The real zombie-apocalypse is the pandemic of drama and mediocrity.
― Bryant McGill
A while back I referred to supercomputing as being a zombie
(https://williamjrider.wordpress.com/2014/10/06/supercomputing-is-a-zombie/). All my experience in the past few months has led me to reconsider this line of thinking, I was wrong. It is much worse than I had ever anticipated. We are continuing to favor computing hardware over more innovative problem solving despite the end of Moore’s law being upon us. The cost is wasted money and significant under-utilization of computing’s benefits.
This morning I awoke thinking about the same thing again, and realized that I missed part of the analogy, not only is the supercomputing emphasis brainless, but it eats our brains too just like a zombie would do. It is rotting the thought out of computational science. The present trends in high performance computing are actually offensively belittling toward the 
degree to which human ingenuity plays a roll in progress. The program that funds a lot of what I work on, the ASC program, is twenty years old. It was part of a larger American effort toward science based stockpile stewardship envisioned to provide confidence in nuclear weapons when they aren’t being tested.
Orthodoxy means not thinking–not needing to think. Orthodoxy is unconsciousness.
― George Orwell
It now is on verge of being ironic by the name science-based. Science is based on evidence and the current approach to supercomputing is not. It is a faith-based program. The faith is founded primarily on the imminently reasonable prospect that faster, bigger computers bring better solutions to computed modeling and simulation. The whole concept is based on “convergence,” which implies that the computed solution approaches the “true” solution as the amount of computational effort increases. Computational effort is typically associated with a mesh, or grid that defines how the real world is chopped up and represented on the computer.
For example, think about weather or climate modeling and how to improve it. If we model the Earth with a grid of 100 kilometers on a side (so about 25 mesh cells would describe New Mexico), we would assume that a grid of 10 kilometers on a side would be better because it now uses 2500 cells for New Mexico. The problem is that a lot else needs to change too in the model to take advantage of the finer grid such as the way clouds, wind, sunlight, plant life, and a whole bunch of things are represented. This is true much more broadly than just weather or climate, almost every single model that connects a simulation to reality needs to be significantly reworked, as the grid is refined. Right now, insufficient work is being funded to do this. This is a big reason why the benefit of the faster computers is not being realized. There’s more.
The more pieces used to represent the world, the smaller the pieces are and the greater the effort. This is the drive for bigger computers. It’s not nearly so simple, but simplicity is what Americans do best these days. It would be true if we weren’t working toward this end with one hand tied behind our backs (maybe both hands). We have to do more than just make faster computers; we have to think about what we are doing, a lot more. The power of computers needs wisdom that we sorely lack.
There is safety in numbers. And science. Clone your way to being safe. Nobody can protect you like you. And you and you and you.
― Jarod Kintz
More than better models, we can do a better job of solving the balance laws defining the models that are used to connect one grid cell with another. We solve these laws with numerical methods that produce errors in the solution. Better methods produce smaller errors, and beyond that all errors are not equal. Some errors are closer to what is physical, while other errors are decidedly unphysical. Better methods often make errors that are more physical (i.e., numerical diffusion). One of the major 
problems of the modern supercomputing is the lack of effort to improve the solution of balance laws. We need to create methods with smaller errors, and when errors are made bias them toward physical errors. There’s more.
As we use finer meshes the computer must use more data. The amount of work the computer needs to do to solve a problem is not necessarily proportional to the amount of data; sometimes (most of the time) it takes more work to solve more data than the previous amount. In other words the amount of work grows faster than the data. A typical problem we solve on the computer is the simultaneous solution of linear equations, i.e., linear algebra. The classical way of solving such a problem is Gaussian elimination where the work scales with the cube of the number of equations. Therefore a thousand times larger problem will require a billion times the work to solve.
For special sorts of linear systems associated with balance laws we can do a lot better. This has been a major part of the advance of computing, and the best we can do is for the amount of work to scale exactly like the number of equations (i.e., linearly). As the number of equations grows large the difference between the cube and the linear growth is astounding. This linear algorithm were enabled by multigrid or multilevel algorithms invented by Achi Brandt almost 40 years ago, and coming into widespread use 25 or 30 years ago.
The desire for safety stands against every great and noble enterprise.
― Tacitus
We can’t really do any better today. The efforts of the intervening three decades of supercomputing has focused on making multilevel methods work on modern parallel computers, but no improvement algorithmically. Perhaps linear is the best that can be done although I doubt this. Work with big data is spurring the development of methods that algorithmically scale at less than linear. Perhaps these ideas can improve on multigrid’s performance. The key is would be to allow inventiveness to flourish. In addition risky and speculative work would need to be encouraged instead of the safe and dull work of porting methods to new computers.
