[llvm-branch-commits] [llvm-branch] r246053 - Polly subproject release notes
Hans Wennborg via llvm-branch-commits
llvm-branch-commits at lists.llvm.org
Wed Aug 26 11:58:16 PDT 2015
Date: Wed Aug 26 13:58:15 2015
New Revision: 246053
Polly subproject release notes
By Tobias Grosser!
--- llvm/branches/release_37/docs/ReleaseNotes.rst (original)
+++ llvm/branches/release_37/docs/ReleaseNotes.rst Wed Aug 26 13:58:15 2015
@@ -246,6 +246,114 @@ Changes to the JIT APIs
encouraged to try ORC out for their projects. (A good place to start is the
new ORC tutorials under llvm/examples/kaleidoscope/orc).
+Sub-project Status Update
+In addition to the core LLVM 3.7 distribution of production-quality compiler
+infrastructure, the LLVM project includes sub-projects that use the LLVM core
+and share the same distribution license. This section provides updates on these
+Polly - The Polyhedral Loop Optimizer in LLVM
+`Polly <http://polly.llvm.org>`_ is a polyhedral loop optimization
+infrastructure that provides data-locality optimizations to LLVM-based
+compilers. When compiled as part of clang or loaded as a module into clang,
+it can perform loop optimizations such as tiling, loop fusion or outer-loop
+vectorization. As a generic loop optimization infrastructure it allows
+developers to get a per-loop-iteration model of a loop nest on which detailed
+analysis and transformations can be performed.
+Changes since the last release:
+* isl imported into Polly distribution
+`isl <http://repo.or.cz/w/isl.git>`_, the math library Polly uses, has been
+imported into the source code repository of Polly and is now distributed as part
+of Polly. As this was the last external library dependency of Polly, Polly can
+now be compiled right after checking out the Polly source code without the need
+for any additional libraries to be pre-installed.
+* Small integer optimization of isl
+The MIT licensed imath backend using in `isl <http://repo.or.cz/w/isl.git>`_ for
+arbitrary width integer computations has been optimized to use native integer
+operations for the common case where the operands of a computation fit into 32
+bit and to only fall back to large arbitrary precision integers for the
+remaining cases. This optimization has greatly improved the compile-time
+performance of Polly, both due to faster native operations also due to a
+reduction in malloc traffic and pointer indirections. As a result, computations
+that use arbitrary precision integers heavily have been speed up by almost 6x.
+As a result, the compile-time of Polly on the Polybench test kernels in the LNT
+suite has been reduced by 20% on average with compile time reductions between
+* Schedule Trees
+Polly now uses internally so-called > Schedule Trees < to model the loop
+structure it optimizes. Schedule trees are an easy to understand tree structure
+that describes a loop nest using integer constraint sets to keep track of
+execution constraints. It allows the developer to use per-tree-node operations
+to modify the loop tree. Programatic analysis that work on the schedule tree
+(e.g., as dependence analysis) also show a visible speedup as they can exploit
+the tree structure of the schedule and need to fall back to ILP based
+optimization problems less often. Section 6 of `Polyhedral AST generation is
+more than scanning polyhedra
+<http://www.grosser.es/#pub-polyhedral-AST-generation>`_ gives a detailed
+explanation of this schedule trees.
+* Scalar and PHI node modeling - Polly as an analysis
+Polly now requires almost no preprocessing to analyse LLVM-IR, which makes it
+easier to use Polly as a pure analysis pass e.g. to provide more precise
+dependence information to non-polyhedral transformation passes. Originally,
+Polly required the input LLVM-IR to be preprocessed such that all scalar and
+PHI-node dependences are translated to in-memory operations. Since this release,
+Polly has full support for scalar and PHI node dependences and requires no
+scalar-to-memory translation for such kind of dependences.
+* Modeling of modulo and non-affine conditions
+Polly can now supports modulo operations such as A[t%2][i][j] as they appear
+often in stencil computations and also allows data-dependent conditional
+branches as they result e.g. from ternary conditions ala A[i] > 255 ? 255 :
+Polly now support the analysis of manually linearized multi-dimensional arrays
+as they result form macros such as
+"#define 2DARRAY(A,i,j) (A.data[(i) * A.size + (j)]". Similar constructs appear
+in old C code written before C99, C++ code such as boost::ublas, LLVM exported
+from Julia, Matlab generated code and many others. Our work titled
+`Optimistic Delinearization of Parametrically Sized Arrays
+<http://www.grosser.es/#pub-optimistic-delinerization>`_ gives details.
+* Compile time improvements
+Pratik Bahtu worked on compile-time performance tuning of Polly. His work
+together with the support for schedule trees and the small integer optimization
+in isl notably reduced the compile time.
+* Increased compute timeouts
+As Polly's compile time has been notabily improved, we were able to increase
+the compile time saveguards in Polly. As a result, the default configuration
+of Polly can now analyze larger loop nests without running into compile time
+* Export Debug Locations via JSCoP file
+Polly's JSCoP import/export format gained support for debug locations that show
+to the user the source code location of detected scops.
+* Improved windows support
+The compilation of Polly on windows using cmake has been improved and several
+visual studio build issues have been addressed.
+* Many bug fixes
External Open Source Projects Using LLVM 3.7
More information about the llvm-branch-commits