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# Conflicts:
# indra/llcommon/CMakeLists.txt
# indra/newview/llspatialpartition.cpp
# indra/newview/llviewergenericmessage.cpp
# indra/newview/llvoavatar.cpp
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This reverts commit 9d49edbc48d81f820870d43edb2c975beffa5485.
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# Conflicts:
# autobuild.xml
# indra/llcommon/tests/llleap_test.cpp
# indra/newview/viewer_manifest.py
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Add python 3.12 to FindPython search path
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Look for python 3.12 in the registry along with all the other versions.
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Elsewhere in CMake land, we reference PYTHONINTERP_FOUND and
PYTHON_EXECUTABLE, both of which are explicitly set by Python.cmake. We don't
seem to need the find_package(Python3 COMPONENTS Interpreter) call. Given that
we take some pains to be careful about which Windows Python interpreter we
find, this eliminates a wildcard.
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DRTVWR-577 (#232)
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to resolve conflicts in installer_template.nsi
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DRTVWR-577 (#232)
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branch
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With VS 2022 on Windows GitHub Actions runners, we can't build apr_suite at
all with the upstream .sln / .vcxproj files, so we had to switch to
"experimental" CMake support. However there's no CMakeLists.txt file for
apr-iconv, so the Windows package omits that library.
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The package doesn't include that any more.
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enable github actions mac nightly builds
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DRTVWR-559
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The trouble with debugLoggingEnabled() is that it locked mutexes and searched
maps every time that call was reached. LL_DEBUGS() has the same functionality
(albeit with idiosyncratic syntax) but performs expensive lookups only once
per session, caching the result in a local static variable.
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# Conflicts:
# indra/cmake/CMakeLists.txt
# indra/llcommon/llsdserialize.cpp
# indra/llcommon/llsdserialize.h
# indra/llcommon/tests/llleap_test.cpp
# indra/newview/llfilepicker.h
# indra/newview/llfilepicker_mac.h
# indra/newview/llfilepicker_mac.mm
# indra/newview/skins/default/xui/en/strings.xml
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# Conflicts:
# indra/cmake/CMakeLists.txt
# indra/newview/skins/default/xui/es/floater_tools.xml
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# Conflicts:
# indra/cmake/Copy3rdPartyLibs.cmake
# indra/cmake/FindOpenJPEG.cmake
# indra/cmake/OpenJPEG.cmake
# indra/integration_tests/llui_libtest/CMakeLists.txt
# indra/newview/CMakeLists.txt
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perfomance SL-18563
```
autobuild installables edit "tracy" url="https://automated-builds-secondlife-com.s3.amazonaws.com/ct2/110561/960415/tracy-v0.7.8.578230-darwin64-578230.tar.bz2" hash="70f31fa71ecb52bd092da52e27c3ff8c"
autobuild installables edit "tracy" url="https://automated-builds-secondlife-com.s3.amazonaws.com/ct2/110562/960424/tracy-v0.7.8.578230-windows-578230.tar.bz2" hash="1dc33422939adf015db85e96c5a8276e"
autobuild installables edit "tracy" url="https://automated-builds-secondlife-com.s3.amazonaws.com/ct2/110563/960429/tracy-v0.7.8.578230-windows64-578230.tar.bz2" hash="fcc6ecece2ecb65aa36500dfa9461fb3"
```
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# Conflicts:
# indra/llcommon/llsdserialize.cpp
# indra/llcommon/llsdserialize.h
# indra/newview/llfilepicker.h
# indra/newview/llfilepicker_mac.h
# indra/newview/llfilepicker_mac.mm
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# Conflicts:
# doc/contributions.txt
# indra/cmake/Copy3rdPartyLibs.cmake
# indra/cmake/FindOpenJPEG.cmake
# indra/cmake/OpenJPEG.cmake
# indra/integration_tests/llui_libtest/CMakeLists.txt
# indra/newview/CMakeLists.txt
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speed matters. (#64)
This commit adds the HBXX64 and HBXX128 classes for use as a drop-in
replacement for the slow LLMD5 hashing class, where speed matters and
backward compatibility (with standard hashing algorithms) and/or
cryptographic hashing qualities are not required.
It also replaces LLMD5 with HBXX* in a few existing hot (well, ok, just
"warm" for some) paths meeting the above requirements, while paving the way for
future use cases, such as in the DRTVWR-559 and sibling branches where the slow
LLMD5 is used (e.g. to hash materials and vertex buffer cache entries), and
could be use such a (way) faster algorithm with very significant benefits and
no negative impact.
Here is the comment I added in indra/llcommon/hbxx.h:
// HBXXH* classes are to be used where speed matters and cryptographic quality
// is not required (no "one-way" guarantee, though they are likely not worst in
// this respect than MD5 which got busted and is now considered too weak). The
// xxHash code they are built upon is vectorized and about 50 times faster than
// MD5. A 64 bits hash class is also provided for when 128 bits of entropy are
// not needed. The hashes collision rate is similar to MD5's.
// See https://github.com/Cyan4973/xxHash#readme for details.
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speed matters. (#64)
This commit adds the HBXX64 and HBXX128 classes for use as a drop-in
replacement for the slow LLMD5 hashing class, where speed matters and
backward compatibility (with standard hashing algorithms) and/or
cryptographic hashing qualities are not required.
It also replaces LLMD5 with HBXX* in a few existing hot (well, ok, just
"warm" for some) paths meeting the above requirements, while paving the way for
future use cases, such as in the DRTVWR-559 and sibling branches where the slow
LLMD5 is used (e.g. to hash materials and vertex buffer cache entries), and
could be use such a (way) faster algorithm with very significant benefits and
no negative impact.
Here is the comment I added in indra/llcommon/hbxx.h:
// HBXXH* classes are to be used where speed matters and cryptographic quality
// is not required (no "one-way" guarantee, though they are likely not worst in
// this respect than MD5 which got busted and is now considered too weak). The
// xxHash code they are built upon is vectorized and about 50 times faster than
// MD5. A 64 bits hash class is also provided for when 128 bits of entropy are
// not needed. The hashes collision rate is similar to MD5's.
// See https://github.com/Cyan4973/xxHash#readme for details.
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