LLVM-MCA(1) LLVM LLVM-MCA(1)
NAME
llvm-mca - LLVM Machine Code Analyzer
SYNOPSIS
llvm-mca [options] [input]
DESCRIPTION
llvm-mca is a performance analysis tool that uses information available
in LLVM (e.g. scheduling models) to statically measure the performance
of machine code in a specific CPU.
Performance is measured in terms of throughput as well as processor re-
source consumption. The tool currently works for processors with a
backend for which there is a scheduling model available in LLVM.
The main goal of this tool is not just to predict the performance of
the code when run on the target, but also help with diagnosing poten-
tial performance issues.
Given an assembly code sequence, llvm-mca estimates the Instructions
Per Cycle (IPC), as well as hardware resource pressure. The analysis
and reporting style were inspired by the IACA tool from Intel.
For example, you can compile code with clang, output assembly, and pipe
it directly into llvm-mca for analysis:
$ clang foo.c -O2 -target x86_64-unknown-unknown -S -o - | llvm-mca -mcpu=btver2
Or for Intel syntax:
$ clang foo.c -O2 -target x86_64-unknown-unknown -mllvm -x86-asm-syntax=intel -S -o - | llvm-mca -mcpu=btver2
(llvm-mca detects Intel syntax by the presence of an .intel_syntax di-
rective at the beginning of the input. By default its output syntax
matches that of its input.)
Scheduling models are not just used to compute instruction latencies
and throughput, but also to understand what processor resources are
available and how to simulate them.
By design, the quality of the analysis conducted by llvm-mca is in-
evitably affected by the quality of the scheduling models in LLVM.
If you see that the performance report is not accurate for a processor,
please file a bug against the appropriate backend.
OPTIONS
If input is “-” or omitted, llvm-mca reads from standard input. Other-
wise, it will read from the specified filename.
If the -o option is omitted, then llvm-mca will send its output to
standard output if the input is from standard input. If the -o option
specifies “-”, then the output will also be sent to standard output.
-help Print a summary of command line options.
-o <filename>
Use <filename> as the output filename. See the summary above for
more details.
-mtriple=<target triple>
Specify a target triple string.
-march=<arch>
Specify the architecture for which to analyze the code. It de-
faults to the host default target.
-mcpu=<cpuname>
Specify the processor for which to analyze the code. By de-
fault, the cpu name is autodetected from the host.
-output-asm-variant=<variant id>
Specify the output assembly variant for the report generated by
the tool. On x86, possible values are [0, 1]. A value of 0
(vic. 1) for this flag enables the AT&T (vic. Intel) assembly
format for the code printed out by the tool in the analysis re-
port.
-print-imm-hex
Prefer hex format for numeric literals in the output assembly
printed as part of the report.
-dispatch=<width>
Specify a different dispatch width for the processor. The dis-
patch width defaults to field ‘IssueWidth’ in the processor
scheduling model. If width is zero, then the default dispatch
width is used.
-register-file-size=<size>
Specify the size of the register file. When specified, this flag
limits how many physical registers are available for register
renaming purposes. A value of zero for this flag means “unlim-
ited number of physical registers”.
-iterations=<number of iterations>
Specify the number of iterations to run. If this flag is set to
0, then the tool sets the number of iterations to a default
value (i.e. 100).
-noalias=<bool>
If set, the tool assumes that loads and stores don’t alias. This
is the default behavior.
-lqueue=<load queue size>
Specify the size of the load queue in the load/store unit emu-
lated by the tool. By default, the tool assumes an unbound num-
ber of entries in the load queue. A value of zero for this flag
is ignored, and the default load queue size is used instead.
-squeue=<store queue size>
Specify the size of the store queue in the load/store unit emu-
lated by the tool. By default, the tool assumes an unbound num-
ber of entries in the store queue. A value of zero for this flag
is ignored, and the default store queue size is used instead.
-timeline
Enable the timeline view.
-timeline-max-iterations=<iterations>
Limit the number of iterations to print in the timeline view. By
default, the timeline view prints information for up to 10 iter-
ations.
-timeline-max-cycles=<cycles>
Limit the number of cycles in the timeline view, or use 0 for no
limit. By default, the number of cycles is set to 80.
-resource-pressure
Enable the resource pressure view. This is enabled by default.
