Re: How is the R2eff data collected and processed for clustered analysis?Re: How is the R2eff data collected and processed for clustered analysis?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 <edward@xxxxxxxxxxxxx>:
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 <edward@xxxxxxxxxxxxx> 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 <tlinnet@xxxxxxxxxxxxx>wrote:Hi Edward. Ah ja. I overwrite the state file for each new global fitting, with the newpipe.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 thentherecurring 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 basefolderof 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 simplycan'tbelieve it! Best Troels 2014-06-04 15:04 GMT+02:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>: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 <tlinnet@xxxxxxxxxxxxx>wrote:Looking at my old data, I can see that writing out of data betweeneachglobal 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 <tlinnet@xxxxxxxxxxxxx>:Hi Edward. After the changes to the lib/dispersion/model.py files, I seemassivespeed-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 1cpu.This is really really impressive. I did though also alter how the grid search was performed,pre-settingsome 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, andsentit 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 relax-devel@xxxxxxx 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