removed "cached" structure.
prices are now saved in the optimal table.
Primarily done for simplification.
Might improve speed by a little.
But actually, and surprisingly, also improves ratio in some circumstances.
recent experienced showed that
default distribution table for offset
can get it wrong pretty quickly with the nb of symbols,
while it remains a reasonable choice much longer for lengths symbols.
Changed the formula,
so that dynamic threshold is now 32 symbols for offsets.
It remains at 64 symbols for lengths.
Detection based on defaultNormLog
zstd rejects blocks which do not compress by at least a certain amount.
In which case, such block is simply emitted uncompressed (even if a little bit of compression could be achieved).
This is better for decompression speed, hence for energy.
The logic is controlled by ZSTD_minGain().
The rule is applied uniformly, at all compression levels.
This change makes btultra accepts blocks with poor compression ratios.
We presume that users of btultra mode prefers compression ratio over some decompress speed gains.
The threshold for minimum gain is lowered for btultra
from s>>6 (~1.5% minimum gain)
to s>>7 (~0.8% minimum gain).
This is a prudent change.
Not sure if it's large enough.
ensure that, when frequency[symbol]==0,
result is (tableLog + 1) bits
with both upper-bit and fractional-bit estimates.
Also : enable BIT_DEBUG in /tests
Work around bug in zstd decoder
Pull request #1144 exercised a new path in the zstd decoder that proved to
be buggy. Avoid the extremely rare bug by emitting an uncompressed block.
This edge case is only possible with the new optimal encoding selector,
since before zstd would always choose `set_basic` for small numbers of
sequences.
Fix `FSE_readNCount()` to support buffers < 4 bytes.
Credit to OSS-Fuzz
Estimate the cost for using FSE modes `set_basic`, `set_compressed`, and
`set_repeat`, and select the one with the lowest cost.
* The cost of `set_basic` is computed using the cross-entropy cost
function `ZSTD_crossEntropyCost()`, using the normalized default count
and the count.
* The cost of `set_repeat` is computed using `FSE_bitCost()`. We check the
previous table to see if it is able to represent the distribution.
* The cost of `set_compressed` is computed with the entropy cost function
`ZSTD_entropyCost()`, together with the cost of writing the normalized
count `ZSTD_NCountCost()`.
The cover algorithm selects one segment per epoch, and it selects the epoch
size such that `epochs * segmentSize ~= dictSize`. Selecting less epochs
gives the algorithm more candidates to choose from for each segment it
selects, and then it will loop back to the first epoch when it hits the
last one.
The trade off is that now it takes longer to select each segment, since it
has to look at more data before making a choice.
I benchmarked on the following data sets using this command:
```sh
$ZSTD -T0 -3 --train-cover=d=8,steps=256 $DIR -r -o dict && $ZSTD -3 -D dict -rc $DIR | wc -c
```
| Data set | k (approx) | Before | After | % difference |
|--------------|------------|----------|----------|--------------|
| GitHub | ~1000 | 738138 | 746610 | +1.14% |
| hg-changelog | ~90 | 4295156 | 4285336 | -0.23% |
| hg-commands | ~500 | 1095580 | 1079814 | -1.44% |
| hg-manifest | ~400 | 16559892 | 16504346 | -0.34% |
There is some noise in the measurements, since small changes to `k` can
have large differences, which is why I'm using `steps=256`, to try to
minimize the noise. However, the GitHub data set still has some noise.
If I run the GitHub data set on my Mac, which presumably lists directory
entries in a different order, so the dictionary builder sees the files in
a different order, or I use `steps=1024` I see these results.
| Run | Before | After | % difference |
|------------|--------|--------|--------------|
| steps=1024 | 738138 | 734470 | -0.50% |
| MacBook | 738451 | 737132 | -0.18% |
Question: Should we expose this as a parameter? I don't think it is
necessary. Someone might want to turn it up to exchange a much longer
dictionary building time in exchange for a slightly better dictionary.
I tested `2`, `4`, and `16`, and `4` got most of the benefit of `16`
with a faster running time.
