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Author	SHA1	Message	Date
Diego Novillo	0de8cecb84	Propagation of profile samples through the CFG. This adds a propagation heuristic to convert instruction samples into branch weights. It implements a similar heuristic to the one implemented by Dehao Chen on GCC. The propagation proceeds in 3 phases: 1- Assignment of block weights. All the basic blocks in the function are initial assigned the same weight as their most frequently executed instruction. 2- Creation of equivalence classes. Since samples may be missing from blocks, we can fill in the gaps by setting the weights of all the blocks in the same equivalence class to the same weight. To compute the concept of equivalence, we use dominance and loop information. Two blocks B1 and B2 are in the same equivalence class if B1 dominates B2, B2 post-dominates B1 and both are in the same loop. 3- Propagation of block weights into edges. This uses a simple propagation heuristic. The following rules are applied to every block B in the CFG: - If B has a single predecessor/successor, then the weight of that edge is the weight of the block. - If all the edges are known except one, and the weight of the block is already known, the weight of the unknown edge will be the weight of the block minus the sum of all the known edges. If the sum of all the known edges is larger than B's weight, we set the unknown edge weight to zero. - If there is a self-referential edge, and the weight of the block is known, the weight for that edge is set to the weight of the block minus the weight of the other incoming edges to that block (if known). Since this propagation is not guaranteed to finalize for every CFG, we only allow it to proceed for a limited number of iterations (controlled by -sample-profile-max-propagate-iterations). It currently uses the same GCC default of 100. Before propagation starts, the pass builds (for each block) a list of unique predecessors and successors. This is necessary to handle identical edges in multiway branches. Since we visit all blocks and all edges of the CFG, it is cleaner to build these lists once at the start of the pass. Finally, the patch fixes the computation of relative line locations. The profiler emits lines relative to the function header. To discover it, we traverse the compilation unit looking for the subprogram corresponding to the function. The line number of that subprogram is the line where the function begins. That becomes line zero for all the relative locations. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@198972 91177308-0d34-0410-b5e6-96231b3b80d8	2014-01-10 23:23:46 +00:00
Manman Ren	bec50063a5	Debug Info: update testing cases to specify the debug info version number. We are going to drop debug info without a version number or with a different version number, to make sure we don't crash when we see bitcode files with different debug info metadata format. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@195504 91177308-0d34-0410-b5e6-96231b3b80d8	2013-11-22 21:49:45 +00:00
Diego Novillo	563b29f8db	SampleProfileLoader pass. Initial setup. This adds a new scalar pass that reads a file with samples generated by 'perf' during runtime. The samples read from the profile are incorporated and emmited as IR metadata reflecting that profile. The profile file is assumed to have been generated by an external profile source. The profile information is converted into IR metadata, which is later used by the analysis routines to estimate block frequencies, edge weights and other related data. External profile information files have no fixed format, each profiler is free to define its own. This includes both the on-disk representation of the profile and the kind of profile information stored in the file. A common kind of profile is based on sampling (e.g., perf), which essentially counts how many times each line of the program has been executed during the run. The SampleProfileLoader pass is organized as a scalar transformation. On startup, it reads the file given in -sample-profile-file to determine what kind of profile it contains. This file is assumed to contain profile information for the whole application. The profile data in the file is read and incorporated into the internal state of the corresponding profiler. To facilitate testing, I've organized the profilers to support two file formats: text and native. The native format is whatever on-disk representation the profiler wants to support, I think this will mostly be bitcode files, but it could be anything the profiler wants to support. To do this, every profiler must implement the SampleProfile::loadNative() function. The text format is mostly meant for debugging. Records are separated by newlines, but each profiler is free to interpret records as it sees fit. Profilers must implement the SampleProfile::loadText() function. Finally, the pass will call SampleProfile::emitAnnotations() for each function in the current translation unit. This function needs to translate the loaded profile into IR metadata, which the analyzer will later be able to use. This patch implements the first steps towards the above design. I've implemented a sample-based flat profiler. The format of the profile is fairly simplistic. Each sampled function contains a list of relative line locations (from the start of the function) together with a count representing how many samples were collected at that line during execution. I generate this profile using perf and a separate converter tool. Currently, I have only implemented a text format for these profiles. I am interested in initial feedback to the whole approach before I send the other parts of the implementation for review. This patch implements: - The SampleProfileLoader pass. - The base ExternalProfile class with the core interface. - A SampleProfile sub-class using the above interface. The profiler generates branch weight metadata on every branch instructions that matches the profiles. - A text loader class to assist the implementation of SampleProfile::loadText(). - Basic unit tests for the pass. Additionally, the patch uses profile information to compute branch weights based on instruction samples. This patch converts instruction samples into branch weights. It does a fairly simplistic conversion: Given a multi-way branch instruction, it calculates the weight of each branch based on the maximum sample count gathered from each target basic block. Note that this assignment of branch weights is somewhat lossy and can be misleading. If a basic block has more than one incoming branch, all the incoming branches will get the same weight. In reality, it may be that only one of them is the most heavily taken branch. I will adjust this assignment in subsequent patches. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194566 91177308-0d34-0410-b5e6-96231b3b80d8	2013-11-13 12:22:21 +00:00

Author

SHA1

Message

Date

Diego Novillo

0de8cecb84

Propagation of profile samples through the CFG.

