acadien

This concept of "drop everything and study only the most 'important' areas" is misguided. Research doesn't work that way.

There are already an enormous number of talent scientists studying climate change, how would putting nuclear battery scientists on this project help? Are climate scientists short on resources? Better yet are they short on resources because NASA wants to produce nuclear batteries.

Finally there is the concept of cross pollination and random discovery. For example, CERN scientists invented the modern cell phone touch screen, super conductivity was discovered by accident, methods developed by materials scientists to study liquids is used in robotics. You kill off this kind of creativity and random advancement as you narrow the scope of scientists.

I feel like these are special cased, searching for: graph looks like <object> with any random object turns up junk.

Edit: then again it does work with Obama but not with Richard Nixon.

Yeah I suspected it was a typo or something, but I kind of like it...

I agree, as others have said the dynamics of a barge is a necessary challenge to prove the technology. But they need some more positive press, maybe a gif that doesn't end in an explosion hehe.

On a side note the colloquialism 'success on their pelt' is totally new to me, I've always heard it as 'success under their belt'.

Musk commented himself on twitter something to the effect that the explosion is from the pressure difference of the fuel canister when it is breached. Also the fuel mostly burned at high heat and didn't explode per say.

The author says multiple times: "an acceleration of [X] km/s is required", maybe they meant to imply that the magnitude of the change of the velocity?

It draws some doubt that the author lists accelerations in units of km/sec, not km/sec^2.

There are big data people running on supercomputers too. I know there are people writing custom asynch job managers to handle big data type problems because the top supercomputers have low memory latency.

Also I think the dichotomy you're looking for is IO bound vs CPU bound problems. Although certainly there are a plethora of different kinds of IO bound problems (asynch vs synch or disk bound vs memory bound vs cache bound).

Think of it as loops in 5 dimensions (x,y,z,a,b). I believe each node connects to 10 different neighboring nodes (2 in each of 5 dimensions), although I could be mistaken on that. You can actually tune which direction you prefer the nodes to communicate over by passing certain flags when you submit a job.

Also here's an image... which I admit is not terribly useful, but its what the national lab people put out. https://computing.llnl.gov/tutorials/bgq/images/5Dtorus.400p...

Yeah the 5 dimensional torus network used at MIRA is just too cool not to bring up here. Modern supercomputers are increasingly become less and less discrete.

https://computing.llnl.gov/tutorials/bgq/

Again I'm not sure if I agree or disagree with this. My hatred of MPI is only outweighed by the fact that I can use it... and my code works.

I think a large part of the inertia behind MPI is legacy code. Often the most complex part of HPC scientific codes is the parallel portion and the abstractions required to perform them (halo decomposition etc). I can't imagine there are too many grad students out there who are eager to re-write a scientific code in a new language that is unproven and requires developing a skill set that is not yet useful in industry (who in industry has ever heard of Chapel or Spark??). Not to mention that re-writing legacy codes means you're delaying from getting results. Its just a terrible situation to be in.

Yeah good point. I just felt it might be a misleading of the author to suggest Chapel as an alternative when you cannot possibly write a useful program with it.

I've heard there is a new effort being led by Torsten Hoefler to modernize MPI and address a number of the issues mentioned in this article.

http://htor.inf.ethz.ch/

I was at a talk of his last year and there are a number of fault tolerant MP algorithms being drawn in. MPI hasn't been updated in ages, I don't think that necessarily means we need to ditch it, it just means the standard needs to be modernized. I don't feel very strongly about this since working with MPI is a huge pain in the ass and it seems like the challenge of modernizing it is just gargantuan.

Also I'm not familiar with spark, but isn't Chapel a decade old at this point and barely works at all? I tried their compiler last summer and it took 5 minutes to compile hello world, hopefully its improving.

In particle simulations interactions can happen at a distance, so at every time step all particles need to be checked with all other nearby particles to get their distances and compute the force of their interaction. It looks like your simulation is a hard-sphere type interaction where you're only checking to see if two particles bump into each other and is considerably faster to compute.

