After sitting for two months in the editor's hand, our latest manuscript has been finally sent out to the reviewers. Coincidentally, just a couple of days after I sent an email asking about the status of manuscript.
Giuseppe Bilotta
Researcher @ INGV-OE. Opinions my own.
Account bridged to Bluesky via Bridgy Fed. If you wish to follow/interact with me from BS, please follow @ap.brid.gy first or your interactions will not be visible from the Fediverse.
Obviously when I teach #GPGPU I point out that the main point is performance, and “how to measure” is a topic we address. Obviously, the primary metric is kernel runtime. I then introduce the effective bandwidth metric (bytes read + bytes written, divided by time taken by the kernel) which is a good way to compare some similar kernels AND to discuss hardware limits and how close we are to them (I don't always discuss the roofline model though, maybe I should).
When we start to look into more advanced things, we hit the “snag” that sometimes a kernel using a more efficient technique may be _less_ effective at using a particular resource, e.g. by having having a _lower_ effective bandwidth —I do this on purpose to show how kernel runtime remains the ultimate “tell” on how good a kernel is compared to another (regardless of the additional information the effective bandwidth may tell us).
At this point I introduce a metric for which I don't actually know if there is a name: number of elements processed per seconds, which is just the number of elements, divided by the kernel runtime.
I call this effective throughput, but sometimes I get the nagging feeling that this may not be the correct term?
Our latest submission to JCP has been rejected as “out of scope” because the editor has interpreted the paper as being “an application”. A curious decision, since the paper is on how to compute acoustic terms efficiently and with higher precision compared to how it's usually done, and there are several applications, not one, where we show how this improves energy conservation and numerical stability in general.
The fact that the decision was taken by the editor *after* sending the paper out to reviewers, but claiming that it was made to avoid going through the review process, and that he only sent it to reviewers *after* I prodded him *after* the manuscript had been laying on his desk for months makes me doubtful of the, shall we say, correctness of the whole thing.
Now the big question is: should we challenge the decision, or should we just drop it and go with a different journal?
I want to simulate this with GPUSPH
From time to time I get a student whose exam project is good enough to be a thesis for their degree, and I feel embarrassed because mine is only a 6 CFU subject and the only thing I can do is give them top marks.
(Yes, they usually already have a thesis supervisor.)
OK I'm going to need ask the Fediverse for some math help here, because I have a feeling I'm forgetting something obvious and can't find the resources I'd need to answer my own question.
Let's say I have an underdetermined linear system. The methods generally employed to solve these find the least (l2) norm solution, which is generally acceptable.
However, in my case, I need to find solutions that satisfy some constraint (simple inequality constraints: solution must be in a box, with known ranges [min max] for each variable).
I think I have had a brain freeze because the only approach I can think of to solve this is to turn the problem into a constrained optimization problem. However, in my case (hundreds of thousands of variables) this is not computationally feasible: the time needed for the optimization approach is ridiculously higher than the standard linear system solution.
OTOH, I have a vague feeling that it should be possible to approach this differently, possibly remaining in the domain of the linear system resolution. What am I missing?