Without deviation from the norm, progress is not possible.
― Frank Zappa
There is something seriously off about working on scientific computing today. Once upon a time it felt like working in the future where the technology and the work was amazingly advanced and forward-looking. Over the past decade this feeling has changed dramatically. Working in scientific computing is starting to feel worn-out, old and backwards. It has lost a lot of its sheen and it’s no longer sexy and fresh. If I look back 10 years everything we then had was top of the line and right at the “bleeding” edge. Now we seem to be living in the past, the current advances driving computing are absent from our work lives. We are slaving away in a totally reactive mode. Scientific computing is staid, immobile and static, where modern computing is dynamic, mobile and adaptive. If I want to step into the modern world, now I have to leave work. Work is a glimpse into the past instead of a window to the future. It is not simply the technology, but the management systems that come along with our approach. We are being left behind, and our leadership seems oblivious to the problem.
For most of the history of computing in the 20th and into the 21st Century, scientific computing was at the forefront of technology. That is starting to change. Even today scientific computing remains exotic in terms of hardware and some aspects of software, but it also feels antiquated and antique. We get to use cutting edge computer chips and networking hardware that demand we live on the ragged edge technologically. This is only half the story. We also remain firmly entrenched in the “mainframe” era with corporate computing divisions that seem more “Mad Men” and less “Star Trek” than ever. The distance between the computers we use to execute our leading edge scientific investigations and our offices or our personal lives are diverging at warp speed. It has become hopelessly ironic in many ways. Worse than ironic, the current state of things is unhealthy and lessens the impact of scientific computing on today’s World.
Even worse than the irony is the price this approach is exacting on scientific computing. For example, the computing industry used to beat a path to scientific computing’s door, and now we have to basically bribe the industry to pay attention to us. A fair accounting of the role of government in computing is some combination of being a purely niche market, and partially pork barrel spending. Scientific computing used to be a driving force in the industry, and now lies as a cul-de-sac, or even pocket universe, divorced from the day-to-day reality of computing. Scientific computing is now a tiny and unimportant market to an industry that dominates the modern World. In the process, scientific computing has allowed itself to become disconnected from modernity, and hopelessly imbalanced. Rather than leverage the modern World and its technological wonders many of which are grounded in information science, it resists and fails to make best use of the opportunity. It robs scientific computing of impact in the broader World, and diminishes the draw of new talent to the field.
It would be great to elaborate on the nature of the opportunities, and the cost of the present imbalances. If one looks at the modern computing industry and its ascension to the top of the economic food chain, two things come to mind: mobile computing – cell phones – and the Internet. Mobile computing made connectivity and access ubiquitous with massive penetration into our lives. Networks and apps began to create new social connections in the real world and lubricated communications between people in a myriad of ways. The Internet became both a huge information repository, and commerce. but also an engine of social connection. In short order, the adoption and use of the internet and computing in the broader human World overtook and surpassed the use by scientists and business. Where once scientists used and knew computers better than anyone, now the World is full of people for whom computing is far more important than for science. Science once were in the lead, and now they are behind. Worse yet, science is not adapting to this new reality.
Those who do not move, do not notice their chains.
― Rosa Luxemburg
The core of the problem with scientific computing is its failure to adapt and take advantage of the opportunity defined by this ascendency of computing. A core of science’s issue with computing is the lost sense that computers are merely a tool. Computers are a tool that may be used to do science. Instead of following this maxim, we simply focus on the older antiquated model of scientific computing firmly grounded in the mainframe era. Our mindset has not evolved with the rest of the World. One of the clear consequences of the mindset is a creeping degree of gluttony and intellectual laziness with high performance computing. All problems reduce to simply creating faster computers and making problems submit to the raw power of virtually limitless computations. We have lost sight of the lack of efficiency of this approach. A renewed focus on issues of modeling, methods and algorithms could be deeply enlivened by the constraints imposed by limited computing resources. Moreover, the benefits of solving problems more efficiently with smaller computing resources would yield innumerable benefits in the setting of big iron. This could be achieved without the very real limitations of having big iron be the sole focus of our efforts.
