# Performance optimization¶

According to C.A.R. Hoare, “Premature optimization is the root of all evil”. Although the performance of Topographica is critically important, the way to achieve high performance is by spending all of our optimization efforts on the very small portion of the code that accounts for nearly all of the run time, i.e., the bottlenecks. The overall architecture of Topographica is designed explicitly to localize those bottlenecks into specific functions and objects that can then be heavily optimized without affecting any of the rest of the code. Only in such small, local regions, behind well-defined modules with clear semantics, is it possible to optimize effectively in a way that can be maintained in the long run. If it is precisely clear what the module is supposed to do, then the implementation can be polished to achieve that, while reasoning only about the behavior of that one specific module.

Conversely, finding that good performance requires adding special hacks in the largest-scale, general-purpose parts of the overall Topographica architecture means that the architecture is flawed and needs to be re-thought. For instance, please do not add any special checks scattered through the code testing for specific PatternGenerator or Sheet objects, substituting a quicker version of some operation but falling back to the general case for others. Such code is impossible to understand and maintain, because changes to the specific object implementations will not have any effect. Instead, we can optimize the individual PatternGenerator or Sheet object heavily. If some special hack needs to be done at a high level, e.g. at the base Sheet class level, we can add a method there that then gets overridden in the subclass with the special purpose code. That way all optimization will be local (and thus maintainable). If it’s not clear how to optimize something cleanly, first do it uncleanly to see if it will have any effect, but don’t check it in to Git. If it looks like the optimization is worthwhile, brainstorm with other team members to figure out a way to do it cleanly and check in the clean version instead.

This document considers runtime performance primarily; optimizing total memory is considered separately under Memory usage. On the other hand, the patterns of access to memory are crucially important for performance in large simulations. For a good overview of how to optimize memory usage patterns, see Ulrich Drepper’s article. If you are ambitious, even the most optimized components in Topographica could be further improved using these techniques, possibly substantially.

## Optimizing Python code¶

Although dramatic speedups usually require big changes as described below, sometimes all you need is minor tweaks to Python code to get it to have reasonable performance. Usually this involves avoiding unnecessary attribute lookup, as described in various collections of Python performance tips.

What is usually more important to ensure is that anything that can use the array-based primitives provided by numpy does so, because these generally have underlying C implementations that are quite fast. Using numpy operations should be the first approach when optimizing any component, and indeed when writing the component for the first time (because the numpy primitives are much easier to use and maintain than e.g. explicitly writing for loops).

## Providing optimized versions of Topographica objects¶

However, there are certain cases where the performance of numpy is not sufficient, or where numpy is unsuitable (for example, some numpy operations do not act in-place on arrays). Other components may be able to be implemented much more quickly if certain assumptions are made about the nature of their arguments, or the types of computations that can be performed.

In these cases, it is worthwhile to have a reference version of the object that is simple to understand and does not make any special assumptions. Then, an optimized version can be offered as an alternative. The convention we use is to add the suffix _optN to the optimized version, where N is a number that allows to distinguish between different optimized versions. This is helpful both for understanding and for ensuring correctness.

For example, consider CFPRF_DotProduct, from topo.responsefn.projfn. If users wish to use a version optimized by having been written in C, they can instead import CFPRF_DotProduct_opt from topo.responsefn.optimized. We use CFPRF_DotProduct_opt as standard in our code because it’s much faster than — but otherwise identical to — the unoptimized version. However, because CFPRF_DotProduct_opt relies on a more complex setup (having the weave module installed, as well as a correctly configured C++ compiler), we cannot assume all users will have access to it. It is also extremely difficult to read and understand. Therefore, we provide an automatic fall-back to the unoptimized version (see topo/responsefn/optimized.py for an example of how to do this).

The non-optimized version also acts as a simple specification of exactly what the optimized version is supposed to do, apart from any optimizations. The optimized versions are often nearly unreadable, so having the simple version available is very helpful for understanding and debugging. The expectation is that the simple (slow) versions will rarely change, but the optimized ones will get faster and faster over time, while preserving the same user-visible behavior.

## Finding bottlenecks¶

As discussed above, we wish to spend our time optimizing parts of the code that account for most of the run time. topo.misc.util contains the profile() function, providing a simple way to do this.

In order to see how basic optimization could be applied, we now show how optimizing one component can lead to a dramatic improvement. We will use examples/lissom_oo_or.ty without its optimized response function, by replacing projection.CFProjection.response_fn=responsefn.optimized.CFPRF_DotProduct_opt() with projection.CFProjection.response_fn=responsefn.optimized.CFPRF_DotProduct().

