150 lines
4.6 KiB
Markdown
150 lines
4.6 KiB
Markdown
# ND-structure generation and operations
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**TODO**
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# Performance for n-dimensional structures operations
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One of the most sought after features of mathematical libraries is the high-performance operations on n-dimensional
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structures. In `kmath` performance depends on which particular context was used for operation.
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Let us consider following contexts:
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```kotlin
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// automatically build context most suited for given type.
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val autoField = NDField.auto(DoubleField, dim, dim)
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// specialized nd-field for Double. It works as generic Double field as well.
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val specializedField = NDField.real(dim, dim)
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//A generic boxing field. It should be used for objects, not primitives.
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val genericField = NDField.buffered(DoubleField, dim, dim)
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```
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Now let us perform several tests and see, which implementation is best suited for each case:
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## Test case
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To test performance we will take 2d-structures with `dim = 1000` and add a structure filled with `1.0`
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to it `n = 1000` times.
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## Specialized
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The code to run this looks like:
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```kotlin
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specializedField.run {
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var res: NDBuffer<Float64> = one
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repeat(n) {
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res += 1.0
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}
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}
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```
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The performance of this code is the best of all tests since it inlines all operations and is specialized for operation
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with doubles. We will measure everything else relative to this one, so time for this test will be `1x` (real time
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on my computer is about 4.5 seconds). The only problem with this approach is that it requires specifying type
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from the beginning. Everyone does so anyway, so it is the recommended approach.
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## Automatic
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Let's do the same with automatic field inference:
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```kotlin
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autoField.run {
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var res = one
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repeat(n) {
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res += 1.0
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}
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}
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```
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Ths speed of this operation is approximately the same as for specialized case since `NDField.auto` just
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returns the same `RealNDField` in this case. Of course, it is usually better to use specialized method to be sure.
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## Lazy
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Lazy field does not produce a structure when asked, instead it generates an empty structure and fills it on-demand
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using coroutines to parallelize computations.
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When one calls
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```kotlin
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lazyField.run {
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var res = one
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repeat(n) {
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res += 1.0
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}
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}
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```
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The result will be calculated almost immediately but the result will be empty. To get the full result
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structure one needs to call all its elements. In this case computation overhead will be huge. So this field never
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should be used if one expects to use the full result structure. Though if one wants only small fraction, it could
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save a lot of time.
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This field still could be used with reasonable performance if call code is changed:
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```kotlin
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lazyField.run {
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val res = one.map {
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var c = 0.0
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repeat(n) {
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c += 1.0
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}
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c
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}
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res.elements().forEach { it.second }
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}
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```
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In this case it completes in about `4x-5x` time due to boxing.
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## Boxing
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The boxing field produced by
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```kotlin
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genericField.run {
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var res: NDBuffer<Float64> = one
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repeat(n) {
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res += 1.0
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}
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}
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```
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is the slowest one, because it requires boxing and unboxing the `double` on each operation. It takes about
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`15x` time (**TODO: there seems to be a problem here, it should be slow, but not that slow**). This field should
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never be used for primitives.
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## Element operation
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Let us also check the speed for direct operations on elements:
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```kotlin
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var res = genericField.one
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repeat(n) {
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res += 1.0
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}
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```
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One would expect to be at least as slow as field operation, but in fact, this one takes only `2x` time to complete.
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It happens, because in this particular case it does not use actual `NDField` but instead calculated directly
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via extension function.
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## What about python?
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Usually it is bad idea to compare the direct numerical operation performance in different languages, but it hard to
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work completely without frame of reference. In this case, simple numpy code:
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```python
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import numpy as np
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res = np.ones((1000,1000))
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for i in range(1000):
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res = res + 1.0
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```
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gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think
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it is
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because better memory management). Of course if one writes `res += 1.0`, the performance will be different,
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but it would be different case, because numpy overrides `+=` with in-place operations. In-place operations are
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available in `kmath` with `MutableNDStructure` but there is no field for it (one can still work with mapping
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functions). |