-register-file-stats
Enable register file usage statistics.
-dispatch-stats
Enable extra dispatch statistics. This view collects and ana-
lyzes instruction dispatch events, as well as static/dynamic
dispatch stall events. This view is disabled by default.
-scheduler-stats
Enable extra scheduler statistics. This view collects and ana-
lyzes instruction issue events. This view is disabled by de-
fault.
-retire-stats
Enable extra retire control unit statistics. This view is dis-
abled by default.
-instruction-info
Enable the instruction info view. This is enabled by default.
-show-encoding
Enable the printing of instruction encodings within the instruc-
tion info view.
-show-barriers
Enable the printing of LoadBarrier and StoreBarrier flags within
the instruction info view.
-all-stats
Print all hardware statistics. This enables extra statistics re-
lated to the dispatch logic, the hardware schedulers, the regis-
ter file(s), and the retire control unit. This option is dis-
abled by default.
-all-views
Enable all the view.
-instruction-tables
Prints resource pressure information based on the static infor-
mation available from the processor model. This differs from the
resource pressure view because it doesn’t require that the code
is simulated. It instead prints the theoretical uniform distri-
bution of resource pressure for every instruction in sequence.
-bottleneck-analysis
Print information about bottlenecks that affect the throughput.
This analysis can be expensive, and it is disabled by default.
Bottlenecks are highlighted in the summary view. Bottleneck
analysis is currently not supported for processors with an
in-order backend.
-json Print the requested views in valid JSON format. The instructions
and the processor resources are printed as members of special
top level JSON objects. The individual views refer to them by
index. However, not all views are currently supported. For exam-
ple, the report from the bottleneck analysis is not printed out
in JSON. All the default views are currently supported.
-disable-cb
Force usage of the generic CustomBehaviour and InstrPostProcess
classes rather than using the target specific implementation.
The generic classes never detect any custom hazards or make any
post processing modifications to instructions.
-disable-im
Force usage of the generic InstrumentManager rather than using
the target specific implementation. The generic class creates
Instruments that provide no extra information, and Instrument-
Manager never overrides the default schedule class for a given
instruction.
EXIT STATUS
llvm-mca returns 0 on success. Otherwise, an error message is printed
to standard error, and the tool returns 1.
USING MARKERS TO ANALYZE SPECIFIC CODE BLOCKS
llvm-mca allows for the optional usage of special code comments to mark
regions of the assembly code to be analyzed. A comment starting with
substring LLVM-MCA-BEGIN marks the beginning of an analysis region. A
comment starting with substring LLVM-MCA-END marks the end of a region.
For example:
# LLVM-MCA-BEGIN
...
# LLVM-MCA-END
If no user-defined region is specified, then llvm-mca assumes a default
region which contains every instruction in the input file. Every re-
gion is analyzed in isolation, and the final performance report is the
union of all the reports generated for every analysis region.
Analysis regions can have names. For example:
# LLVM-MCA-BEGIN A simple example
add %eax, %eax
# LLVM-MCA-END
The code from the example above defines a region named “A simple exam-
ple” with a single instruction in it. Note how the region name doesn’t
have to be repeated in the LLVM-MCA-END directive. In the absence of
overlapping regions, an anonymous LLVM-MCA-END directive always ends
the currently active user defined region.
Example of nesting regions:
# LLVM-MCA-BEGIN foo
add %eax, %edx
# LLVM-MCA-BEGIN bar
sub %eax, %edx
# LLVM-MCA-END bar
# LLVM-MCA-END foo
Example of overlapping regions:
# LLVM-MCA-BEGIN foo
add %eax, %edx
# LLVM-MCA-BEGIN bar
sub %eax, %edx
# LLVM-MCA-END foo
add %eax, %edx
# LLVM-MCA-END bar
Note that multiple anonymous regions cannot overlap. Also, overlapping
regions cannot have the same name.
There is no support for marking regions from high-level source code,
like C or C++. As a workaround, inline assembly directives may be used:
int foo(int a, int b) {
__asm volatile("# LLVM-MCA-BEGIN foo":::"memory");
a += 42;
__asm volatile("# LLVM-MCA-END":::"memory");
a *= b;
return a;
}
However, this interferes with optimizations like loop vectorization and
may have an impact on the code generated. This is because the __asm
statements are seen as real code having important side effects, which
limits how the code around them can be transformed. If users want to
make use of inline assembly to emit markers, then the recommendation is
to always verify that the output assembly is equivalent to the assembly
generated in the absence of markers. The Clang options to emit opti-
mization reports can also help in detecting missed optimizations.