This adds a propagation heuristic to convert instruction samples
into branch weights. It implements a similar heuristic to the one
implemented by Dehao Chen on GCC.

The propagation proceeds in 3 phases:

1- Assignment of block weights. All the basic blocks in the function
   are initial assigned the same weight as their most frequently
   executed instruction.

2- Creation of equivalence classes. Since samples may be missing from
   blocks, we can fill in the gaps by setting the weights of all the
   blocks in the same equivalence class to the same weight. To compute
   the concept of equivalence, we use dominance and loop information.
   Two blocks B1 and B2 are in the same equivalence class if B1
   dominates B2, B2 post-dominates B1 and both are in the same loop.

3- Propagation of block weights into edges. This uses a simple
   propagation heuristic. The following rules are applied to every
   block B in the CFG:

   - If B has a single predecessor/successor, then the weight
     of that edge is the weight of the block.

   - If all the edges are known except one, and the weight of the
     block is already known, the weight of the unknown edge will
     be the weight of the block minus the sum of all the known
     edges. If the sum of all the known edges is larger than B's weight,
     we set the unknown edge weight to zero.

   - If there is a self-referential edge, and the weight of the block is
     known, the weight for that edge is set to the weight of the block
     minus the weight of the other incoming edges to that block (if
     known).

Since this propagation is not guaranteed to finalize for every CFG, we
only allow it to proceed for a limited number of iterations (controlled
by -sample-profile-max-propagate-iterations). It currently uses the same
GCC default of 100.

Before propagation starts, the pass builds (for each block) a list of
unique predecessors and successors. This is necessary to handle
identical edges in multiway branches. Since we visit all blocks and all
edges of the CFG, it is cleaner to build these lists once at the start
of the pass.

Finally, the patch fixes the computation of relative line locations.
The profiler emits lines relative to the function header. To discover
it, we traverse the compilation unit looking for the subprogram
corresponding to the function. The line number of that subprogram is the
line where the function begins. That becomes line zero for all the
relative locations.

git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@198972 91177308-0d34-0410-b5e6-96231b3b80d8

2014-01-10 23:23:46 +00:00

Manman Ren

bec50063a5

Debug Info: update testing cases to specify the debug info version number.

We are going to drop debug info without a version number or with a different
version number, to make sure we don't crash when we see bitcode files with
different debug info metadata format.


git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@195504 91177308-0d34-0410-b5e6-96231b3b80d8

2013-11-22 21:49:45 +00:00

Diego Novillo

563b29f8db

SampleProfileLoader pass. Initial setup.

This adds a new scalar pass that reads a file with samples generated
by 'perf' during runtime. The samples read from the profile are
incorporated and emmited as IR metadata reflecting that profile.

The profile file is assumed to have been generated by an external
profile source. The profile information is converted into IR metadata,
which is later used by the analysis routines to estimate block
frequencies, edge weights and other related data.

External profile information files have no fixed format, each profiler
is free to define its own. This includes both the on-disk representation
of the profile and the kind of profile information stored in the file.
A common kind of profile is based on sampling (e.g., perf), which
essentially counts how many times each line of the program has been
executed during the run.

The SampleProfileLoader pass is organized as a scalar transformation.
On startup, it reads the file given in -sample-profile-file to
determine what kind of profile it contains.  This file is assumed to
contain profile information for the whole application. The profile
data in the file is read and incorporated into the internal state of
the corresponding profiler.

To facilitate testing, I've organized the profilers to support two file
formats: text and native. The native format is whatever on-disk
representation the profiler wants to support, I think this will mostly
be bitcode files, but it could be anything the profiler wants to
support. To do this, every profiler must implement the
SampleProfile::loadNative() function.

The text format is mostly meant for debugging. Records are separated by
newlines, but each profiler is free to interpret records as it sees fit.
Profilers must implement the SampleProfile::loadText() function.

Finally, the pass will call SampleProfile::emitAnnotations() for each
function in the current translation unit. This function needs to
translate the loaded profile into IR metadata, which the analyzer will
later be able to use.

This patch implements the first steps towards the above design. I've
implemented a sample-based flat profiler. The format of the profile is
fairly simplistic. Each sampled function contains a list of relative
line locations (from the start of the function) together with a count
representing how many samples were collected at that line during
execution. I generate this profile using perf and a separate converter
tool.

Currently, I have only implemented a text format for these profiles. I
am interested in initial feedback to the whole approach before I send
the other parts of the implementation for review.

This patch implements:

- The SampleProfileLoader pass.
- The base ExternalProfile class with the core interface.
- A SampleProfile sub-class using the above interface. The profiler
  generates branch weight metadata on every branch instructions that
  matches the profiles.
- A text loader class to assist the implementation of
  SampleProfile::loadText().
- Basic unit tests for the pass.

Additionally, the patch uses profile information to compute branch
weights based on instruction samples.

This patch converts instruction samples into branch weights. It
does a fairly simplistic conversion:

Given a multi-way branch instruction, it calculates the weight of
each branch based on the maximum sample count gathered from each
target basic block.

Note that this assignment of branch weights is somewhat lossy and can be
misleading. If a basic block has more than one incoming branch, all the
incoming branches will get the same weight. In reality, it may be that
only one of them is the most heavily taken branch.

I will adjust this assignment in subsequent patches.

git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194566 91177308-0d34-0410-b5e6-96231b3b80d8

2013-11-13 12:22:21 +00:00

3 Commits