Hi Ed. I have worked on a rather long profiling script now.
It creates the necessary data structures, and then call the relax_disp target function. Can you devise a "place" to put this script? Best Troels 2014-06-05 11:13 GMT+02:00 Edward d'Auvergne <[email protected]>: > Hi Troels, > > This huge speed up you see also applies when you have multiple field > strength data. To understand how you can convert the long rank-1 > array you have in your g_* data structures into the multi-index rank-5 > back_calc array with dimensions {Ei, Si, Mi, Oi, Di}, see the numpy > reshape() function: > > http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html > > You can obtain this huge speed up if you convert the > target_functions.relax_disp data structures to be similar to your g_* > data structures, delete the looping in the func_*() target functions > over the {Ei, Si, Mi, Oi, Di} dimensions (for the numeric models, this > looping would need to be shifted into the lib.dispersion code to keep > the API consistent), pass the new higher-dimensional data into the > lib.dispersion modules, and finally use R2eff.reshape() to place the > data back into the back_calc data structure. This would again need to > be in a new branch, and you should only do it if you wish to have huge > speed ups for multi-experiment, clustered, multi-field, or > multi-offset data. The speed ups will also only be for the analytic > models as the numeric models unfortunately do not have the necessary > maths derived for calculating everything simultaneously in one linear > algebra operation. > > Regards, > > Edward > > > > On 4 June 2014 17:11, Edward d'Auvergne <[email protected]> wrote: > > Hi, > > > > The huge differences are because of the changes in the lib.dispersion > > modules. But wait! The r2eff_CR72() receives the data for each > > experiment, spin, and offset separately. So this insane speed up is > > not realised in the current target functions. But the potential for > > these speed ups is there thanks to your infrastructure work in the > > 'disp_speed' branch. I have mentioned this before: > > > > http://thread.gmane.org/gmane.science.nmr.relax.devel/5726 > > > > Specifically the follow up at: > > > > http://thread.gmane.org/gmane.science.nmr.relax.devel/5726/focus=5806 > > > > The idea mentioned in this post is exactly the speed up you see in > > this test! So if the idea is implemented in relax then, yes, you will > > see this insane speed up in a clustered analysis. Especially for > > large clusters and a large number of offsets (for R1rho but also for > > CPMG when off-resonace effects are implemented, > > http://thread.gmane.org/gmane.science.nmr.relax.devel/5414/focus=5445). > > But unfortunately currently you do not. > > > > Regards, > > > > Edward > > > > > > > > > > On 4 June 2014 16:45, Troels Emtekær Linnet <[email protected]> > wrote: > >> Hi Edward. > >> > >> Ah ja. > >> I overwrite the state file for each new global fitting, with the new > pipe. > >> So that is increasing quite much. > >> I will change that. > >> > >> I just checked my scripts. > >> In both cases, I would do one grid search for the first run, and then > the > >> recurring analysis would copy the parameters from the first pipe. > >> > >> And the speed-up is between these analysis. > >> > >> Hm. > >> I have to take that variable out with the grid search! > >> > >> I am trying to device a profile script, which I can put in the base > folder > >> of older versions of relax. > >> For example relax 3.1.6 which I also have. > >> > >> It looks like this: > >> ------------- > >> # Python module imports. > >> from numpy import array, float64, pi, zeros > >> import sys > >> import os > >> import cProfile > >> > >> # relax module imports. > >> from lib.dispersion.cr72 import r2eff_CR72 > >> > >> # Default parameter values. > >> r20a = 2.0 > >> r20b = 4.0 > >> pA = 0.95 > >> dw = 2.0 > >> kex = 1000.0 > >> > >> relax_times = 0.04 > >> ncyc_list = [2, 4, 8, 10, 20, 40, 500] > >> > >> # Required data structures. > >> s_ncyc = array(ncyc_list) > >> s_num_points = len(s_ncyc) > >> s_cpmg_frqs = s_ncyc / relax_times > >> s_R2eff = zeros(s_num_points, float64) > >> > >> g_ncyc = array(ncyc_list*100) > >> g_num_points = len(g_ncyc) > >> g_cpmg_frqs = g_ncyc / relax_times > >> g_R2eff = zeros(g_num_points, float64) > >> > >> # The spin Larmor frequencies. > >> sfrq = 200. * 1E6 > >> > >> # Calculate pB. > >> pB = 1.0 - pA > >> > >> # Exchange rates. > >> k_BA = pA * kex > >> k_AB = pB * kex > >> > >> # Calculate spin Larmor frequencies in 2pi. > >> frqs = sfrq * 2 * pi > >> > >> # Convert dw from ppm to rad/s. > >> dw_frq = dw * frqs / 1.e6 > >> > >> > >> def single(): > >> for i in xrange(0,10000): > >> r2eff_CR72(r20a=r20a, r20b=r20b, pA=pA, dw=dw_frq, kex=kex, > >> cpmg_frqs=s_cpmg_frqs, back_calc=s_R2eff, num_points=s_num_points) > >> > >> cProfile.run('single()') > >> > >> def cluster(): > >> for i in xrange(0,10000): > >> r2eff_CR72(r20a=r20a, r20b=r20b, pA=pA, dw=dw_frq, kex=kex, > >> cpmg_frqs=g_cpmg_frqs, back_calc=g_R2eff, num_points=g_num_points) > >> > >> cProfile.run('cluster()') > >> ------------------------ > >> > >> For 3.1.6 > >> [tlinnet@tomat relax-3.1.6]$ python profile_lib_dispersion_cr72.py > >> 20003 function calls in 0.793 CPU seconds > >> > >> Ordered by: standard name > >> > >> ncalls tottime percall cumtime percall filename:lineno(function) > >> 1 0.000 0.000 0.793 0.793 <string>:1(<module>) > >> 10000 0.778 0.000 0.783 0.000 cr72.py:98(r2eff_CR72) > >> 1 0.010 0.010 0.793 0.793 > >> profile_lib_dispersion_cr72.py:69(single) > >> 1 0.000 0.000 0.000 0.000 {method 'disable' of > >> '_lsprof.Profiler' objects} > >> 10000 0.005 0.000 0.005 0.000 {range} > >> > >> > >> 20003 function calls in 61.901 CPU seconds > >> > >> Ordered by: standard name > >> > >> ncalls tottime percall cumtime percall filename:lineno(function) > >> 1 0.000 0.000 61.901 61.901 <string>:1(<module>) > >> 10000 61.853 0.006 61.887 0.006 cr72.py:98(r2eff_CR72) > >> 1 0.013 0.013 61.901 61.901 > >> profile_lib_dispersion_cr72.py:75(cluster) > >> 1 0.000 0.000 0.000 0.000 {method 'disable' of > >> '_lsprof.Profiler' objects} > >> 10000 0.035 0.000 0.035 0.000 {range} > >> > >> > >> For trunk > >> > >> [tlinnet@tomat relax_trunk]$ python profile_lib_dispersion_cr72.py > >> 80003 function calls in 0.514 CPU seconds > >> > >> Ordered by: standard name > >> > >> ncalls tottime percall cumtime percall filename:lineno(function) > >> 1 0.000 0.000 0.514 0.514 <string>:1(<module>) > >> 10000 0.390 0.000 0.503 0.000 cr72.py:100(r2eff_CR72) > >> 10000 0.008 0.000 0.040 0.000 fromnumeric.py:1314(sum) > >> 10000 0.007 0.000 0.037 0.000 fromnumeric.py:1708(amax) > >> 10000 0.006 0.000 0.037 0.000 fromnumeric.py:1769(amin) > >> 1 0.011 0.011 0.514 0.514 > >> profile_lib_dispersion_cr72.py:69(single) > >> 10000 0.007 0.000 0.007 0.000 {isinstance} > >> 1 0.000 0.000 0.000 0.000 {method 'disable' of > >> '_lsprof.Profiler' objects} > >> 10000 0.030 0.000 0.030 0.000 {method 'max' of > >> 'numpy.ndarray' objects} > >> 10000 0.030 0.000 0.030 0.000 {method 'min' of > >> 'numpy.ndarray' objects} > >> 10000 0.025 0.000 0.025 0.000 {method 'sum' of > >> 'numpy.ndarray' objects} > >> > >> > >> 80003 function calls in 1.209 CPU seconds > >> > >> Ordered by: standard name > >> > >> ncalls tottime percall cumtime percall filename:lineno(function) > >> 1 0.000 0.000 1.209 1.209 <string>:1(<module>) > >> 10000 1.042 0.000 1.196 0.000 cr72.py:100(r2eff_CR72) > >> 10000 0.009 0.000 0.049 0.000 fromnumeric.py:1314(sum) > >> 10000 0.007 0.000 0.052 0.000 fromnumeric.py:1708(amax) > >> 10000 0.007 0.000 0.052 0.000 fromnumeric.py:1769(amin) > >> 1 0.014 0.014 1.209 1.209 > >> profile_lib_dispersion_cr72.py:75(cluster) > >> 10000 0.007 0.000 0.007 0.000 {isinstance} > >> 1 0.000 0.000 0.000 0.000 {method 'disable' of > >> '_lsprof.Profiler' objects} > >> 10000 0.045 0.000 0.045 0.000 {method 'max' of > >> 'numpy.ndarray' objects} > >> 10000 0.045 0.000 0.045 0.000 {method 'min' of > >> 'numpy.ndarray' objects} > >> 10000 0.033 0.000 0.033 0.000 {method 'sum' of > >> 'numpy.ndarray' objects} > >> --------------- > >> > >> For 10000 iterations > >> > >> 3.1.6 > >> Single: 0.778 > >> 100 cluster: 61.853 > >> > >> trunk > >> Single: 0.390 > >> 100 cluster: 1.042 > >> > >> ------ > >> > >> For 1000000 iterations > >> 3.1.6 > >> Single: 83.365 > >> 100 cluster: ???? Still running.... > >> > >> trunk > >> Single: 40.825 > >> 100 cluster: 106.339 > >> > >> I am doing something wrong