Has some fairly good topics, but I'm surprised it is lacking some of the basics you normally find: Numerical integration and optimization algorithms.
Root-finding/optimizing is something many people do/need.
Introduction to High-Performance Scientific Computing
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Re: Introduction to High-Performance Scientific Computing
#32Earlier quoted context omitted.
I never got into high performance scientific computing, but I believe the stuff that was done in my department at university was all MPI based and required very high interconnect speeds (like with Infiniband). It looks like your offering is much more standard, what's the thinking there, or am I just wrong/out of date?
An important secret in HPC is that MPI is rarely required to achieve your objectives. In many ways, vendors just use MPI as a way to sell expensive systems. If you can find any way to make your system scale using threads on a single machine, or use non-latency-sensitive networking, do so.
Or rent a bigger AWS/EC2 instance to prepare for the eventual demise of old school HPC
Re: Introduction to High-Performance Scientific Computing
#33This site is going down the drain. More and more like Slashdot and others. I'll only post useless comments any more, anything else is a waste of effort. The only way to achieve change is to make things WORSE. New mission: I'll slowly help lower the quality of this "discussion" with two many angry know-nothing teenagers even more.
With an attitude like that, are you surprised? How about trying to bring it back up or offer some constructive criticism? Alternatively, you can just leave ...
And take his/her valuable contributions elsewhere? Y combinator will surely collapse like a house of cards /s
Re: Introduction to High-Performance Scientific Computing
#34Has some fairly good topics, but I'm surprised it is lacking some of the basics you normally find: Numerical integration and optimization algorithms. Root-finding/optimizing is something many people do/need.
With "HPC" you usually mean "cluster computing". The problems you mention rarely go beyond what a laptop can do in seconds. So I am not sure if they qualify as HPC? Just "numerics", which is more of a prerequisite you might learn before HPC.
Also, compared to that curriculum, the topics you mention have robust methods and stable libraries? So you can use what somebody else did. It is more likely that you need to know the gritty details if you solve PDEs, vs solving ODEs.
Re: Introduction to High-Performance Scientific Computing
#35Looks like a good collection of important topics. The tutorials feel a bit 2005 to me. GNUplot and svn? In my scientific universe it's all matplotlib and git these days. Maybe I'm unique.
Gnuplot is more customizable than matplotlib, more powerful, fastar and handles larger datasets, exports seamlessly to images, LaTeX, PDF, HTML5, animated gifs, among others, etc. It's a better tool than matplotlib, for me at least.
That it takes more effort to produce a "beautiful" output is myth, in my experience. People used to say that in large part because up to gnuplot 4 the default colors were ugly primary colors. On gnuplot 5 and above, it's no longer the case. Plus, you can customize the default colors/styles by editing the .gnuplot file. I've done so: https://ghostbin.com/paste/pvj5m
Re: Introduction to High-Performance Scientific Computing
#36Is this a good book to get into the topic? Are there any other titles that can be recommended?
The author is one of the heavyweights in numerical linear algebra (which a lot of hpc boils down to in the end), and certainly knows his stuff. And based on some skimming of the book I did a while ago, yes, I'd say it's good. As for whether it's a good book "to get into the topic", I guess it depends on what you mean exactly. If you're a scientist who needs to write simulation code that can run on current HPC resourc…
> The author is one of the heavyweights in numerical linear algebra (which a lot of hpc boils down to in the end), and certainly knows his stuff
Came here to say this. I know Victor, and he is top notch.
Re: Introduction to High-Performance Scientific Computing
#37Looks like a good collection of important topics. The tutorials feel a bit 2005 to me. GNUplot and svn? In my scientific universe it's all matplotlib and git these days. Maybe I'm unique.
Another big surprise to me -- Gnuplot is NOT free/open source software in the GNU sense: "Gnuplot is freeware in the sense that you don’t have to pay for it. However it is not freeware in the sense that you would be allowed to distribute a modified version of your gnuplot freely. Please read and accept the modification and redistribution terms in the Copyright file." (see http://www.gnuplot.info/faq/faq.html) I had to remove Gnuplot from SageMath because of their GPL-incompatible license.
Re: Introduction to High-Performance Scientific Computing
#38Looks like a good collection of important topics. The tutorials feel a bit 2005 to me. GNUplot and svn? In my scientific universe it's all matplotlib and git these days. Maybe I'm unique.
Another big surprise to me -- Gnuplot is NOT free/open source software in the GNU sense: "Gnuplot is freeware in the sense that you don’t have to pay for it. However it is not freeware in the sense that you would be allowed to distribute a modified version of your gnuplot freely. Please read and accept the modification and redistribution terms in the Copyright file." (see http://www.gnuplot.info/faq/faq.html ) I had…
Gnuplot is free enough to be included in Debian. But no, not GPL licensed.
Re: Introduction to High-Performance Scientific Computing
#39At Nerdalize we're building a cloud that's specifally build for high performance scientific compute: http://www.nerdalize.com/cloud/
I don't understand what this means: "Great chance that it is cost efficient to run your job on our servers. Our servers are distributed over homes, so you don’t have to pay for the overhead of a datacenter. This means that your cost-per-job is up to 55% lower and you compute sustainably, as we use the produced heat to heat homes." Distributed over homes? As in "houses"? Your customer's data is stored at someone's (an…
If this is similar to Qarnot[1], the servers are also heaters in people home. I'm not sure how the Internet connection data transfers are handled by the ISP
Re: Introduction to High-Performance Scientific Computing
#40At Nerdalize we're building a cloud that's specifally build for high performance scientific compute: http://www.nerdalize.com/cloud/
I don't understand what this means: "Great chance that it is cost efficient to run your job on our servers. Our servers are distributed over homes, so you don’t have to pay for the overhead of a datacenter. This means that your cost-per-job is up to 55% lower and you compute sustainably, as we use the produced heat to heat homes." Distributed over homes? As in "houses"? Your customer's data is stored at someone's (an…
It looks like they install (sell?) racks of computers as household "heaters". Scroll down to the "Win, win, win!" section on their homepage with a video.
This is a cute idea but I am skeptical that it makes sense from either an economic or environmental perspective. There are far more efficient ways to produce heat than electric heaters that run 24/7, and likewise cooling in data centers can be extremely efficient by making use of water, e.g., see https://www.google.com/about/datacenters/efficiency/internal...
Also, maintaining servers in people's homes must be quite expensive and there is limited capacity. It's hard to see that scaling.
advanderveer -- do you have some sort of white-paper that compares the alternatives?
Disclaimer: I work for Google, but not on Google Cloud.