Now we can run topographica as follows, using the profile() function to give us information about the performance:

$./topographica examples/lissom_oo_or.ty -c "from topo.misc.util import profile; \ profile('topo.sim.run(99)',n=20)" 28148082 function calls (28145508 primitive calls) in 81.806 CPU seconds Ordered by: cumulative time, internal time List reduced from 245 to 20 due to restriction <20> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.041 0.041 81.806 81.806 topo/base/simulation.py:1121(run) 2178 0.006 0.000 79.951 0.037 topo/base/simulation.py:437(__call__) 2178 0.021 0.000 79.925 0.037 topo/base/projection.py:399(input_event) 2178 0.003 0.000 79.879 0.037 topo/base/projection.py:527(present_input) 2178 0.052 0.000 79.875 0.037 topo/base/cf.py:696(activate) 1980 0.013 0.000 79.816 0.040 topo/sheet/lissom.py:95(input_event) 1980 19.207 0.010 79.640 0.040 topo/base/cf.py:348(__call__) 4561920 8.435 0.000 34.585 0.000 topo/base/functionfamily.py:125(__call__) 4561920 19.912 0.000 19.912 0.000 topo/base/sheetcoords.py:387(submatrix) 9124038 13.639 0.000 13.639 0.000 {method 'ravel' of 'numpy.ndarray' objects} 4561920 12.512 0.000 12.512 0.000 {numpy.core._dotblas.dot} 4563900 5.848 0.000 5.932 0.000 topo/base/cf.py:861(__call__) 1188 0.010 0.000 1.133 0.001 topo/sheet/lissom.py:113(process_current_time) 1094 0.005 0.000 0.866 0.001 topo/misc/inlinec.py:72(inline_weave) 1094 0.017 0.000 0.855 0.001 lib/python2.6/site-packages/weave/inline_tools.py:130(inline) 1094 0.658 0.001 0.831 0.001 {apply} 99 0.001 0.000 0.684 0.007 topo/sheet/basic.py:284(learn) 100 0.002 0.000 0.546 0.005 topo/sheet/basic.py:263(_normalize_weights) 99 0.000 0.000 0.469 0.005 topo/base/simulation.py:511(__call__) 99 0.002 0.000 0.469 0.005 topo/sheet/basic.py:140(generate)  The n=20 argument restricts the list to the top 20 functions, ordered by cumulative time. For more information about the types of ordering available, help(profile) provides a link to Python’s documentation. From profile()‘s output above, we see (as expected) that all the time is spent in Simulation‘s run() method. We must proceed down the list until we find a less granular function — one that does not call many others, but instead performs some atomic task. The first such function is cf.py:352(__call__) (cf.py, line 352), CFPRF_Plugin‘s __call__() method: class CFPRF_Plugin(CFPResponseFn): """ Generic large-scale response function based on a simple single-CF function. Applies the single_cf_fn to each CF in turn. For the default single_cf_fn of DotProduct(), does a basic dot product of each CF with the corresponding slice of the input array. This function is likely to be slow to run, but it is easy to extend with any arbitrary single-CF response function. The single_cf_fn must be a function f(X,W) that takes two identically shaped matrices X (the input) and W (the ConnectionField weights) and computes a scalar activation value based on those weights. """ single_cf_fn = param.ClassSelector(ResponseFn,default=DotProduct(), doc="Accepts a ResponseFn that will be applied to each CF individually.") def __call__(self, iterator, input_activity, activity, strength): single_cf_fn = self.single_cf_fn for cf,r,c in iterator(): X = cf.input_sheet_slice.submatrix(input_activity) activity[r,c] = single_cf_fn(X,cf.weights) activity *= strength  About 97% of the total run time is spent in this method, so if we were able to optimize it, this would lead to good optimization of the simulation in total. How do we begin to optimize this method? In the first section of profile()’s output, we have more fine-grained information about the occupation of the CPU while executing this method: Function called... ncalls tottime cumtime ... topo/base/cf.py:348(__call__) -> 1980 0.003 0.004 param/parameterized.py:339(__get__) 4563900 5.848 5.932 topo/base/cf.py:861(__call__) 4561920 8.435 34.585 topo/base/functionfamily.py:125(__call__) 4561920 19.912 19.912 topo/base/sheetcoords.py:387(submatrix)  Over 40% of the time is spent running functionfamily.py:151(__call__), CFPRF_Plugin‘s default single_cf_fn: class DotProduct(ResponseFn): """ Return the sum of the element-by-element product of two 2D arrays. """ def __call__(self,m1,m2): return numpy.dot(m1.ravel(),m2.ravel())  Optimizing this dot product is evidently important, but it is not the only significant component. About 25% of the time is spent in the call to submatrix(), which is simply returning a section of the input activity array. Following this, the next most significant component is unlisted: about 20% of the time in the CFPRF’s __call__ is spent not calling other functions, i.e. inside this function itself. We could simply replace the dot product with an optimized version, but that would still leave other parts of this function as the speed-limiting factors. Line-by-line profiling could indicate exactly where the problems are, but a component such as this is a good candidate for replacement with an optimized version; we will describe this in the following section. Line-by-line profiling is described in a later section. ### Considering optimizations with C++ (weave)¶ Topographica makes it reasonably easy to re-write functions in C++ and offer them as optimized alternatives. We have done this for the CFPResponseFn described in the previous section, resulting