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Previously the dataflow propagation algorithm would calculate the ACP
live-in and -out sets in a two-pass fixed-point algorithm. The first
pass would update the live-out sets of all basic blocks of the program
based on their live-in sets, while the second pass would update the
live-in sets based on the live-out sets. This is incredibly
inefficient in the typical case where the CFG of the program is
approximately acyclic, because it can take up to 2*n passes for an ACP
entry introduced at the top of the program to reach the bottom (where
n is the number of basic blocks in the program), until which point the
algorithm won't be able to reach a fixed point.
The same effect can be achieved in a single pass by computing the
live-in and -out sets in lock-step, because that makes sure that
processing of any basic block will pick up the updated live-out sets
of the lexically preceding blocks. This gives the dataflow
propagation algorithm effectively O(n) run-time instead of O(n^2) in
the acyclic case.
The time spent in dataflow propagation is reduced by 30x in the
GLES31.functional.ssbo.layout.random.all_shared_buffer.5 dEQP
test-case on my CHV system (the improvement is likely to be of the
same order of magnitude on other platforms). This more than reverses
an apparent run-time regression in this test-case from my previous
copy-propagation undefined-value handling patch, which was ultimately
caused by the additional work introduced in that commit to account for
undefined values being multiplied by a huge quadratic factor.
According to Chad this test was failing on CHV due to a 30s time-out
imposed by the Android CTS (this was the case regardless of my
undefined-value handling patch, even though my patch substantially
exacerbated the issue). On my CHV system this patch reduces the
overall run-time of the test by approximately 12x, getting us to
around 13s, well below the time-out.
v2: Initialize live-out set to the universal set to avoid rather
pessimistic dataflow estimation in shaders with cycles (Addresses
performance regression reported by Eero in GpuTest Piano).
Performance numbers given above still apply. No shader-db changes
with respect to master.
Bugzilla: https://bugs.freedesktop.org/show_bug.cgi?id=104271
Reported-by: Chad Versace <chadversary@chromium.org>
Reviewed-by: Ian Romanick <ian.d.romanick@intel.com>
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File: docs/README.WIN32 Last updated: 21 June 2013 Quick Start ----- ----- Windows drivers are build with SCons. Makefiles or Visual Studio projects are no longer shipped or supported. Run scons libgl-gdi to build gallium based GDI driver. This will work both with MSVS or Mingw. Windows Drivers ------- ------- At this time, only the gallium GDI driver is known to work. Source code also exists in the tree for other drivers in src/mesa/drivers/windows, but the status of this code is unknown. Recipe ------ Building on windows requires several open-source packages. These are steps that work as of this writing. - install python 2.7 - install scons (latest) - install mingw, flex, and bison - install pywin32 from here: http://www.lfd.uci.edu/~gohlke/pythonlibs get pywin32-218.4.win-amd64-py2.7.exe - install git - download mesa from git see https://www.mesa3d.org/repository.html - run scons General ------- After building, you can copy the above DLL files to a place in your PATH such as $SystemRoot/SYSTEM32. If you don't like putting things in a system directory, place them in the same directory as the executable(s). Be careful about accidentially overwriting files of the same name in the SYSTEM32 directory. The DLL files are built so that the external entry points use the stdcall calling convention. Static LIB files are not built. The LIB files that are built with are the linker import files associated with the DLL files. The si-glu sources are used to build the GLU libs. This was done mainly to get the better tessellator code. If you have a Windows-related build problem or question, please post to the mesa-dev or mesa-users list.