I’m not sure exactly what the question is

Mark, the question is, when all the cores are not being used, how do I

1. Detect this condition?
2. Determine how to change the model / merit function such that all cores are being used

This assumes that more cores = better solution in less time.  I hope this is a good assumption!

From your screenshot that not what’s happening here. Is there maybe some interaction between the #tosave and #cores? It’s odd that #tosave is 10 and you’re only getting 10 cores running. That would be a bug if true.

Exactly why I am asking for advice… the results do not make sense to me.  Sandrine’s post is not specific enough for me to debug this myself:  link.

Agreed… waiting for Zemax staff to respond here or I will send in a ticket.  Not sure how long is polite to wait before sending in a ticket.

I’m assuming that not too many other people check to see how many cores are working.  If they did, I’d love to hear about the experiences of other users.

-B

Have you tried to change the priority of the OpticsStudio process to “Above average” in the task manager?

Interesting questions Brian.

I can’t speak for where Ansys is going, obviously, but I do know a lot about how we got to where we are now. In the past, we never prioritized simply lighting up all the cores. The metric was always the total time taken to complete the task. Most often, and the default case, more processors is better, especially when there are relatively few processors. But it isn’t always, for the reasons Kevin and I have given. 

There is a good example of Amdahl’s law on this Wikipedia page: https://en.wikipedia.org/wiki/Amdahl's_law. Stealing a graphic from there, and quoting its caption:

The speed-up of a program from parallelization is limited by how much of the program can be parallelized.  For example, if 95% of the program can be parallelized, the theoretical maximum speedup using parallel computing would be 20× as shown in the diagram, no matter how many processors are used. If 90% of the program can be parallelized, the theoretical maximum speed-up using parallel computing would be 10×.

No way round this. It doesn’t matter if you’re using multiple processors or multiple machines, or what. Parallelization is not a magic bullet, but it can have good, positive benefits. BUT, getting 20x is a great improvement, even if you can’t get to 40x by just adding more processors.

I feel your pain about customers asking if the Cloud won’t just solve everything, or if putting it on a GPU card won’t just solve everything, etc. Sadly there are no simple answers. There is pure processor speed, the amount of memory each processor can access, the speed of that memory, the cache memory for each processor and so on.

We did actually offer a Cloud version of OS about five years ago, but it was not a commercial success except with people who were developing Cloud capability. OS is a great test bed for multi-core machines as it really can work all the cores, and not many codes can really do it as well as OS does it. When you look at the spec of a virtual machine on AWS or Azure, their ‘High Performance’ options are not as good as a medium grade engineering workstation. If you’re willing to pay ~$5k or more for a workstation, a physical machine on your desk is still the best way to go IMHO. The Cloud benefits are to do with team collaboration etc, not raw horsepower. 

For someone doing the weird and wonderful stuff I know you do, I think a workstation will be better than the Cloud for some time.

• Mark

@Mark.Nicholson 

Thanks for the sympathy.

Seems to me that Global Optimization and Local Optimization can easily be ported to multiple cores.  Let’s face it, we know that the optimization is a non-linear multi-variable problem.  Thus, more sampling and more information is more better.  To compute gradients or Jacobians, we need multiple computations.  Granted, we need memory for these, but that’s cheap.  So, from my perspective, a copy of each system with one core to rule them all makes a lot of sense. 

This seems like a simple partitioning problem.  I could literally do this with the API if I could deploy the images on multiple cores.  So, the real question is why can’t I?  If the answer is that the O/S is not good enough, then it should be deployed on an O/S that is good enough otherwise the technology will never advance.

So, I do get your point.

Now imagine you have enough memory (which I do) for each core to have a complete duplicate of the code and the model.  If that is the case, then as you work you way down the merit function, you can compute the Jacobian for each row in your merit function simultaneously.

This is the book that I used when I was in graduate school:  link.

So, yes I understand that shared memory, individual processor memory, and load sharing of processors all play a role in parallel processing.

One could ask, why bother having multiple cores if you can’t go any faster.  If OpticStudio cannot go faster, and its operations are imminently parallelizable, then what software can go faster with multiple cores.

No...not quite

We do already compute the merit function line by line. This works best in NS mode, as in sequential you have default merit functions that depend on the centroid, so there are groups of operands that have to be computed in series (think of RMS spot per field per wavelength...it’s not just one operand).

But even if each line could be evaluated independently of the others, it will never be simultaneous in the absolute sense that you’re using it. There must be serial code that launches and manages the parallel code. The performance improvement cannot be linear forever. That’s Amdahl’s Law.

By all means, write your own code and see. Ken put years into this, and Tim and his team have taken it even further. But you cannot make an infinitely fast computer by giving it an infinite number of processors. It just doesn’t work like that!

BTW, this is just one example of ‘Moore’s Law Doesn’t Work Anymore’. This is Gordon Moore’s law that CPU speeds will double every 18 months. It was true for a long, long time but it has now flattened out. We’re now at point in technology where CPU speeds in consumer products are being throttled by the speed of light itself, as data moves around the chip. It’s really a great time to be alive if you work with computers, even if no exponential lasts forever 😀

• Mark

I worked on the military version of this:  link.  HPCs have some of the technology to achieve that optical link between silicon.

Below:  parallel optical fiber to chip.