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Introduction to High-Performance Scientific Computing

pages.tacc.utexas.edu

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Re: Introduction to High-Performance Scientific Computing

#41

Post by robmccoll »

williamstein wrote: post #37
acidburnNSA wrote: post #3

Looks 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…

GLE can produce some decent plots and is BSD-licensed, but the DSL used to create the plots can be cumbersome. http://glx.sourceforge.net/examples/3dplots/index.html

Re: Introduction to High-Performance Scientific Computing

#42

Post by stonogo »

dekhn wrote: post #22
danpalmer wrote: post #11

Earlier 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 It looks like your offering is much more standard, what's the thinking there, or am I just wrong/out of date?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.

If you don't need a high-speed interconnect, you don't need HPC. That's not to say that MPI per se must always be involved, but if for instance the 10gbit connection on Amazon's half-baked "HPC" offering is sufficient, then you definitely don't need a supercomputer.

There is a ton of important scientific work waiting for core hours that really shouldn't be. A loosely-connected grid of laptops would serve a lot of projects very well. On the other hand, there is a large body of work that does require a classical supercomputer, so it doesn't really do anyone any good to accuse MPI of being a sales gimmick.

Re: Introduction to High-Performance Scientific Computing

#43

Post by sctb »

Karnickel wrote: post #12

This 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.

Please don't do that.

Re: Introduction to High-Performance Scientific Computing

#44

Post by victotronics »

BeetleB wrote: post #31

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.

Basics of what? High performance computing? I'd say those are numerical analysis topics and there are tons of books for that. Unless you can make a case that there are high performance aspects to root finding, I'm not going to include it. (You should have said FFT. That has very funky interaction with caches and TBL that absolutely necessitate its inclusion.)

Re: Introduction to High-Performance Scientific Computing

#45

Post by dekhn »

stonogo wrote: post #42
dekhn wrote: post #22

Earlier quoted context omitted.

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.

If you don't need a high-speed interconnect, you don't need HPC. That's not to say that MPI per se must always be involved, but if for instance the 10gbit connection on Amazon's half-baked "HPC" offering is sufficient, then you definitely don't need a supercomputer. There is a ton of important scientific work waiting for core hours that really shouldn't be. A loosely-connected grid of laptops would serve a lot of pro…

There is plenty of HPC that does not need interconnect. It's false, categorically, to say that HPC requires interconnect of any kind.

An isolated, off-net computer - even a desktop PC- stuffed to the gills with GPUs can do HPC. On the other hand, machines connected with 10gbit might do HPC, but you'll have trouble getting codes to scale in a way that is "high performance", relative to what you can get out of threading on a single machine, or a small number of GPUs.

Very little work truly requires classic supercomputers or MPI- there are very few codes where an important engineering problem must be run on a system with low latency, high bandwidth.

Re: Introduction to High-Performance Scientific Computing

#46

Post by j-pb »

shoyer wrote: post #40
metaobject wrote: post #29

Earlier quoted context omitted.

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…

> There are far more efficient ways to produce heat than electric heaters that run 24/7.

Do you mean cheaper? Because generating heat always has 100% efficiency. The only difference is that if you go from burnable materials to heat directly you don't get the nice side effect of getting computation done, so burning stuff is actually less efficient.

Re: Introduction to High-Performance Scientific Computing

#47

Post by n00b101 »

metaobject wrote: post #26
leephillips wrote: post #24

Earlier quoted context omitted.

Gnuplot can handle data files with millions of points, that some users have reported to cause Matplotlib to crash. It's actually easy to make the plots look good. It can be controlled through a socket interface from any programming language, and seemlessly integrates with LaTeX. We're about to start rolling out chapters from my book, which covers the latest version: https://alogus.com/publishing/gnuplot5/ EDITed to r…

Hmmm, I've never had mpl crash and we routinely plot data sets with 10s of millions of points. We do a mixture of 2D scatter plots. 1D/2D histograms, and a few 3D plots. What type of plots are you generating? What type of data?

> we routinely plot data sets with 10s of millions of points

What is the performance like on that?

Re: Introduction to High-Performance Scientific Computing

#48

Post by BeetleB »

dagss wrote: post #34
BeetleB wrote: post #31

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.

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…

I guess I misunderstood the audience for the book - I took it more as an introductory level book that goes deep into topics (some of the items listed in the TOC are very "basic", which was why I was surprised it had some advanced topics but not all the basics).

As for "laptop can do in seconds" - well, not if your objective function takes a few minutes to run. The last time I needed it, the objective function took about 2 minutes, and there were 8 parameters we were optimizing over. Standard derivative based optimizing algorithms will require 9 invocations of the function per iteration. So one iteration of the algorithm took about 18 minutes. Certainly not "seconds". Of course, those evaluations could be done in parallel, so I just had them run on multiple cores, bringing it down to only a few minutes per iteration.

Without knowing something about the algorithms my library was using, I would have been totally lost (not to mention I would have likely picked the wrong algorithm for the job at hand).

But yes - I did not write my own algorithm - just used an off the shelf one. However, if you expect that someone who hasn't studied the topic can just use a random optimization algorithm and get things to work, you are mistaken.

Re: Introduction to High-Performance Scientific Computing

#49

Post by [deleted] »

shoyer wrote: post #40
metaobject wrote: post #29

Earlier quoted context omitted.

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…

[deleted]

Re: Introduction to High-Performance Scientific Computing

#50

Post by RhysU »

arcanus wrote: post #36
jabl wrote: post #28

Earlier quoted context omitted.

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.

Agreed.
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