INSTRUMENT REGIONS
An InstrumentRegion describes a region of assembly code guarded by spe-
cial LLVM-MCA comment directives.
# LLVM-MCA-<INSTRUMENT_TYPE> <data>
... ## asm
where INSTRUMENT_TYPE is a type defined by the target and expects to
use data.
A comment starting with substring LLVM-MCA-<INSTRUMENT_TYPE> brings
data into scope for llvm-mca to use in its analysis for all following
instructions.
If a comment with the same INSTRUMENT_TYPE is found later in the in-
struction list, then the original InstrumentRegion will be automati-
cally ended, and a new InstrumentRegion will begin.
If there are comments containing the different INSTRUMENT_TYPE, then
both data sets remain available. In contrast with an AnalysisRegion, an
InstrumentRegion does not need a comment to end the region.
Comments that are prefixed with LLVM-MCA- but do not correspond to a
valid INSTRUMENT_TYPE for the target cause an error, except for BEGIN
and END, since those correspond to AnalysisRegions. Comments that do
not start with LLVM-MCA- are ignored by :program llvm-mca.
An instruction (a MCInst) is added to an InstrumentRegion R only if its
location is in range [R.RangeStart, R.RangeEnd].
On RISCV targets, vector instructions have different behaviour depend-
ing on the LMUL. Code can be instrumented with a comment that takes the
following form:
# LLVM-MCA-RISCV-LMUL <M1|M2|M4|M8|MF2|MF4|MF8>
The RISCV InstrumentManager will override the schedule class for vector
instructions to use the scheduling behaviour of its pseudo-instruction
which is LMUL dependent. It makes sense to place RISCV instrument com-
ments directly after vset{i}vl{i} instructions, although they can be
placed anywhere in the program.
Example of program with no call to vset{i}vl{i}:
# LLVM-MCA-RISCV-LMUL M2
vadd.vv v2, v2, v2
Example of program with call to vset{i}vl{i}:
vsetvli zero, a0, e8, m1, tu, mu
# LLVM-MCA-RISCV-LMUL M1
vadd.vv v2, v2, v2
Example of program with multiple calls to vset{i}vl{i}:
vsetvli zero, a0, e8, m1, tu, mu
# LLVM-MCA-RISCV-LMUL M1
vadd.vv v2, v2, v2
vsetvli zero, a0, e8, m8, tu, mu
# LLVM-MCA-RISCV-LMUL M8
vadd.vv v2, v2, v2
Example of program with call to vsetvl:
vsetvl rd, rs1, rs2
# LLVM-MCA-RISCV-LMUL M1
vadd.vv v12, v12, v12
vsetvl rd, rs1, rs2
# LLVM-MCA-RISCV-LMUL M4
vadd.vv v12, v12, v12
HOW LLVM-MCA WORKS
llvm-mca takes assembly code as input. The assembly code is parsed into
a sequence of MCInst with the help of the existing LLVM target assembly
parsers. The parsed sequence of MCInst is then analyzed by a Pipeline
module to generate a performance report.
The Pipeline module simulates the execution of the machine code se-
quence in a loop of iterations (default is 100). During this process,
the pipeline collects a number of execution related statistics. At the
end of this process, the pipeline generates and prints a report from
the collected statistics.
