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4.2.1 (p. 177): > Hyperbolic PDEs ... will not be discussed in this book. Aw. :(
At Nerdalize we're building a cloud that's specifally build for high performance scientific compute: http://www.nerdalize.com/cloud/
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 It looks like your offering is much more standard, what's the thinking there, or am I just wrong/out of date?
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.
Gnuplot is still used quite a lot in my area. You need to work more to make something aesthetically pleasing, but it's generally a better fit to plotting large amounts of data coming from another code. I've also been quite badly burnt by Matplotlib scripts no longer producing the same plot with changes in the library. Gnuplot version 5+ is nicer to use and has some pretty powerful features. This blog has been a good…
We're about to start rolling out chapters from my book, which covers the latest version:
https://alogus.com/publishing/gnuplot5/
EDITed to reflect comment below.
I feel the topics should also cover topics on GPGPU, OpenGL/CUDA.
Earlier quoted context omitted.
Gnuplot is still used quite a lot in my area. You need to work more to make something aesthetically pleasing, but it's generally a better fit to plotting large amounts of data coming from another code. I've also been quite badly burnt by Matplotlib scripts no longer producing the same plot with changes in the library. Gnuplot version 5+ is nicer to use and has some pretty powerful features. This blog has been a good…
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…
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?
I've edited my comment to make it more accurate.
Is this a good book to get into the topic? Are there any other titles that can be recommended?
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 resources, congratulations , you're smack in the middle of the target demographic of this book (I guess). If not, well, maybe some other book is more appropriate.
At Nerdalize we're building a cloud that's specifally build for high performance scientific compute: http://www.nerdalize.com/cloud/
"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 employee's?) house?
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.
It really depends on the size and the age of the code you're working with. We, for instance, use a code package that was born in 2005 (and continuously developed since), so it lives in SVN. For all the heavy-duty scientific visualization, we use VisIt [1], because the size and dimensionality of the data require a special-purpose visualization package. To me, "gnuplot or matplotlib" is a little beside the point - if w…