printInst prints a branch/call instruction as `b offset` (there are many
variants on various targets) instead of `b address`.
It is a convention to use address instead of offset in most external
symbolizers/disassemblers. This difference makes `llvm-objdump -d`
output unsatisfactory.
Add `uint64_t Address` to printInst(), so that it can pass the argument to
printInstruction(). `raw_ostream &OS` is moved to the last to be
consistent with other print* methods.
The next step is to pass `Address` to printInstruction() (generated by
tablegen from the instruction set description). We can gradually migrate
targets to print addresses instead of offsets.
In any case, downstream projects which don't know `Address` can pass 0 as
the argument.
Reviewed By: jhenderson
Differential Revision: https://reviews.llvm.org/D72172
Summary:
As disscused in https://bugs.llvm.org/show_bug.cgi?id=43219,
i believe it may be somewhat useful to show //some// aggregates
over all the sea of statistics provided.
Example:
```
Average Wait times (based on the timeline view):
[0]: Executions
[1]: Average time spent waiting in a scheduler's queue
[2]: Average time spent waiting in a scheduler's queue while ready
[3]: Average time elapsed from WB until retire stage
[0] [1] [2] [3]
0. 3 1.0 1.0 4.7 vmulps %xmm0, %xmm1, %xmm2
1. 3 2.7 0.0 2.3 vhaddps %xmm2, %xmm2, %xmm3
2. 3 6.0 0.0 0.0 vhaddps %xmm3, %xmm3, %xmm4
3 3.2 0.3 2.3 <total>
```
I.e. we average the averages.
Reviewers: andreadb, mattd, RKSimon
Reviewed By: andreadb
Subscribers: gbedwell, arphaman, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D68714
llvm-svn: 374361
This patch introduces a cut-off threshold for dependency edge frequences with
the goal of simplifying the critical sequence computation. This patch also
removes the cost normalization for loop carried dependencies. We didn't really
need to artificially amplify the cost of loop-carried dependencies since it is
already computed as the integral over time of the delay (in cycle).
In the absence of backend stalls there is no need for computing a critical
sequence. With this patch we early exit from the critical sequence computation
if no bottleneck was reported during the simulation.
llvm-svn: 372337
Flag -show-encoding enables the printing of instruction encodings as part of the
the instruction info view.
Example (with flags -mtriple=x86_64-- -mcpu=btver2):
Instruction Info:
[1]: #uOps
[2]: Latency
[3]: RThroughput
[4]: MayLoad
[5]: MayStore
[6]: HasSideEffects (U)
[7]: Encoding Size
[1] [2] [3] [4] [5] [6] [7] Encodings: Instructions:
1 2 1.00 4 c5 f0 59 d0 vmulps %xmm0, %xmm1, %xmm2
1 4 1.00 4 c5 eb 7c da vhaddps %xmm2, %xmm2, %xmm3
1 4 1.00 4 c5 e3 7c e3 vhaddps %xmm3, %xmm3, %xmm4
In this example, column Encoding Size is the size in bytes of the instruction
encoding. Column Encodings reports the actual instruction encodings as byte
sequences in hex (objdump style).
The computation of encodings is done by a utility class named mca::CodeEmitter.
In future, I plan to expose the CodeEmitter to the instruction builder, so that
information about instruction encoding sizes can be used by the simulator. That
would be a first step towards simulating the throughput from the decoders in the
hardware frontend.
Differential Revision: https://reviews.llvm.org/D65948
llvm-svn: 368432
1. raw_ostream supports ANSI colors so that you can write messages to
the termina with colors. Previously, in order to change and reset
color, you had to call `changeColor` and `resetColor` functions,
respectively.
So, if you print out "error: " in red, for example, you had to do
something like this:
OS.changeColor(raw_ostream::RED);
OS << "error: ";
OS.resetColor();
With this patch, you can write the same code as follows:
OS << raw_ostream::RED << "error: " << raw_ostream::RESET;
2. Add a boolean flag to raw_ostream so that you can disable colored
output. If you disable colors, changeColor, operator<<(Color),
resetColor and other color-related functions have no effect.
Most LLVM tools automatically prints out messages using colors, and
you can disable it by passing a flag such as `--disable-colors`.
This new flag makes it easy to write code that works that way.
Differential Revision: https://reviews.llvm.org/D65564
llvm-svn: 367649
This patch teaches the bottleneck analysis how to identify and print the most
expensive sequence of instructions according to the simulation. Fixes PR37494.
The goal is to help users identify the sequence of instruction which is most
critical for performance.