in this code: class CFPRF_DotProduct_opt(CFPResponseFn): """ Dot-product response function. Written in C for a manyfold speedup; see CFPRF_DotProduct for an easier-to-read version in Python. The unoptimized Python version is equivalent to this one, but it also works for 1D arrays. """ single_cf_fn = param.ClassSelector(ResponseFn,DotProduct(),readonly=True) def __call__(self, iterator, input_activity, activity, strength, **params): temp_act = activity irows,icols = input_activity.shape X = input_activity.ravel() cfs = iterator.flatcfs num_cfs = len(cfs) mask = iterator.mask.data cf_type = iterator.cf_type code = c_header + """ DECLARE_SLOT_OFFSET(weights,cf_type); DECLARE_SLOT_OFFSET(input_sheet_slice,cf_type); npfloat *tact = temp_act; for (int r=0; r < num_cfs; ++r) { if((*mask++) == 0.0) *tact = 0; else { PyObject *cf = PyList_GetItem(cfs,r); CONTIGUOUS_ARRAY_FROM_SLOT_OFFSET(float,weights,cf) LOOKUP_FROM_SLOT_OFFSET(int,input_sheet_slice,cf); UNPACK_FOUR_TUPLE(int,rr1,rr2,cc1,cc2,input_sheet_slice); double tot = 0.0; npfloat *xj = X+icols*rr1+cc1; // computes the dot product for (int i=rr1; i < rr2; ++i) { npfloat *xi = xj; float *wi = weights; for (int j=cc1; j < cc2; ++j) { tot += *wi * *xi; ++wi; ++xi; } xj += icols; weights += cc2-cc1; } *tact = tot*strength; DECREF_CONTIGUOUS_ARRAY(weights); } ++tact; } """ inline(code, ['mask','X', 'strength', 'icols', 'temp_act','cfs','num_cfs','cf_type'], local_dict=locals(), headers=[''])  Replacing the CFP function with one written entirely in C++ (by reverting the line previously edited), we get the following profile: ./topographica examples/lissom_oo_or.ty -c "from topo.misc.util import profile; profile('topo.sim.run(99)',n=20)" 778542 function calls (775968 primitive calls) in 4.691 CPU seconds Ordered by: cumulative time, internal time List reduced from 239 to 20 due to restriction <20> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.039 0.039 4.691 4.691 topo/base/simulation.py:1121(run) 3074 0.018 0.000 3.359 0.001 topo/misc/inlinec.py:72(inline_weave) 3074 0.045 0.000 3.326 0.001 lib/python2.6/site-packages/weave/inline_tools.py:130(inline) 3074 3.101 0.001 3.273 0.001 {apply} 2178 0.006 0.000 2.838 0.001 topo/base/simulation.py:437(__call__) 2178 0.019 0.000 2.814 0.001 topo/base/projection.py:399(input_event) 2178 0.003 0.000 2.770 0.001 topo/base/projection.py:527(present_input) 2178 0.055 0.000 2.767 0.001 topo/base/cf.py:696(activate) 1980 0.012 0.000 2.703 0.001 topo/sheet/lissom.py:95(input_event) 2178 0.021 0.000 2.642 0.001 topo/responsefn/optimized.py:35(__call__) 1188 0.010 0.000 1.135 0.001 topo/sheet/lissom.py:113(process_current_time) 99 0.001 0.000 0.680 0.007 topo/sheet/basic.py:284(learn) 100 0.003 0.000 0.545 0.005 topo/sheet/basic.py:263(_normalize_weights) 99 0.000 0.000 0.469 0.005 topo/base/simulation.py:511(__call__) 99 0.002 0.000 0.468 0.005 topo/sheet/basic.py:140(generate) 297/99 0.016 0.000 0.451 0.005 topo/base/patterngenerator.py:116(__call__) 1089 0.112 0.000 0.446 0.000 topo/base/projection.py:462(activate) 99 0.013 0.000 0.403 0.004 topo/pattern/basic.py:589(function) 400 0.002 0.000 0.327 0.001 topo/base/cf.py:719(apply_learn_output_fns) 400 0.003 0.000 0.318 0.001 topo/transferfn/optimized.py:33(__call__)  The simulation is now almost 20 times faster than the Numpy version. The C++ code adds extra work: for maintenance, for deployment on different platforms, and for user understanding — so it has to be justified, meaning it should provide large speedups. In this case, the performance improvement justifies the additional costs (which have been substantial in terms of maintenance and platform support — although platform support cost is diluted by all such C++ functions, and any added in the future). While making this kind of investigation, you must check that simulations run with different versions of a function are producing the same results. In particular, when working with optimized C++ functions, it is possible for one version to appear much faster than another when in fact the computations being performed are not equivalent. A final consideration is to ensure that the profile run times are long enough to obtain reliable results. For shorter runs, it would be necessary to repeat them to find a reasonable estimate of the minimum time. ### Line-by-line profiling¶ The profile function described above (which uses Python’s inbuilt profiling) only reports time spent inside functions, but gives no information about how that time is spent. There is also an optional line-by-line profiling package available that gives information about how the time is spent inside one or two specific functions. So, for instance, if you have a function that does various operations on arrays, you can now see how long all those operations take relative to each other. That might allow you to identify a bottleneck in the function easily. (Note that before doing a line-by-line profile of a function, you should previously have identified that function as a bottleneck using the profiling function described earlier. Otherwise, optimizing the function will result in little performance gain overall.) The line-by-line profiling package is not yet built by default. If you want to build it, execute the following from your Topographica directory: $ make -C external line_profiler