As I’ve said before risk avoidance is killing research in many field, scientific computing is no different (https://williamjrider.wordpress.com/2014/12/05/is-risk-aversion-killing-innovation/, https://williamjrider.wordpress.com/2014/03/03/we-only-fund-low-risk-research-today/, https://williamjrider.wordpress.com/2014/12/12/whats-your-backup-plan/). One sign of risk aversion is the inability to start new computer codes, the implementations of the algorithms, methods and models. We continue to work and rework old codes because of the capability they offer compared to a new code. We see these old codes as investments that we must continue to remodel. It’s time to tear them down and put up a fresh structure with new ideas instead of continually putting a fresh coat of paint on the tired old ones. 
The potential for good I’ve touched on here is the tip of the iceberg. Algorithms and models can add vastly more value to computational science than faster machines. The only issue is that we aren’t brave enough to take advantage of opportunity. That is the saddest thing about this.
Writing is thinking. To write well is to think clearly. That’s why it’s so hard.
― David McCullough
Like most Americans I speak primarily with people who are like me. In my case it those educated in a scientific or technical field, working at a Lab or University, doing research. I don’t have a lot of contact with people of a different background. I do have a handful of conservative friends, and the difference in their Worldview is both understandable as it is stunning. What is really breathtaking is the difference in what we think is true. This is attributable to where our information comes from.
In large part the Internet has allowed everyone to draw from sources of information that suits their preconceptions. To a large extent we are fed information that drives us
public.
The traditional news media is dominated by corporate interests with Fox News leading the way, but the old three ABC, CBS and NBC being no different. MSNBC is viewed as the liberal vanguard, but again it’s no different either. Once this dynamic was primarily associated with Newspapers, but as they die, it has been replaced by TV and as they begin to die by the Internet. Big money is running the show I every case, and finding a niche that provides them profit and power.
Sometimes it’s semi-innocuous such as advertising for a TV show or movie. The dangerous aspect is the continuous avoidance of issues that the big moneyed interests don’t want portrayed, discussed or explored. This lack of coverage for a class of issues associated with money and class is poisoning democracy, tilting the discussion, and ultimately serving only the short-term needs of the businesses themselves.
A more innocuous aspect is the slanting of the political dynamic, which is happening pervasively via Fox News and its use of a message firmly associated with a single political agenda. In the UK it’s Sky that does the same thing. Across the board the impact has been to turn up the heat on partisan bickering and diminish the ability of the democratic process to function. Part of the problem is that it becomes clear if you make the mistake of talking about such things, people no longer operate with the same facts. Each side of the debate simply cherry-picks facts to suit their aims and avoids facts that undermine their chosen message. As a result no one really gets the full story, and the truth is always slanted one way or another. As a result the ability to compromise and move forward on vexing problems has become impossible.
he situation today favors those who have power, and today power stems from wealth. Money is paying for information, and this information is paving the way toward an even greater accumulation of power. The Internet is not providing the democratization of knowledge, but rather giving those already in power access to unparalleled capability to issue effective propaganda. Those in power are assisted by a relatively weak government whose ability to counter their stranglehold on society is stymied by inaction.
One of the things that continually bother me the most about the changes where I work is the sunset of the individual from importance. The single person is gradually losing power to the nameless and increasingly faceless management. Increasingly everyone is viewed as a commodity, and each of us is interchangeable. Another scientist or engineer can be slotted into my place without any loss of function. My innate knowledge, experience, creativity, passion are each worthless when compared to the financial imperatives of my employer. We are encouraged if not commanded to be obedient sheep. Along the way we have lost the capability to foster deep sustained careers, the current regime encourages the opposite. The reason given for sacrificing this aspect of work is financial. No one can pay for the cost of the social construct necessary to enable this. I think that is BS, the reason is power and control. 
employer. This model was the cornerstone of the National Laboratory system. The Nation benefited greatly from the model both in terms of security and National defense, but also from the scientific and engineering greatness it engendered.
scientists during the Manhattan Project provided the catalyst to extend this model more broadly. Their achievements fueled its continued support into the late 70’s. Then it was deemed to be too expensive to maintain. The management of the Labs is choking this culture to death. If it isn’t already gone, it soon will be.