A dependency graph is internally used by the bottleneck analysis to describe
data dependencies and processor resource interferences between instructions.
There is one node in the graph for every instruction in the input assembly
sequence. The number of nodes in the graph is independent from the number of
iterations simulated by the tool. It means that a single node of the graph
represents all the possible instances of a same instruction contributed by the
simulated iterations.
Edges are dynamically "discovered" by the bottleneck analysis by observing
instruction state transitions and "backend pressure increase" events generated
by the Execute stage. Information from the events is used to identify critical
dependencies, and materialize edges in the graph. A dependency edge is uniquely
identified by a pair of node identifiers plus an instance of struct
DependencyEdge::Dependency (which provides more details about the actual
dependency kind).
The bottleneck analysis internally ranks dependency edges based on their impact
on the runtime (see field DependencyEdge::Dependency::Cost). To this end, each
edge of the graph has an associated cost. By default, the cost of an edge is a
function of its latency (in cycles). In practice, the cost of an edge is also a
function of the number of cycles where the dependency has been seen as
'contributing to backend pressure increases'. The idea is that the higher the
cost of an edge, the higher is the impact of the dependency on performance. To
put it in another way, the cost of an edge is a measure of criticality for
performance.
Note how a same edge may be found in multiple iteration of the simulated loop.
The logic that adds new edges to the graph checks if an equivalent dependency
already exists (duplicate edges are not allowed). If an equivalent dependency
edge is found, field DependencyEdge::Frequency of that edge is incremented by
one, and the new cost is cumulatively added to the existing edge cost.
At the end of simulation, costs are propagated to nodes through the edges of the
graph. The goal is to identify a critical sequence from a node of the root-set
(composed by node of the graph with no predecessors) to a 'sink node' with no
successors. Note that the graph is intentionally kept acyclic to minimize the
complexity of the critical sequence computation algorithm (complexity is
currently linear in the number of nodes in the graph).
The critical path is finally computed as a sequence of dependency edges. For
edges describing processor resource interferences, the view also prints a
so-called "interference probability" value (by dividing field
DependencyEdge::Frequency by the total number of iterations).
Examples of critical sequence computations can be found in tests added/modified
by this patch.
On output streams that support colored output, instructions from the critical
sequence are rendered with a different color.
Strictly speaking the analysis conducted by the bottleneck analysis view is not
a critical path analysis. The cost of an edge doesn't only depend on the
dependency latency. More importantly, the cost of a same edge may be computed
differently by different iterations.
The number of dependencies is discovered dynamically based on the events
generated by the simulator. However, their number is not fixed. This is
especially true for edges that model processor resource interferences; an
interference may not occur in every iteration. For that reason, it makes sense
to also print out a "probability of interference".
By construction, the accuracy of this analysis (as always) is strongly dependent
on the simulation (and therefore the quality of the information available in the
scheduling model).
That being said, the critical sequence effectively identifies a performance
criticality. Instructions from that sequence are expected to have a very big
impact on performance. So, users can take advantage of this information to focus
their attention on specific interactions between instructions.
In my experience, it works quite well in practice, and produces useful
output (in a reasonable amount time).
Differential Revision: https://reviews.llvm.org/D63543
llvm-svn: 364045
This patch slightly refactors data structures internally used by the bottleneck
analysis to track data and resource dependencies.
This patch also updates methods used to print out information about dependency
edges when in debug mode.
This is the last of a sequence of commits done in preparation for an upcoming
patch that fixes PR37494. No functional change intended.
llvm-svn: 363677
The resource pressure distribution computation is now delegated by class
BottleneckAnalysis to an instance of class PressureTracker.
Class PressureTracker is also responsible for:
- tracking users of processor resource units.
- tracking the number of delay cycles caused by increases in backpressure.
BottleneckAnalysis internally initializes a dependency graph. Each nodes
represents an instruction in the input code sequence. Edges of the dependency
graph are critical register/memory/resource dependencies. Dependencies are only
added to the graph if they are seen as critical by backend pressure events.
The DependencyGraph is currently unused. It is possible to print the dependency
graph (see method DependencyGraph::dump()) for debugging purposes.
The long term goal is to use the information stored by the dependency graph in
order to do critical path computation.
llvm-svn: 362246
It makes more sense to print out the number of micro opcodes that are issued
every cycle rather than the number of instructions issued per cycle.
This behavior is also consistent with the dispatch-stats: numbers from the two
views can now be easily compared.
llvm-svn: 357919
Found by inspection when looking at the debug output of MCA.
This problem was latent, and none of the upstream models were affected by it.