Here is an example of a performance report generated by the tool for a
dot-product of two packed float vectors of four elements. The analysis
is conducted for target x86, cpu btver2. The following result can be
produced via the following command using the example located at
test/tools/llvm-mca/X86/BtVer2/dot-product.s:
$ llvm-mca -mtriple=x86_64-unknown-unknown -mcpu=btver2 -iterations=300 dot-product.s
Iterations: 300
Instructions: 900
Total Cycles: 610
Total uOps: 900
Dispatch Width: 2
uOps Per Cycle: 1.48
IPC: 1.48
Block RThroughput: 2.0
Instruction Info:
[1]: #uOps
[2]: Latency
[3]: RThroughput
[4]: MayLoad
[5]: MayStore
[6]: HasSideEffects (U)
[1] [2] [3] [4] [5] [6] Instructions:
1 2 1.00 vmulps %xmm0, %xmm1, %xmm2
1 3 1.00 vhaddps %xmm2, %xmm2, %xmm3
1 3 1.00 vhaddps %xmm3, %xmm3, %xmm4
Resources:
[0] - JALU0
[1] - JALU1
[2] - JDiv
[3] - JFPA
[4] - JFPM
[5] - JFPU0
[6] - JFPU1
[7] - JLAGU
[8] - JMul
[9] - JSAGU
[10] - JSTC
[11] - JVALU0
[12] - JVALU1
[13] - JVIMUL
Resource pressure per iteration:
[0] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]
- - - 2.00 1.00 2.00 1.00 - - - - - - -
Resource pressure by instruction:
[0] [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] Instructions:
- - - - 1.00 - 1.00 - - - - - - - vmulps %xmm0, %xmm1, %xmm2
- - - 1.00 - 1.00 - - - - - - - - vhaddps %xmm2, %xmm2, %xmm3
- - - 1.00 - 1.00 - - - - - - - - vhaddps %xmm3, %xmm3, %xmm4
According to this report, the dot-product kernel has been executed 300
times, for a total of 900 simulated instructions. The total number of
simulated micro opcodes (uOps) is also 900.
The report is structured in three main sections. The first section
collects a few performance numbers; the goal of this section is to give
a very quick overview of the performance throughput. Important perfor-
mance indicators are IPC, uOps Per Cycle, and Block RThroughput (Block
Reciprocal Throughput).
Field DispatchWidth is the maximum number of micro opcodes that are
dispatched to the out-of-order backend every simulated cycle. For pro-
cessors with an in-order backend, DispatchWidth is the maximum number
of micro opcodes issued to the backend every simulated cycle.
IPC is computed dividing the total number of simulated instructions by
the total number of cycles.
Field Block RThroughput is the reciprocal of the block throughput.
Block throughput is a theoretical quantity computed as the maximum num-
ber of blocks (i.e. iterations) that can be executed per simulated
clock cycle in the absence of loop carried dependencies. Block through-
put is superiorly limited by the dispatch rate, and the availability of
hardware resources.
In the absence of loop-carried data dependencies, the observed IPC
tends to a theoretical maximum which can be computed by dividing the
number of instructions of a single iteration by the Block RThroughput.
Field ‘uOps Per Cycle’ is computed dividing the total number of simu-
lated micro opcodes by the total number of cycles. A delta between Dis-
patch Width and this field is an indicator of a performance issue. In
the absence of loop-carried data dependencies, the observed ‘uOps Per
Cycle’ should tend to a theoretical maximum throughput which can be
computed by dividing the number of uOps of a single iteration by the
Block RThroughput.
Field uOps Per Cycle is bounded from above by the dispatch width. That
is because the dispatch width limits the maximum size of a dispatch
group. Both IPC and ‘uOps Per Cycle’ are limited by the amount of hard-
ware parallelism. The availability of hardware resources affects the
resource pressure distribution, and it limits the number of instruc-
tions that can be executed in parallel every cycle. A delta between
Dispatch Width and the theoretical maximum uOps per Cycle (computed by
dividing the number of uOps of a single iteration by the Block
RThroughput) is an indicator of a performance bottleneck caused by the
lack of hardware resources. In general, the lower the Block RThrough-
put, the better.
In this example, uOps per iteration/Block RThroughput is 1.50. Since
there are no loop-carried dependencies, the observed uOps Per Cycle is
expected to approach 1.50 when the number of iterations tends to infin-
ity. The delta between the Dispatch Width (2.00), and the theoretical
maximum uOp throughput (1.50) is an indicator of a performance bottle-
neck caused by the lack of hardware resources, and the Resource pres-
sure view can help to identify the problematic resource usage.
The second section of the report is the instruction info view. It shows
the latency and reciprocal throughput of every instruction in the se-
quence. It also reports extra information related to the number of mi-
cro opcodes, and opcode properties (i.e., ‘MayLoad’, ‘MayStore’, and
‘HasSideEffects’).
