# Compression Filters¶

Filters to compress the data in snapshots can be applied to reduce the disk footprint of the datasets. The filters provided by SWIFT are filters natively provided by HDF5, implying that the library will automatically and transparently apply the reverse filter when reading the data stored on disk. They can be applied in combination with, or instead of, the lossless gzip compression filter.

**These compression filters are lossy, meaning that they modify the
data written to disk**

*The filters will reduce the accuracy of the data stored. No check is
made inside SWIFT to verify that the applied filters make sense. Poor
choices can lead to all the values of a given array reduced to 0, Inf,
or to have lost too much accuracy to be useful. The onus is entirely
on the user to choose wisely how they want to compress their data.*

The filters are not applied when using parallel-hdf5.

The available filters are listed below.

## Scaling filters for floating-point numbers¶

The D-scale filters can be used to round floating-point values to a fixed
*absolute* accuracy.

They start by computing the minimum of an array that is then deducted from all the values. The array is then multiplied by \(10^n\) and truncated to the nearest integer. These integers are stored with the minimal number of bits required to store the values. That process is reversed when reading the data.

For an array of values

1.2345 | -0.1267 | 0.0897 |

and \(n=2\), we get stored on disk (but hidden to the user):

136 | 0 | 22 |

This can be stored with 8 bits instead of the 32 bits needed to store the original values in floating-point precision, realising a gain of 4x.

When reading the values (for example via `h5py`

or `swiftsimio`

), that
process is transparently reversed and we get:

1.2333 | -0.1267 | 0.0933 |

Using a scaling of \(n=2\) hence rounds the values to two digits after the decimal point.

SWIFT implements 4 variants of this filter:

`DScale1`

scales by \(10^1\)`DScale2`

scales by \(10^2\)`DScale3`

scales by \(10^3\)`DScale6`

scales by \(10^6\)

An example application is to store the positions with `pc`

accuracy in
simulations that use `Mpc`

as their base unit by using the `DScale6`

filter.

The compression rate of these filters depends on the data. On an
EAGLE-like simulation, compressing the positions from `Mpc`

to `pc`

(via
`Dscale6`

) leads to rate of around 2.2x.

## Modified floating-point representation filters¶

These filters modify the bit-representation of floating point numbers
to get a different *relative* accuracy.

In brief, floating point (FP) numbers are represented in memory as \((\pm 1)\times a \times 2^b\) with a certain number of bits used to store each of \(a\) (the mantissa) and \(b\) (the exponent) as well as one bit for the overall sign [1]. For example, a standard 4-bytes float uses 23 bits for \(a\) and 8 bits for \(b\). The number of bits in the exponent mainly drives the range of values that can be represented whilst the number of bits in the mantissa drives the relative accuracy of the numbers.

Converting to the more familiar decimal notation, we get that the number of decimal digits that are correctly represented is \(\log_{10}(2^{n(a)+1})\), with \(n(x)\) the number of bits in \(x\). The range of positive numbers that can be represented is given by \([2^{-2^{n(b)-1}+2}, 2^{2^{n(b)-1}}]\). For a standard float, this gives a relative accuracy of \(7.2\) decimal digits and a representable range of \([1.17\times 10^{-38}, 3.40\times 10^{38}]\). Numbers above the upper limit are labeled as Inf and below this range they default to zero.

The filters in this category change the number of bits in the mantissa and
exponent. When reading the values (for example via `h5py`

or
`swiftsimio`

) the numbers are transparently restored to regular `float`

but with 0s in the bits of the mantissa that were not stored on disk, hence
changing the result from what was stored originally before compression.

These filters offer a fixed compression ratio and a fixed relative accuracy. The available options in SWIFT are:

Filter name | \(n(a)\) | \(n(b)\) | Accuracy | Range | Compression ratio |
---|---|---|---|---|---|

No filter | 23 | 8 | 7.22 digits | \([1.17\times 10^{-38}, 3.40\times 10^{38}]\) | — |

`FMantissa13` |
13 | 8 | 4.21 digits | \([1.17\times 10^{-38}, 3.40\times 10^{38}]\) | 1.45x |

`FMantissa9` |
9 | 8 | 3.01 digits | \([1.17\times 10^{-38}, 3.40\times 10^{38}]\) | 1.78x |

`BFloat16` |
7 | 8 | 2.41 digits | \([1.17\times 10^{-38}, 3.40\times 10^{38}]\) | 2x |

`HalfFloat` |
10 | 5 | 3.31 digits | \([6.1\times 10^{-5}, 6.5\times 10^{4}]\) | 2x |

The accuracy given in the table corresponds to the number of decimal digits that can be correctly stored. The “no filter” row is displayed for comparison purposes.

The first two filters are useful to keep the same range as a standard float but with a reduced accuracy of 3 or 4 decimal digits. The last two are the two standard reduced precision options fitting within 16 bits: one with a much reduced relative accuracy and one with a much reduced representable range.

An example application is to store the densities with the `FMantissa9`

filter as we rarely need more than 3 decimal digits of accuracy for this
quantity.

[1] | Note that the representation in memory of FP numbers is more complicated than this simple picture. See for instance this Wikipedia article. |