No functional change intended.
llvm-svn: 357000
Summary:
Since bottleneck hints are enabled via user request, it can be
confusing if no bottleneck information is presented. Such is the
case when no bottlenecks are identified. This patch emits a message
in that case.
Reviewers: andreadb
Reviewed By: andreadb
Subscribers: tschuett, gbedwell, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D59098
llvm-svn: 355628
This patch adds a new flag named -bottleneck-analysis to print out information
about throughput bottlenecks.
MCA knows how to identify and classify dynamic dispatch stalls. However, it
doesn't know how to analyze and highlight kernel bottlenecks. The goal of this
patch is to teach MCA how to correlate increases in backend pressure to backend
stalls (and therefore, the loss of throughput).
From a Scheduler point of view, backend pressure is a function of the scheduler
buffer usage (i.e. how the number of uOps in the scheduler buffers changes over
time). Backend pressure increases (or decreases) when there is a mismatch
between the number of opcodes dispatched, and the number of opcodes issued in
the same cycle. Since buffer resources are limited, continuous increases in
backend pressure would eventually leads to dispatch stalls. So, there is a
strong correlation between dispatch stalls, and how backpressure changed over
time.
This patch teaches how to identify situations where backend pressure increases
due to:
- unavailable pipeline resources.
- data dependencies.
Data dependencies may delay execution of instructions and therefore increase the
time that uOps have to spend in the scheduler buffers. That often translates to
an increase in backend pressure which may eventually lead to a bottleneck.
Contention on pipeline resources may also delay execution of instructions, and
lead to a temporary increase in backend pressure.
Internally, the Scheduler classifies instructions based on whether register /
memory operands are available or not.
An instruction is marked as "ready to execute" only if data dependencies are
fully resolved.
Every cycle, the Scheduler attempts to execute all instructions that are ready
to execute. If an instruction cannot execute because of unavailable pipeline
resources, then the Scheduler internally updates a BusyResourceUnits mask with
the ID of each unavailable resource.
ExecuteStage is responsible for tracking changes in backend pressure. If backend
pressure increases during a cycle because of contention on pipeline resources,
then ExecuteStage sends a "backend pressure" event to the listeners.
That event would contain information about instructions delayed by resource
pressure, as well as the BusyResourceUnits mask.
Note that ExecuteStage also knows how to identify situations where backpressure
increased because of delays introduced by data dependencies.
The SummaryView observes "backend pressure" events and prints out a "bottleneck
report".
Example of bottleneck report:
```
Cycles with backend pressure increase [ 99.89% ]
Throughput Bottlenecks:
Resource Pressure [ 0.00% ]
Data Dependencies: [ 99.89% ]
- Register Dependencies [ 0.00% ]
- Memory Dependencies [ 99.89% ]
```
A bottleneck report is printed out only if increases in backend pressure
eventually caused backend stalls.
About the time complexity:
Time complexity is linear in the number of instructions in the
Scheduler::PendingSet.
The average slowdown tends to be in the range of ~5-6%.
For memory intensive kernels, the slowdown can be significant if flag
-noalias=false is specified. In the worst case scenario I have observed a
slowdown of ~30% when flag -noalias=false was specified.
We can definitely recover part of that slowdown if we optimize class LSUnit (by
doing extra bookkeeping to speedup queries). For now, this new analysis is
disabled by default, and it can be enabled via flag -bottleneck-analysis. Users
of MCA as a library can enable the generation of pressure events through the
constructor of ExecuteStage.
This patch partially addresses https://bugs.llvm.org/show_bug.cgi?id=37494
Differential Revision: https://reviews.llvm.org/D58728
llvm-svn: 355308
This patch adds a lookup table to speed up resource queries in the ResourceManager.
This patch also moves helper function 'getResourceStateIndex()' from
ResourceManager.cpp to Support.h, so that we can reuse that logic in the
SummaryView (and potentially other views in llvm-mca).
No functional change intended.
llvm-svn: 354470
This patch adds a new ReadAdvance definition named ReadInt2Fpu.
ReadInt2Fpu allows x86 scheduling models to accurately describe delays caused by
data transfers from the integer unit to the floating point unit.
ReadInt2Fpu currently defaults to a delay of zero cycles (i.e. no delay) for all
x86 models excluding BtVer2. That means, this patch is only a functional change
for the Jaguar cpu model only.
Tablegen definitions for instructions (V)PINSR* have been updated to account for
the new ReadInt2Fpu. That read is mapped to the the GPR input operand.