Field RThroughput is the reciprocal of the instruction throughput.
Throughput is computed as the maximum number of instructions of a same
type that can be executed per clock cycle in the absence of operand de-
pendencies. In this example, the reciprocal throughput of a vector
float multiply is 1 cycles/instruction. That is because the FP multi-
plier JFPM is only available from pipeline JFPU1.
Instruction encodings are displayed within the instruction info view
when flag -show-encoding is specified.
Below is an example of -show-encoding output for the dot-product ker-
nel:
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
The Encoding Size column shows the size in bytes of instructions. The
Encodings column shows the actual instruction encodings (byte sequences
in hex).
The third section is the Resource pressure view. This view reports the
average number of resource cycles consumed every iteration by instruc-
tions for every processor resource unit available on the target. In-
formation is structured in two tables. The first table reports the num-
ber of resource cycles spent on average every iteration. The second ta-
ble correlates the resource cycles to the machine instruction in the
sequence. For example, every iteration of the instruction vmulps always
executes on resource unit [6] (JFPU1 - floating point pipeline #1),
consuming an average of 1 resource cycle per iteration. Note that on
AMD Jaguar, vector floating-point multiply can only be issued to pipe-
line JFPU1, while horizontal floating-point additions can only be is-
sued to pipeline JFPU0.
The resource pressure view helps with identifying bottlenecks caused by
high usage of specific hardware resources. Situations with resource
pressure mainly concentrated on a few resources should, in general, be
avoided. Ideally, pressure should be uniformly distributed between
multiple resources.
Timeline View
The timeline view produces a detailed report of each instruction’s
state transitions through an instruction pipeline. This view is en-
abled by the command line option -timeline. As instructions transition
through the various stages of the pipeline, their states are depicted
in the view report. These states are represented by the following
characters:
• D : Instruction dispatched.
• e : Instruction executing.
• E : Instruction executed.
• R : Instruction retired.
• = : Instruction already dispatched, waiting to be executed.
• - : Instruction executed, waiting to be retired.
Below is the timeline view for a subset of the dot-product example lo-
cated in test/tools/llvm-mca/X86/BtVer2/dot-product.s and processed by
llvm-mca using the following command:
$ llvm-mca -mtriple=x86_64-unknown-unknown -mcpu=btver2 -iterations=3 -timeline dot-product.s
Timeline view:
012345
Index 0123456789
[0,0] DeeER. . . vmulps %xmm0, %xmm1, %xmm2
[0,1] D==eeeER . . vhaddps %xmm2, %xmm2, %xmm3
[0,2] .D====eeeER . vhaddps %xmm3, %xmm3, %xmm4
[1,0] .DeeE-----R . vmulps %xmm0, %xmm1, %xmm2
[1,1] . D=eeeE---R . vhaddps %xmm2, %xmm2, %xmm3
[1,2] . D====eeeER . vhaddps %xmm3, %xmm3, %xmm4
[2,0] . DeeE-----R . vmulps %xmm0, %xmm1, %xmm2
[2,1] . D====eeeER . vhaddps %xmm2, %xmm2, %xmm3
[2,2] . D======eeeER vhaddps %xmm3, %xmm3, %xmm4
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 3.3 vmulps %xmm0, %xmm1, %xmm2
1. 3 3.3 0.7 1.0 vhaddps %xmm2, %xmm2, %xmm3
2. 3 5.7 0.0 0.0 vhaddps %xmm3, %xmm3, %xmm4
3 3.3 0.5 1.4 <total>
The timeline view is interesting because it shows instruction state
changes during execution. It also gives an idea of how the tool pro-
cesses instructions executed on the target, and how their timing infor-
mation might be calculated.
The timeline view is structured in two tables. The first table shows
instructions changing state over time (measured in cycles); the second
table (named Average Wait times) reports useful timing statistics,
which should help diagnose performance bottlenecks caused by long data
dependencies and sub-optimal usage of hardware resources.
An instruction in the timeline view is identified by a pair of indices,
where the first index identifies an iteration, and the second index is
the instruction index (i.e., where it appears in the code sequence).
Since this example was generated using 3 iterations: -iterations=3, the
iteration indices range from 0-2 inclusively.
Excluding the first and last column, the remaining columns are in cy-
cles. Cycles are numbered sequentially starting from 0.