Yeah that's what I'm saying, if this demo has been 80,000 interacting particles it would be extremely impressive.

Particles can be misleading too because if they're simply propagated by some PDE on a regular mesh (or even no mesh) it is an embarrassingly parallel problem. But if they're interacting particles then you need neighbor lists and predictor corrector methods to propagate them which is considerably more difficult to implement and parallelize.

If only our government taxed people appropriately and had a system for foreign aid that was well studied and effective. Is whats proposed in this article as effective or better than the alternative? I have no idea but it certainly seems less organized.

It is a phase transition, it takes energy to evaporate because of the change in entropy/volume (from Clausius Clapeyron).

Some of the comments made there are pretty interesting, specifically that it's not about the use of water its about the waste.

-Ground water can be at least partially recouped through wells. This would be a really interesting number to look up.

-Sewage water is wasted as it is dumped into the sea.

Anything else? Where does water leave the cycle?

1 hamburger or 1 cow? Genuinely curious, also could you source that? Thanks!

All the runners came in 2nd to last, I guess.

How much design and algorithm design did you do in simulation as opposed to using the physical robot itself?

>The success the U.S. has had in being innovating in STEM fields is due to our cultural tradition of comprehensive education rooted in the liberal arts.

There is no evidence for this.

>A focus only on STEM is what gets you modern-day China, Korea, and Japan, where there are many technically skilled workers but a lower inclination to be innovative.

More stereotyping with no evidence. As someone who was worked with many Chinese, Koreans and Japanese as a scientist this statement is just outright ridiculous. Guess what, many of the top creative scientists in America are educated in the very countries you deride.

Its about an educated and critical thinking public. A public that approaches everything with a critical eye is less susceptible to political pandering and more accepting of good science and evidence/data based laws. The average person doesn't know how to interpret a bar graph and has no clue what a good source of data looks like. Agreeing on a specific point, such as evolution, is just a symptom of the larger problem.

Yes, I remember friends having that problem in undergrad in Canada. There was a way around it but it was an application process with an interview. I believe they were concerned with people trying to game the system to get lower tuition fees.

Why not just make it a graduated tuition based on parental income?

I'm guessing they just took the figures from the Chinese press release and turned it into an article. Lazy journalism at its best.

You get a range of skills while working on a science PhD that will benefit you in industry no matter where you end up. If you choose to study an area that actually has industry applications then its even better.

If, on the other hand, you get a science PhD and spend all of your time working with some insanely complex device that only has 1 application studying an esoteric process that has no industry applications... then yeah you're going to have a hard time finding work. This is the story I have seen over and over again among my friends and colleagues that get PhDs.

If you're a graduate student be smart about it and make sure you get the experience necessary to help you find work after graduation! No one else is going to help you with that, it is up to you to build your resume.

Professors that take advantage of cheap labor and don't help their students transition after graduation are a problem that Universities should take up. It is to the University's benefit to get their students into nice industry positions.

Just because someone doesn't understand how a jet engine works doesn't mean its incomprehensible to them. It just means they haven't spent the time and effort to understand it. There are very very few topics out there in this world that you cannot understand if you've had the proper training.

Granted a lot of topics may take a couple of years to understand, or even a decade. But guess what, it took the author that long to understand it too.

>99.9%+ of all papers published by Math professors are incomprehensible to all but a few experts.

That's just flat out not true. In fact I would hazard a guess that the reverse is true (0.1% of math papers are incomprehensible to most). Not to say that math is simple but that just because something is highly specialized does not mean it is incomprehensible. Of course my only evidence is that the vast majority of specialized physics papers that I read only require some background in quantum mechanics, statistical mechanics and some of the problem domain. I read some papers in math journals too and feel like they're about on par in general.

There are certainly some mathematicians that publish in super highly specialized fields that are remarkably difficult to understand but these are a teensy tiny minority.