On Jaguar, int-to-fpu transfers are modeled as a +6cy delay. Before this patch,
that extra delay was added to the opcode latency. In practice, the insert opcode
only executes for 1cy. Most of the actual latency is actually contributed by the
so-called operand-latency. According to the AMD SOG for family 16h, (V)PINSR*
latency is defined by expression f+1, where f is defined as a forwarding delay
from the integer unit to the fpu.
When printing instruction latency from MCA (see InstructionInfoView.cpp) and LLC
(only when flag -print-schedule is speified), we now need to account for any
extra forwarding delays. We do this by checking if scheduling classes declare
any negative ReadAdvance entries. Quoting a code comment in TargetSchedule.td:
"A negative advance effectively increases latency, which may be used for
cross-domain stalls". When computing the instruction latency for the purpose of
our scheduling tests, we now add any extra delay to the formula. This avoids
regressing existing codegen and mca schedule tests. It comes with the cost of an
extra (but very simple) hook in MCSchedModel.
Differential Revision: https://reviews.llvm.org/D57056
llvm-svn: 351965
to reflect the new license.
We understand that people may be surprised that we're moving the header
entirely to discuss the new license. We checked this carefully with the
Foundation's lawyer and we believe this is the correct approach.
Essentially, all code in the project is now made available by the LLVM
project under our new license, so you will see that the license headers
include that license only. Some of our contributors have contributed
code under our old license, and accordingly, we have retained a copy of
our old license notice in the top-level files in each project and
repository.
llvm-svn: 351636
Field ResourceUnitMask was incorrectly defined as a 'const unsigned' mask. It
should have been a 64 bit quantity instead. That means, ResourceUnitMask was
always implicitly truncated to a 32 bit quantity.
This issue has been found by inspection. Surprisingly, that bug was latent, and
it never negatively affected any existing upstream targets.
This patch fixes the wrong definition of ResourceUnitMask, and adds a bunch of
extra debug prints to help debugging potential issues related to invalid
processor resource masks.
llvm-svn: 350820
This patch adds the ability to specify via tablegen which processor resources
are load/store queue resources.
A new tablegen class named MemoryQueue can be optionally used to mark resources
that model load/store queues. Information about the load/store queue is
collected at 'CodeGenSchedule' stage, and analyzed by the 'SubtargetEmitter' to
initialize two new fields in struct MCExtraProcessorInfo named `LoadQueueID` and
`StoreQueueID`. Those two fields are identifiers for buffered resources used to
describe the load queue and the store queue.
Field `BufferSize` is interpreted as the number of entries in the queue, while
the number of units is a throughput indicator (i.e. number of available pickers
for loads/stores).
At construction time, LSUnit in llvm-mca checks for the presence of extra
processor information (i.e. MCExtraProcessorInfo) in the scheduling model. If
that information is available, and fields LoadQueueID and StoreQueueID are set
to a value different than zero (i.e. the invalid processor resource index), then
LSUnit initializes its LoadQueue/StoreQueue based on the BufferSize value
declared by the two processor resources.
With this patch, we more accurately track dynamic dispatch stalls caused by the
lack of LS tokens (i.e. load/store queue full). This is also shown by the
differences in two BdVer2 tests. Stalls that were previously classified as
generic SCHEDULER FULL stalls, are not correctly classified either as "load
queue full" or "store queue full".
About the differences in the -scheduler-stats view: those differences are
expected, because entries in the load/store queue are not released at
instruction issue stage. Instead, those are released at instruction executed
stage. This is the main reason why for the modified tests, the load/store
queues gets full before PdEx is full.
Differential Revision: https://reviews.llvm.org/D54957
llvm-svn: 347857
RetireControlUnitStatistics now reports extra information about the ROB and the
avg/maximum number of entries consumed over the entire simulation.
Example:
Retire Control Unit - number of cycles where we saw N instructions retired:
[# retired], [# cycles]
0, 109 (17.9%)
1, 102 (16.7%)
2, 399 (65.4%)
Total ROB Entries: 64
Max Used ROB Entries: 35 ( 54.7% )
Average Used ROB Entries per cy: 32 ( 50.0% )
Documentation in llvm/docs/CommandGuide/llvmn-mca.rst has been updated to
reflect this change.
llvm-svn: 347493
This patch teaches view RegisterFileStatistics how to report events for
optimizable register moves.
For each processor register file, view RegisterFileStatistics reports the
following extra information:
- Number of optimizable register moves
- Number of register moves eliminated
- Number of zero moves (i.e. register moves that propagate a zero)
- Max Number of moves eliminated per cycle.