here? > >> > >> That is such a massive speed up for clustered analysis, that I simply > can't > >> believe it! > >> > >> Best > >> Troels > >> > >> > >> > >> > >> > >> > >> > >> 2014-06-04 15:04 GMT+02:00 Edward d'Auvergne <[email protected]>: > >> > >>> Hi, > >>> > >>> Such a huge speed up cannot be from the changes of the 'disp_speed' > >>> branch alone. I would expect from that branch a maximum drop from 30 > >>> min to 15 min. Therefore it must be your grid search changes. When > >>> changing, simplifying, or eliminating the grid search, you have to be > >>> very careful about the introduced bias. This bias is unavoidable. It > >>> needs to be mentioned in the methods of any paper. The key is to be > >>> happy that the bias you have introduced will not negatively impact > >>> your results. For example if you believe that the grid search > >>> replacement is reasonably close to the true solution that the > >>> optimisation will be able to reach the global minimum. You also have > >>> to convince the people reading your paper that the introduced bias is > >>> reasonable. > >>> > >>> As for a script to show the speed changes, you could have a look at > >>> maybe the > >>> test_suite/shared_data/dispersion/Hansen/relax_results/relax_disp.py > >>> file. This performs a full analysis with a large range of dispersion > >>> models on the truncated data set from Flemming Hansen. Or > >>> test_suite/shared_data/dispersion/Hansen/relax_disp.py which uses all > >>> of Flemming's data. These could be run before and after the merger of > >>> the 'disp_speed' branch, maybe with different models and the profile > >>> flag turned on. You could then create a text file in the > >>> test_suite/shared_data/dispersion/Hansen/relax_results/ directory > >>> called something like 'relax_timings' to permanently record the speed > >>> ups. This file can be used in the future for documenting any other > >>> speed ups as well. > >>> > >>> Regards, > >>> > >>> Edward > >>> > >>> > >>> > >>> > >>> On 4 June 2014 14:37, Troels Emtekær Linnet <[email protected]> > wrote: > >>> > Looking at my old data, I can see that writing out of data between > each > >>> > global fit analysis before took around 30 min. > >>> > > >>> > They now take 2-6 mins. > >>> > > >>> > I almost can't believe that speed up! > >>> > > >>> > Could we devise a devel-script, which we could use to simulate the > >>> > change? > >>> > > >>> > Best > >>> > Troels > >>> > > >>> > > >>> > > >>> > 2014-06-04 14:24 GMT+02:00 Troels Emtekær Linnet > >>> > <[email protected]>: > >>> > > >>> >> Hi Edward. > >>> >> > >>> >> After the changes to the lib/dispersion/model.py files, I see > massive > >>> >> speed-up of the computations. > >>> >> > >>> >> During 2 days, I performed over 600 global fittings for a 68 residue > >>> >> protein, where all residues where clustered.I just did it with 1 > cpu. > >>> >> > >>> >> This is really really impressive. > >>> >> > >>> >> I did though also alter how the grid search was performed, > pre-setting > >>> >> some of the values from known values referred to in a paper. > >>> >> So I can't really say what has cut the time down. > >>> >> > >>> >> But looking at the calculations running, the minimisation runs quite > >>> >> fast. > >>> >> > >>> >> So, how does relax do the collecting of data for global fitting? > >>> >> > >>> >> Does i collect all the R2eff values for the clustered spins, and > sent > >>> >> it > >>> >> to the target function > >>> >> together with the array of parameters to vary? > >>> >> > >>> >> Or does it calculate per spin, and share the common parameters? > >>> >> > >>> >> My current bottle neck actually seems to be the saving of the state > >>> >> file, > >>> >> between each iteration of global analysis. > >>> >> > >>> >> Best > >>> >> Troels > >>> >> > >>> > _______________________________________________ > >>> > relax (http://www.nmr-relax.com) > >>> > > >>> > This is the relax-devel mailing list > >>> > [email protected] > >>> > > >>> > To unsubscribe from this list, get a password > >>> > reminder, or change your subscription options, > >>> > visit the list information page at > >>> > https://mail.gna.org/listinfo/relax-devel > >> > >> > _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