A deep part of its power was the enabling of individual achievement and independent thought. Perhaps more than the cost of the social contract, the Nation has allowed the force of conformity, lack of trust and fear of intellect to undermine this model. While the financial costs have escalated largely due to systematic mismanagement and the absence of political courage and leadership, it has been the excuse for the changes. While the details are different at the Labs the overall forces are hand-in-hand with the overall destruction of the middle class who was offered a similar social contract in the post war years. This has been replaced by cheap labor, or outsources always with the excuse about cost.
objectives actually undermined the achievement of innovation. It was a fascinating and provocative idea. I knew it would be the best thing I did all day (it was), and it keeps coming back to my thoughts. I don’t think it was the complete answer to innovation, but Professor Stanley was onto a key aspect of what is needed to innovate.
In other words, the system we have today is harmful. We are committed to continue down the current path, results be damned. We have to plan and have milestones just like business theory tells us with no failure being accepted. In fact we seem to act as if failure can simply be managed away. Instead of recognizing that failure is essential to progress, and that failure is actually healthy, we attempt to remove it from the mix. Of course, failure is especially unlikely if you don’t try to do anything difficult, and choose your objectives with the sort of mediocrity accepted today because lack of failure is greeted as success.
The courage that once described Americans during the last century has been replaced by a fear of any change. Worries about a variety of risks have spurred Americans to accept a host of horrible decreases in freedom for a modest-to-negligible increase in safety. The costs to society have been massive including the gutting of science and research vitality. Of course fear is the clearest was to pave the way for the very outcomes that you sought to avoid. We eschew risk attempting to manage the smallest detail as if that might matter. This is combined with a telling lack of trust, which implies a certain amount of deep self-reflection. People don’t trust because they are not trustworthy and project that character onto others. The combination of risk avoidance, and lack of trust produces a toxic recipe for decline and the opposite of the environment for innovation.
We collectively believe that running everything like a business is the path to success. This means applying business management principles to areas it has no business being applied to. It means applying principles that have been bad for business. Their application has destroyed entire industries in the name of short-term gains provided to a small number of shareholders. The problem is that these “principles” have been extremely good for a small cadre of people who happen to be in power. Despite the obvious damage that they do, their application widely makes perfect sense to the chief beneficiaries. It is both an utterly reasonable and completely depressing conclusion.
When returning to the theme of how to best spur innovation and its antithetical relation to objectives, I become a bit annoyed. I can’t help but believe that before we can build the conditions for innovation we must destroy the false belief that business principles are the way to manage anything. This probably means that we have to see a fundamental shift in what business principles we favor. We should trade the dictum of shareholder benefit for a broader social contract that benefits the company’s long-term health, the employees and the communities as well. Additionally, we need to recover our courage to take risks and let failure happen. We need to learn to trust each other again and stop trying to manage everything.
I’m sure that most people would take one look at the sort of things I read professionally and collectively gasp. The technical papers I read are usually greeted by questions like “do you really understand that?” Its usually a private thing, but occasionally on a plane ride, I’ll give a variety of responses based on the paper from “yeah, it actually makes sense” to “not really, this paper is terrible, but I think the work might be important.”
and becomes increasingly unapproachable to anyone else. This tendency should mark the death knell of the area, but instead the current system seems to do a great deal to encourage this pathology.
Other areas seem to be so devoid of the human element of science that the work has not contextual basis. In every case science is an intrinsically human endeavor, yet scientists often work to divorce humanity from the work. A great deal of mathematics works this way and leads to a gap in the understanding of the flow of ideas. The source and inspiration for key ideas and work is usually missing from the writing. This leads to a lack of comprehension of the creative process. A foolhardy commitment to loses history only including the technical detail in the writing. Part and participle of this problem are horrific literature reviews.
In some fields the number of citations for the work is appalling. The author ends up providing no map for the uninitiated reader to figure out what they are talking about. Again this works both to hide information and context while making the author seem smarter than they really are.
The phrase “world class” appears so often in reviews I’ve seen that it has become a cliché. It is a completely unnecessary throwaway compliment handed out like candy on Halloween. It’s become expected, hollow praise that ultimately undermines any honest critique that follows its utterance. It’s time to stop handing out this compliment unless the situation calls for it, which is almost never.
t-to-project all of them being “world class”. The first few times I heard it, I felt great. Like wow, I’m a high performing individual in a world class organization, doing world class research. I must be really great too. As time went on, I kept hearing this even if the review was a complete train wreck. The comment would come even if the content of the review were decisively mediocre.
ego massaging part of the review.” Are the organizations I work for so weak-willed and pathetic that they need this sort of garbage? Is the overly defensive and meagerly technical content of the review actually worthy of such high praise? Increasingly this oft-heard phrase has become an excuse to dismiss everything the review has to say. Mostly for the sentiment that if they think that was “world class”, these guys are a bunch a bozos. They are either stupid or dishonest, if not both. Are we paying them to give us this empty praise? How did we find people with such low standards?