Differential Revision: https://reviews.llvm.org/D53976
llvm-svn: 345865
Summary: This allows to remove `using namespace llvm;` in those *.cpp files
When we want to revisit the decision (everything resides in llvm::mca::*) in the future, we can move things to a nested namespace of llvm::mca::, to conceptually make them separate from the rest of llvm::mca::*
Reviewers: andreadb, mattd
Reviewed By: andreadb
Subscribers: javed.absar, tschuett, gbedwell, llvm-commits
Differential Revision: https://reviews.llvm.org/D53407
llvm-svn: 345612
Also, removed the initialization of vectors used for processor resource masks.
Support function 'computeProcResourceMasks()' already calls method resize on
those vectors.
No functional change intended.
llvm-svn: 345161
Added begin()/end() methods to allow the usage of SourceMgr in foreach loops.
With this change, method getMCInstFromIndex() (as well as a couple of other
methods) are now redundant, and can be removed from the public interface.
llvm-svn: 345147
Summary:
This patch removes the storing of accumulated floating point data
within the llvm-mca library.
This patch splits-up the two quantities: cycles and number of resource units.
By splitting-up these two quantities, we delay the calculation of "cycles per resource unit"
until that value is read, reducing the chance of accumulating floating point error.
I considered using the APFloat, but after measuring performance, for a large (many iteration)
sample, I decided to go with this faster solution.
Reviewers: andreadb, courbet, RKSimon
Reviewed By: andreadb
Subscribers: llvm-commits, javed.absar, tschuett, gbedwell
Differential Revision: https://reviews.llvm.org/D51903
llvm-svn: 341980
This patch introduces the following changes to the DispatchStatistics view:
* DispatchStatistics now reports the number of dispatched opcodes instead of
the number of dispatched instructions.
* The "Dynamic Dispatch Stall Cycles" table now also reports the percentage of
stall cycles against the total simulated cycles.
This change allows users to easily compare dispatch group sizes with the
processor DispatchWidth.
Before this change, it was difficult to correlate the two numbers, since
DispatchStatistics view reported numbers of instructions (instead of opcodes).
DispatchWidth defines the maximum size of a dispatch group in terms of number of
micro opcodes.
The other change introduced by this patch is related to how DispatchStage
generates "instruction dispatch" events.
In particular:
* There can be multiple dispatch events associated with a same instruction
* Each dispatch event now encapsulates the number of dispatched micro opcodes.
The number of micro opcodes declared by an instruction may exceed the processor
DispatchWidth. Therefore, we cannot assume that instructions are always fully
dispatched in a single cycle.
DispatchStage knows already how to handle instructions declaring a number of
opcodes bigger that DispatchWidth. However, DispatchStage always emitted a
single instruction dispatch event (during the first simulated dispatch cycle)
for instructions dispatched.
With this patch, DispatchStage now correctly notifies multiple dispatch events
for instructions that cannot be dispatched in a single cycle.
A few views had to be modified. Views can no longer assume that there can only
be one dispatch event per instruction.
Tests (and docs) have been updated.
Differential Revision: https://reviews.llvm.org/D51430
llvm-svn: 341055
This patch adds two new fields to the perf report generated by the SummaryView.
Fields are now logically organized into two small groups; only the second group
contains throughput indicators.
Example:
```
Iterations: 100
Instructions: 300
Total Cycles: 414
Total uOps: 700
Dispatch Width: 4
uOps Per Cycle: 1.69
IPC: 0.72
Block RThroughput: 4.0
```
This patch also updates the docs for llvm-mca.
Due to the nature of this change, several tests in the tools/llvm-mca directory
were affected, and had to be updated using script `update_mca_test_checks.py`.
llvm-svn: 340946
This patch also uses colors to highlight problematic wait-time entries.
A problematic entry is an entry with an high wait time that tends to match (or
exceed) the size of the scheduler's buffer.
Color RED is used if an instruction had to wait an average number of cycles
which is bigger than (or equal to) the size of the underlying scheduler's
buffer.
Color YELLOW is used if the time (in cycles) spend waiting for the
operands or pipeline resources is bigger than half the size of the underlying
scheduler's buffer.
Color MAGENTA is used if an instruction does not consume buffer resources
according to the scheduling model.
llvm-svn: 340825
Before this patch, the SchedulerStatistics only printed the maximum number of
buffer entries consumed in each scheduler's queue at a given point of the
simulation.
This patch restructures the reported table, and adds an extra field named
"Average number of used buffer entries" to it.
This patch also uses different colors to help identifying bottlenecks caused by
high scheduler's buffer pressure.
llvm-svn: 340746
Fangrui Song