Then, the easiest way to use the new package is to:

1. put the following two lines into ~/ipy_user_conf.py (in the main() function):

import line_profiler
ip.expose_magic('lprun',line_profiler.magic_lprun)

2. use %lprun from the Topographica prompt

#### Examples¶

To profile topo.base.cf.ConnectionField’s _create_input_sheet_slice() method while starting the lissom.ty script:

$./topographica topo_t000000.00_c1>>> from topo.base.cf import ConnectionField topo_t000000.00_c2>>> %lprun -f ConnectionField._create_input_sheet_slice execfile("examples/lissom.ty")  To profile calling of topo.transferfn.HomeoStaticMaxEnt while Topographica is running: $ ./topographica -i contrib/lesi.ty
topo_t000000.00_c1>>> from topo.transferfn import HomeostaticMaxEnt
topo_t000000.00_c2>>> %lprun -f HomeostaticMaxEnt.__call__ topo.sim.run(30)


The output you get is something like this:

Timer unit: 1e-06 s

File: /disk/data1/workspace/v1cball/topographica/topo/transferfn/basic.py
Function: __call__ at line 749
Total time: 0.955004 s

Line Hits   Time  PerHit %Time Line Contents
================================================
749                           def __call__(self,x):
750   450  13003   28.9   1.4    if self.first_call:
751     1      9    9.0   0.0         self.first_call = False
752     1     20   20.0   0.0         if self.a_init==None:
753     1    817  817.0   0.1             self.a = self.random_generator.uniform(low=10, high=20,size=x.shape)
754                                   else:
755                                       self.a = ones(x.shape, x.dtype.char) * self.a_init
756     1     27   27.0   0.0         if self.b_init==None:
757     1    411  411.0   0.0             self.b = self.random_generator.uniform(low=-8.0, high=-4.0,size=x.shape)
758                                   else:
759                                       self.b = ones(x.shape, x.dtype.char) * self.b_init
760     1    128  128.0   0.0        self.y_avg = zeros(x.shape, x.dtype.char)
761
762                              # Apply sigmoid function to x, resulting in what Triesch calls y
763   450  88485  196.6   9.3     x_orig = copy.copy(x)
764
765   450  24277   53.9   2.5     x *= 0.0
766   450 662809 1472.9  69.4     x += 1.0 / (1.0 + exp(-(self.a*x_orig + self.b)))
767
768
769   450   5979   13.3   0.6    self.n_step += 1
770   450  34237   76.1   3.6    if self.n_step == self.step:
771    30    253    8.4   0.0        self.n_step = 0
772    30    654   21.8   0.1        if self.plastic:
773    30  19448  648.3   2.0            self.y_avg = (1.0-self.smoothing)*x + self.smoothing*self.y_avg
774
775                                      # Update a and b
776    30  65652 2188.4   6.9            self.a += self.eta * (1.0/self.a + x_orig - (2.0 + 1.0/self.mu)*...
777    30  38795 1293.2   4.1            self.b += self.eta * (1.0 - (2.0 + 1.0/self.mu)*x + x*x/self.mu)


From this output, you can see that 69.4% of the time is spent in line 766, which is thus the best place to start optimizing (e.g. by using a lookup table for the sigmoid function, in this case).