Timeline for Parallel Computing Problem
Current License: CC BY-SA 4.0
19 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 19, 2019 at 12:19 | comment | added | guangya | @CATrevillian you can contact with me from this address.I didn't live in the USA.So the information your send made me confused | |
| Apr 19, 2019 at 12:15 | comment | added | guangya | @CATrevillian My E-mail address:[email protected] | |
| Apr 19, 2019 at 12:05 | comment | added | CA Trevillian | @guangya Uncompress["1:eJxTTMoPChZjYGAoKUoty8zJyUzMc8hPzM5JzEvRS00pBQCXeApu"] | |
| Apr 19, 2019 at 11:48 | comment | added | guangya | @CATrevillian E-mail address? | |
| Apr 19, 2019 at 11:47 | comment | added | CA Trevillian | @guangya trevilliandotoaklanddotedu :D | |
| Apr 19, 2019 at 11:45 | comment | added | guangya | @CATrevillian I didn't find your contact information :) | |
| Apr 19, 2019 at 11:31 | comment | added | CA Trevillian | @guangya wonderful! I have added my contact information to my profile. | |
| Apr 19, 2019 at 11:13 | comment | added | guangya | Of course.How to get in touch with you?Se isn't a good communication platform@CATrevillian | |
| Apr 19, 2019 at 11:06 | comment | added | CA Trevillian | @guangya ah very nice :) I study magnetism and spin dynamics! Thank you for your clarification :) I may clean your code up and experiment with it, if this is okay? I will share my results and perhaps some interactive demonstration with you. | |
| Apr 19, 2019 at 11:02 | comment | added | guangya | we use it to judge whether a substance is trivial topology or not@CATrevillian. Nobel Prize in 2016 | |
| Apr 19, 2019 at 10:57 | comment | added | guangya | Condensed matter physics.Bott index,is a kind of Chern number@CATrevillian | |
| Apr 19, 2019 at 10:53 | comment | added | CA Trevillian | @guangya I am happy with the solution provided by happy fish as well :) enjoy your parallelization, I am curious, what is the goal of your program, to show what exactly? | |
| Apr 19, 2019 at 10:51 | comment | added | guangya | I appreciate your advise.happy fish's answer has solved my problem@CATrevillian | |
| Apr 19, 2019 at 7:38 | history | edited | CA Trevillian | CC BY-SA 4.0 | More clarification, addition of function testing information |
| Apr 19, 2019 at 6:49 | comment | added | vapor | I agree with your general ideas on parallel evaluations. I am just saying that these theories don't localize for this particular problem. If you experiment on the problem you will find immediately that the bottleneck is not on where you focus: it's just distributing 10 difficult tasks to 6(by default) kernels, the overhead of subsequent calls and copying definitions is negligible. For the automatically distribute definition part, please refer to the first example in Options->DistributedContexts and mathematica.stackexchange.com/questions/39178/… | |
| Apr 19, 2019 at 6:49 | comment | added | CA Trevillian | @happyfish my comment stems from my having had an order of magnitude speed-up earlier today, by distributing definitions of my self-packaged function and launching the kernels ahead of their parallel execution. It seems, though, this may be another time when "vectorized"(I still struggle to confidently state/grasp this concept/method) functions would be helpful if implemented? Your expertise puts mine at a junior, however, so I will look to your continued interpretation of this problem solution. | |
| Apr 19, 2019 at 6:41 | comment | added | CA Trevillian | @happyfish I'm not sure that is entirely accurate, unfortunately. Though it would be nice! My understanding is as follows: When you perform the first call on a parallel function, you will spend more time than subsequent calls, this being due to the need to launch all kernels. Additionally there is some time taken to distribute definitions, if this is indeed done automatically. I am curious if there is a part of the documentation you can point to for this? I am unable to have ParallelTable actually use all kernels unless you have done as I stated, otherwise they take about a second longer. | |
| Apr 19, 2019 at 6:34 | comment | added | vapor | Thanks for your answer, but I don't think it addresses the problem OP encountered. LaunchKernels and DistributeDefinitions are done automatically, there is no need of explicitly writing down. There won't be an "immense speed-up with parallel functions" in either case. Testing the parameter can avoid unnecessary symbolic computations, but won't help here since everything is numerical. | |
| Apr 19, 2019 at 6:16 | history | answered | CA Trevillian | CC BY-SA 4.0 |