984d72ec65 Return the script type from Solver (Ben Woosley)
Pull request description:
Because false is synonymous with TX_NONSTANDARD, this conveys the same
information and makes the handling explicitly based on script type,
simplifying each call site.
Prior to this change it was common for the return value to be ignored, or for the
return value and TX_NONSTANDARD to be redundantly handled.
Tree-SHA512: 31864f856b8cb75f4b782d12678070e8b1cfe9665c6f57cfb25e7ac8bcea8a22f9a78d7c8cf0101c841f2a612400666fb91798bffe88de856e98b873703b0965
This commit removes the `CBloomFilter::CBloomFilter(const unsigned int, const double, const unsigned int)` constructor, which became obsolete with 086ee67.
Because false is synonymous with TX_NONSTANDARD, this conveys the same
information and makes the handling explicitly based on script type,
simplifying each call site.
Prior to this change it was common for the return value to be ignored,
or for the return value and TX_NONSTANDARD to be redundantly handled.
Replaces the slow modulo operation with a much faster 32bit multiplication & shift. This works
because the hash should be uniformly distributed between 0 and 2^32-1. This speeds up the benchmark
by a factor of about 1.3:
RollingBloom, 5, 1500000, 3.73733, 4.97569e-07, 4.99002e-07, 4.98372e-07 # before
RollingBloom, 5, 1500000, 2.86842, 3.81630e-07, 3.83730e-07, 3.82473e-07 # FastMod
Be aware that this changes the position of the bits that are toggled, so this should probably
not be used for CBloomFilter which is serialized.
On msvc14, the compiler error C4146 (unary minus operator applied to unsigned type, result still unsigned) had been occured.
Use '0 - x' styled formula instead of '-x' so as to fix the error.
Fixes newly initialized bloom filters being
constructed with isEmpty(false), which still
works but loses the possible speedup when
checking for key membership in an empty filter.
This patch changes the implementation from one that stores 16 2-bit integers
in one uint32_t's, to one that stores the first bit of 64 2-bit integers in
one uint64_t and the second bit in another. This allows for 450x faster
refreshing and 2.2x faster average speed.
For each 'bit' in the filter we really maintain 2 bits, which store either:
0: not set
1-3: set in generation N
After (nElements / 2) insertions, we switch to a new generation, and wipe
entries which already had the new generation number, effectively switching
from the last 1.5 * nElements set to the last 1.0 * nElements set.
This is 25% more space efficient than the previous implementation, and can
(at peak) store 1.5 times the requested amount of history (though only
1.0 times the requested history is guaranteed).
The existing unit tests should be sufficient.
While CBloomFilter is usually used with an explicitly set nTweak,
CRollingBloomFilter is only used internally. Requiring every caller to
set nTweak is error-prone and redundant; better to have the class handle
that for you with a high-quality randomness source.
Additionally when clearing the filter it makes sense to change nTweak as
well to recover from a bad setting, e.g. due to insufficient randomness
at initialization, so the clear() method is replaced by a reset() method
that sets a new, random, nTweak value.
For when you need to keep track of the last N items
you've seen, and can tolerate some false-positives.
Rebased-by: Pieter Wuille <pieter.wuille@gmail.com>
- Update comments in checkpoints to be doxygen compatible
- Update comments in checkqueue to be doxygen compatible
- Update coins to be doxygen compatible
- Fix comment typo in crypter.h
- Update licenses/copyright dates
Closes#5325#5184#5183#5182
Use misc methods of avoiding unnecesary header includes.
Replace int typedefs with int##_t from stdint.h.
Replace PRI64[xdu] with PRI[xdu]64 from inttypes.h.
Normalize QT_VERSION ifs where possible.
Resolve some indirect dependencies as direct ones.
Remove extern declarations from .cpp files.
This reduces a peer's ability to attack network resources by
using a full bloom filter, but without reducing the usability
of bloom filters. It sets a default match everything filter
for peers and it generalizes a prior optimization to
cover more cases.
