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# Pillow-SIMD
Pillow-SIMD is "following" Pillow fork (which is PIL fork itself).
"Following" means than Pillow-SIMD versions are 100% compatible
drop-in replacement for Pillow with the same version number.
For example, `Pillow-SIMD 3.2.0.post3` is drop-in replacement for
`Pillow 3.2.0` and `Pillow-SIMD 3.3.3.post0` for `Pillow 3.3.3`.
Pillow-SIMD is "following" Pillow (which is a PIL's fork itself).
"Following" here means than Pillow-SIMD versions are 100% compatible
drop-in replacements for Pillow of the same version.
For example, `Pillow-SIMD 3.2.0.post3` is a drop-in replacement for
`Pillow 3.2.0`, and `Pillow-SIMD 3.3.3.post0` — for `Pillow 3.3.3`.
For more information about original Pillow, please
For more information on the original Pillow, please refer to:
[read the documentation][original-docs],
[check the changelog][original-changelog] and
[find out how to contribute][original-contribute].
@ -14,35 +14,35 @@ For more information about original Pillow, please
## Why SIMD
There are many ways to improve the performance of image processing.
You can use better algorithms for the same task, you can make better
implementation for current algorithms, or you can use more processing unit
resources. It is perfect when you can just use more efficient algorithm like
when gaussian blur based on convolutions [was replaced][gaussian-blur-changes]
by sequential box filters. But a number of such improvements are very limited.
It is also very tempting to use more processor unit resources
(via parallelization) when they are available. But it is handier just
to make things faster on the same resources. And that is where SIMD works better.
There are multiple ways to tweak image processing performance.
To name a few, such ways can be: utilizing better algorithms, optimizing existing implementations,
using more processing power and/or resources.
One of the great examples of using a more efficient algorithm is [replacing][gaussian-blur-changes]
a convolution-based Gaussian blur with a sequential-box one.
SIMD stands for "single instruction, multiple data". This is a way to perform
same operations against the huge amount of homogeneous data.
Modern CPU have different SIMD instructions sets like
MMX, SSE-SSE4, AVX, AVX2, AVX512, NEON.
Such examples are rather rare, though. It is also known, that certain processes might be optimized
by using parallel processing to run the respective routines.
But a more practical key to optimizations might be making things work faster
using the resources at hand. For instance, SIMD computing might be the case.
Currently, Pillow-SIMD can be [compiled](#installation) with SSE4 (default)
and AVX2 support.
SIMD stands for "single instruction, multiple data" and its essence is
in performing the same operation on multiple data points simultaneously
by using multiple processing elements.
Common CPU SIMD instruction sets are MMX, SSE-SSE4, AVX, AVX2, AVX512, NEON.
Currently, Pillow-SIMD can be [compiled](#installation) with SSE4 (default) or AVX2 support.
## Status
Pillow-SIMD project is production-ready.
The project is supported by Uploadcare, a SAAS for cloud-based image storing and processing.
[![Uploadcare][uploadcare.logo]][uploadcare.com]
Pillow-SIMD can be used in production. Pillow-SIMD has been operating on
[Uploadcare][uploadcare.com] servers for more than 1 year.
Uploadcare is SAAS for image storing and processing in the cloud
and the main sponsor of Pillow-SIMD project.
In fact, Uploadcare has been running Pillow-SIMD for about two years now.
Currently, following operations are accelerated:
The following image operations are currently SIMD-accelerated:
- Resize (convolution-based resampling): SSE4, AVX2
- Gaussian and box blur: SSE4
@ -50,14 +50,17 @@ Currently, following operations are accelerated:
- RGBA → RGBa (alpha premultiplication): SSE4, AVX2
- RGBa → RGBA (division by alpha): AVX2
See [CHANGES](CHANGES.SIMD.rst).
See [CHANGES](CHANGES.SIMD.rst) for more information.
## Benchmarks
The numbers in the table represent processed megapixels of source RGB 2560x1600
image per second. For example, if resize of 2560x1600 image is done
in 0.5 seconds, the result will be 8.2 Mpx/s.
In order for you to clearly assess the productivity of implementing SIMD computing into Pillow image processing,
we ran a number of benchmarks. The respective results can be found in the table below (the more — the better).
The numbers represent processing rates in megapixels per second (Mpx/s).
For instance, the rate at which a 2560x1600 RGB image is processed in 0.5 seconds equals to 8.2 Mpx/s.
Here is the list of libraries and their versions we've been up to during the benchmarks:
- Skia 53
- ImageMagick 6.9.3-8 Q8 x86_64
@ -83,89 +86,84 @@ Operation | Filter | IM | Pillow| SIMD SSE4| SIMD AVX2| Skia 53
| 100px | 0.34| 16.93| 35.53| |
### Some conclusion
### A brief conclusion
Pillow is always faster than ImageMagick. And Pillow-SIMD is faster
than Pillow in 4—5 times. In general, Pillow-SIMD with AVX2 always
**16-40 times faster** than ImageMagick and overperforms Skia,
high-speed graphics library used in Chromium, up to 2 times.
The results show that Pillow is always faster than ImageMagick,
Pillow-SIMD, in turn, is even faster than the original Pillow by the factor of 4-5.
