llvm-project

History

Michael Kruse fbde435517 [CodeGen] Use MapVector instead of DenseMap.

The map is iterated over when generating the values escaping the SCoP. The
indeterministic iteration order of DenseMap causes the output IR to change at
every compilation, adding noise to comparisons.

Replace DenseMap by a MapVector to ensure the same iteration order at every
compilation.

llvm-svn: 277832

2016-08-05 16:45:51 +00:00

cmake

Respect LLVM_INSTALL_TOOLCHAIN_ONLY.

2016-06-21 18:14:01 +00:00

docs

docs: Remove reference to PoCC

2016-05-17 19:44:16 +00:00

include/polly

[CodeGen] Use MapVector instead of DenseMap.

2016-08-05 16:45:51 +00:00

lib

[CodeGen] Use MapVector instead of DenseMap.

2016-08-05 16:45:51 +00:00

test

GPGPU: Sort dimension sizes of multi-dimensional shared memory arrays correctly

2016-08-05 08:27:24 +00:00

tools

GPGPU: Cache PTX kernels

2016-08-04 09:15:58 +00:00

utils

Revise polly-{update|check}-format targets

2015-09-14 16:59:50 +00:00

www

[WWW] Mark task as done and me as owner of some task

2016-05-02 11:21:30 +00:00

.arcconfig

Upgrade all the .arcconfigs to https.

2016-07-14 13:15:37 +00:00

.arclint

Adjusted arc linter config for modern version of arcanist

2015-08-12 09:01:16 +00:00

.gitattributes

gitattributes: .png and .txt are no text files

2013-07-28 09:05:20 +00:00

.gitignore

Add git patch files to .gitignore

2015-06-23 20:55:01 +00:00

CMakeLists.txt

GPGPU: Shorten ppcg include paths to avoid conflict with cuda.h

2016-07-15 07:50:36 +00:00

CREDITS.txt

Add myself to the credits

2014-08-10 03:37:29 +00:00

LICENSE.txt

2016-03-30 22:41:38 +00:00

README

Trivial change to the README, mainly to test commit access.

2012-10-09 04:59:42 +00:00

README

Polly - Polyhedral optimizations for LLVM
-----------------------------------------
http://polly.llvm.org/

Polly uses a mathematical representation, the polyhedral model, to represent and
transform loops and other control flow structures. Using an abstract
representation it is possible to reason about transformations in a more general
way and to use highly optimized linear programming libraries to figure out the
optimal loop structure. These transformations can be used to do constant
propagation through arrays, remove dead loop iterations, optimize loops for
cache locality, optimize arrays, apply advanced automatic parallelization, drive
vectorization, or they can be used to do software pipelining.