the computational implementation work, the trend continues. The less valuable implementation work has the lion’s share of the focus, while the groundbreaking type of algorithmic work is virtually absent. Finally, modeling is nearly a complete absentee. No wonder the application case for exascale computing is so pathetically lacking
It is sometimes an appropriate response to reality to go insane.
― Philip K. Dick
Alas, we are going down this road whether it is a good idea or not. Ultimately this is a complete failure of the scientific leadership of our nation. No one has taken the time or effort to think this shit through. As a result the program will not be worth a shit. You’ve been warned.
The difference between genius and stupidity is; genius has its limits.
― Alexandre Dumas-fils