In general, Pillow-SIMD with AVX2 is always **16 to 40 times faster** than
ImageMagick and outperforms Skia, the high-speed graphics library used in Chromium.
### Methodology
All tests were performed on Ubuntu 14.04 64-bit running on
Intel Core i5 4258U with AVX2 CPU on the single thread.
ImageMagick performance was measured with command-line tool `convert` with
`-verbose` and `-bench` arguments. I use command line because
I need to test the latest version and this is the easiest way to do that.
All operations produce exactly the same results.
All rates were measured using the following setup: Ubuntu 14.04 64-bit,
single-thread AVX2-enabled Intel i5 4258U CPU.
ImageMagick performance was measured with the `convert` command-line tool
followed by `-verbose` and `-bench` arguments.
Such approach was used because there's usually a need in testing
the latest software versions and command-line is the easiest way to do that.
All the routines involved with the testing procedure produced identic results.
Resizing filters compliance:
- PIL.Image.BILINEAR == Triangle
- PIL.Image.BICUBIC == Catrom
- PIL.Image.LANCZOS == Lanczos
In ImageMagick, the radius of gaussian blur is called sigma and the second
parameter is called radius. In fact, there should not be additional parameters
for *gaussian blur*, because if the radius is too small, this is *not*
gaussian blur anymore. And if the radius is big this does not give any
advantages but makes operation slower. For the test, I set the radius
to sigma × 2.5.
In ImageMagick, Gaussian blur operation invokes two parameters:
the first is called 'radius' and the second is called 'sigma'.
In fact, in order for the blur operation to be Gaussian, there should be no additional parameters.
When the radius value is too small the blur procedure ceases to be Gaussian and
if the value is excessively big the operation gets slowed down with zero benefits in exchange.
For the benchmarking purposes, the radius was set to `sigma × 2.5`.
Following script was used for testing:
Following script was used for the benchmarking procedure:
https://gist.github.com/homm/f9b8d8a84a57a7e51f9c2a5828e40e63
## Why Pillow itself is so fast
There are no cheats. High-quality resize and blur methods are used for all
benchmarks. Results are almost pixel-perfect. The difference is only effective
algorithms. Resampling in Pillow was rewritten in version 2.7 with
minimal usage of floating point numbers, precomputed coefficients and
cache-awareness transposition. This result was improved in 3.3 & 3.4 with
integer-only arithmetics and other optimizations.
No cheats involved. We've used identical high-quality resize and blur methods for the benchmark.
Outcomes produced by different libraries are in almost pixel-perfect agreement.
The difference in measured rates is only provided with the performance of every involved algorithm.
## Why Pillow-SIMD is even faster
Because of SIMD, of course. But this is not all. Heavy loops unrolling,
specific instructions, which not available for scalar.
Because of the SIMD computing, of course. But there's more to it:
heavy loops unrolling, specific instructions, which aren't available for scalar data types.
## Why do not contribute SIMD to the original Pillow
Well, that's not simple. First of all, Pillow supports a large number
of architectures, not only x86. But even for x86 platforms, Pillow is often
distributed via precompiled binaries. To integrate SIMD in precompiled binaries
we need to do runtime checks of CPU capabilities.
To compile the code with runtime checks we need to pass `-mavx2` option
to the compiler. But with that option compiller will inject AVX instructions
enev for SSE functions, because every SSE instruction has AVX equivalent.
Well, it's not that simple. First of all, the original Pillow supports
a large number of architectures, not just x86.
But even for x86 platforms, Pillow is often distributed via precompiled binaries.
In order for us to integrate SIMD into the precompiled binaries
we'd need to execute runtime CPU capabilities checks.
To compile the code this way we need to pass the `-mavx2` option to the compiler.
But with the option included, a compiler will inject AVX instructions even
for SSE functions (i.e. interchange them) since every SSE instruction has its AVX equivalent.
So there is no easy way to compile such library, especially with setuptools.
## Installation
In general, you need to do `pip install pillow-simd` as always and if you
are using SSE4-capable CPU everything should run smoothly.
Do not forget to remove original Pillow package first.
If you want the AVX2-enabled version, you need to pass the additional flag to C
compiler. The easiest way to do that is define `CC` variable while compilation.
If there's a copy of the original Pillow installed, it has to be removed first
with `$ pip uninstall -y pillow`.
The installation itself is simple just as running `$ pip install pillow-simd`,
and if you're using SSE4-capable CPU everything should run smoothly.
If you'd like to install the AVX2-enabled version,
you need to pass the additional flag to a C compiler.
The easiest way to do so is to define the `CC` variable during the compilation.
```bash
$ pip uninstall pillow
$ CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
```
## Contributing to Pillow-SIMD
Pillow-SIMD and Pillow are two separate projects.
Please submit bugs and improvements not related to SIMD to
[original Pillow][original-issues]. All bugs and fixes in Pillow
will appear in next Pillow-SIMD version automatically.
Please be aware that Pillow-SIMD and Pillow are two separate projects.
Please submit bugs and improvements not related to SIMD to the [original Pillow][original-issues].
All bugfixes to the original Pillow will then be transferred to the next Pillow-SIMD version automatically.
[original-docs]: http://pillow.readthedocs.io/