This plays into the general theme of the role of bullshit in today’s society. Telling the truth is the new sin. It’s so much more acceptable to tell the lies you are intended to believe. If someone actually expressed the truth that should be said they would be treated like a pariah. This explains why we pay external reviews to come around every year and tell us that we are still “world class”. Along with the empty platitudes we get a handful of suggestions that can all be ignored because why would a “world class” organization need to improve.
One of the keys to using a backup plan effectively is that it allows your primary plan to be more aggressive. Knowing that a viable alternative is available allows a more expansive primary plan to be envisioned. Presently the sort of planning that yields a single path forward produces risk adverse objectives. This produces the state of affairs we see today. Plans are generally too short-term focused and contain relatively little risk. Having contingency plans to fall back upon would allow a much greater amount of risk to be absorbed in the primary plan.
principle by which to combine them. The principle is use the high-order when the situation is safe and won’t produce oscillations, and fall back to the low-order method when danger ensues. Now multiple methods work well enough that people think that nothing more needs to be done (I don’t agree!). The same approach has worked well in other areas, and in my opinion could be employed far more broadly.
Sports provide another way to look at adaptive approaches and planning. Some teams are exceptional in a single approach to playing the game, and fail when they come up against the perfect counter. Great teams can play multiple ways and can be effective with Plan A, Plan B, Plan C… They can attack and defend is a variety of ways. The best among them can switch between different approaches seamlessly either to adapt to an opponent, or to surprise or overwhelm an opponent. To execute well in a variety of ways requires immense effort and practice, but this is the price of excellence.
The key element in this thought process is the devotion to solve problems. The secondary element is the development of multiple solutions to problems. This requires the developer of the plans to not be over-committed to a single approach. Sometimes the biggest problem with a plan is the over-investment of those executing the plan in the single path to success.
I used to work at McDonalds a long time ago. Most people know that a Big Mac uses a secret sauce in dressing the sandwich. It looks like Thousand Island dressing, but rest assured, it is a secret sauce of some sort. Ideally, the secret sauce is the literal trademark of the sandwich, its identity and it’s a secret only known by a hallowed priesthood. Little did I know that in my chosen professional life I would be exposed to a completely different “secret sauce”.
A successful modeling and simulation code is very much the same thing; it has a trademark “secret sauce”. Usually this is the character for the code is determined by how it is made robust enough to run interesting applied problems. Someone special figured out how to take the combination of physical models, numerical methods, mesh, computer code, round-off error, input, output… and figured out how to make it all work. This isn’t usually documented well, if at all. Quite often it is more than a little embarrassing. The naïve implementation of the same method usually doesn’t quite work. This is a dark art, the work of wizards and the difference between success and failure.
The rub is that we are losing the recipes. In many places the people who developed the secret sauce are retiring and dying. They aren’t being replaced. We are losing the community knowledge of the practices that lead to success. We may be in for a rude awakening because these aspects of modeling and simulation are underappreciated, undocumented and generally ignored. Sometimes the secret to make the code work is sort-to-very embarrassing.
Conservatives are not necessarily stupid, but most stupid people are conservatives…
keep-out-of-partisan-politics-1.16473
to allow that all those who conduct themselves as worthy members of the community are equally entitled to the protections of civil government. I hope ever to see America among the foremost nations of justice and liberality.
The assault on science and reason by conservatives is seemingly endless. Leading the charge is the denial of climate change and the threat it poses to humanity. The reasoning for the denial is two-fold, the risk action on climate would impart to the ruling corporate class, and the conservatives love of their wasteful, destructive lifestyles (which largely fuel profit to the corporate overlords). Further attacks occur within their embrace of fundamental religious faction’s desire to indoctrinate our 
children with their myths in place of science (i.e., creationism). In other venues the conservatives attack science that goes against corporate greed be it environmental science, medicine and especially the social sciences. Conservatives deny the underlying biological basis of homosexuality because of its implications for their religious beliefs. Time and time again it is their commitment to traditional religious belief over science and reason that drives a wedge.
The attacks on science and reason are by no means completely one-sided. Both liberals and conservatives fail to repudiate the various science deniers and neo-Luddite factions in their midst. For instance liberal anti-science can be see with anti-vaccine, anti-GMO and anti-nuclear movements. Each of these is based on fear of technology and is fundamentally irrational. For instance, the coupling of liberal environmental leanings and anti-nuclear mindsets undermines support for action on climate change 
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The Regularized Singularity 