Scientific computing could be arranged to leverage the technology that is advancing the World today. We could look at a mobile, adaptive platform for modeling, simulation and data analysis that harnessed the best of technology. We could move through the cloud using technology in an adaptive, multiscale manner. One of the biggest challenges is letting go of the power dynamic that drives thinking today. Scientific computing has been addicted to Moore’s law for too long. The current exascale push is symptomatic of this addiction. Like any addiction it is unhealthy and causes the subject to avoid real cures for their problem. We see progress as equivalent to raw power with a single computer. The huge stunt calculation as a vehicle for science is a manifestation of this addiction. Science is done with many calculations along with an adaptive examination of problems or mindful interrogation of results. Power can also be achieved through mobility, ubiquity and flexibility. The big iron we pursue has become tantamount to progress because it’s the only route we can envision. The problem is that technology, and the arc of progress is working against us instead of with us. It is past time to change our vision of what the future can be. The future needs to be different by embracing a different technological path. On one hand, we won’t be swimming against the current of computing technology, but on the other hand we will need to invest in different solutions to make it work.
Flexibility is an art of creating way outs within the cul-de-sacs!
― Mehmet Murat ildan
Mobility is power, and it has made computing ubiquitous. When the broader computing industry embraced the death of Moore’s law, it switched its attention to cell phones. Instead of simply being phones, they became mobile computers and mobile extensions of the Internet. In doing so we unleashed a torrent of creativity and connection. All of a sudden, we saw computers enable the level of social connection that the Internet always had promised, but never delivered. The mobile computing revolution has reshaped the World in a decade. In the process, the mobile market overwhelmed the entire computing industry and created economic dominance on an unparalleled scale. The killer piece of technology was the iPhone. It combined a focus on user interface along with software that enabled everything. We also need to recognize that each phone is more powerful than the fastest computer in the World 25 years ago. We have tremendous power at our fingertips.
One of the really clear messages of the recent era in computing is a change in the nature of value and power. For a long time, power was measured by hardware gains in speed, memory and capability, but now application innovation and flexibility rule. Hardware is largely a fixed and slowly changing commodity and represents a level playing field. The software in the applications and the user interface are far more important. Algorithms that direct information and attention are dominating the success in computing. Providing the basis of connection and adaption to the needs of the users has become the medium for creating new markets. At the same time these algorithms have come under fire for how they manipulate people and data. These mobile computers have become a massive issue for society as a whole. We are creating brand new social problems and side-effects we need to effectively solve. The impact of this revolution in computing on society as a whole has been incredible.
A whole cadre of experts is fading from the field of play in computing. In taking the tact of focusing on mainframe computing, scientific computing is sidelining itself. Instead of this enormously talented group of people playing in the area that means the most to society, they are focused on a cul-de-sac grounded in old and outdated models of success. Our society would benefit by engaging these experts in making mobile computing more effective in delivering value in new innovative ways. We could be contributing to solving some of the greatest problems facing us rather than seeing our computing as a special niche serving a relatively small segment of society’s needs. In the past, scientific computing has provided innovative and dynamic solutions that ultimately made their way into the general computing. A perfect example is Google. The problem that Google solved is firmly grounded in scientific computing and applied mathematics. It is easy to see how massive the impact of this solution is. Today we in scientific computing are getting further and further from relevance to society. This niche does scientific computing little good because it is swimming against a tide that is more like a tsunami. The result is a horribly expensive and marginally effective effort that will fail needlessly where it has the potential to provide phenomenal value.
You never change things by fighting the existing reality.
To change something, build a new model that makes the existing model obsolete.
― R. Buckminster Fuller
We are long passed the time to make a change in scientific computing’s direction and strategy. Almost everywhere else the mainframe era died decades ago. Why is scientific computing tied to this model? Why are scientists resisting the conclusions so nakedly obvious? In today’s risk, adverse environment making a change to the underlying model of this branch of science is virtually impossible. Even when the change is dramatically needed and overdue by years the resistance is strong. The status quo is safe and firmly entrenched. In a time when success can be simply asserted and largely manufactured, this unacceptable state of affairs will persist far longer than it should. Sooner or later someone will take the plunge, and success will follow them. They will have the winds of progress at their backs solving most of the problems easily that we throw billions of dollars at with meager success.
The measure of intelligence is the ability to change.
― Albert Einstein