From the example output above, we know the following:
• Instruction [1,0] was dispatched at cycle 1.
• Instruction [1,0] started executing at cycle 2.
• Instruction [1,0] reached the write back stage at cycle 4.
• Instruction [1,0] was retired at cycle 10.
Instruction [1,0] (i.e., vmulps from iteration #1) does not have to
wait in the scheduler’s queue for the operands to become available. By
the time vmulps is dispatched, operands are already available, and
pipeline JFPU1 is ready to serve another instruction. So the instruc-
tion can be immediately issued on the JFPU1 pipeline. That is demon-
strated by the fact that the instruction only spent 1cy in the sched-
uler’s queue.
There is a gap of 5 cycles between the write-back stage and the retire
event. That is because instructions must retire in program order, so
[1,0] has to wait for [0,2] to be retired first (i.e., it has to wait
until cycle 10).
In the example, all instructions are in a RAW (Read After Write) depen-
dency chain. Register %xmm2 written by vmulps is immediately used by
the first vhaddps, and register %xmm3 written by the first vhaddps is
used by the second vhaddps. Long data dependencies negatively impact
the ILP (Instruction Level Parallelism).
In the dot-product example, there are anti-dependencies introduced by
instructions from different iterations. However, those dependencies
can be removed at register renaming stage (at the cost of allocating
register aliases, and therefore consuming physical registers).
Table Average Wait times helps diagnose performance issues that are
caused by the presence of long latency instructions and potentially
long data dependencies which may limit the ILP. Last row, <total>,
shows a global average over all instructions measured. Note that
llvm-mca, by default, assumes at least 1cy between the dispatch event
and the issue event.
When the performance is limited by data dependencies and/or long la-
tency instructions, the number of cycles spent while in the ready state
is expected to be very small when compared with the total number of cy-
cles spent in the scheduler’s queue. The difference between the two
counters is a good indicator of how large of an impact data dependen-
cies had on the execution of the instructions. When performance is
mostly limited by the lack of hardware resources, the delta between the
two counters is small. However, the number of cycles spent in the
queue tends to be larger (i.e., more than 1-3cy), especially when com-
pared to other low latency instructions.
Bottleneck Analysis
The -bottleneck-analysis command line option enables the analysis of
performance bottlenecks.
This analysis is potentially expensive. It attempts to correlate in-
creases in backend pressure (caused by pipeline resource pressure and
data dependencies) to dynamic dispatch stalls.
Below is an example of -bottleneck-analysis output generated by
llvm-mca for 500 iterations of the dot-product example on btver2.
Cycles with backend pressure increase [ 48.07% ]
Throughput Bottlenecks:
Resource Pressure [ 47.77% ]
- JFPA [ 47.77% ]
- JFPU0 [ 47.77% ]
Data Dependencies: [ 0.30% ]
- Register Dependencies [ 0.30% ]
- Memory Dependencies [ 0.00% ]
Critical sequence based on the simulation:
Instruction Dependency Information
+----< 2. vhaddps %xmm3, %xmm3, %xmm4
|
| < loop carried >
|
| 0. vmulps %xmm0, %xmm1, %xmm2
+----> 1. vhaddps %xmm2, %xmm2, %xmm3 ## RESOURCE interference: JFPA [ probability: 74% ]
+----> 2. vhaddps %xmm3, %xmm3, %xmm4 ## REGISTER dependency: %xmm3
|
| < loop carried >
|
+----> 1. vhaddps %xmm2, %xmm2, %xmm3 ## RESOURCE interference: JFPA [ probability: 74% ]
According to the analysis, throughput is limited by resource pressure
and not by data dependencies. The analysis observed increases in back-
end pressure during 48.07% of the simulated run. Almost all those pres-
sure increase events were caused by contention on processor resources
JFPA/JFPU0.
The critical sequence is the most expensive sequence of instructions
according to the simulation. It is annotated to provide extra informa-
tion about critical register dependencies and resource interferences
between instructions.
Instructions from the critical sequence are expected to significantly
impact performance. By construction, the accuracy of this analysis is
strongly dependent on the simulation and (as always) by the quality of
the processor model in llvm.
Bottleneck analysis is currently not supported for processors with an
in-order backend.
Extra Statistics to Further Diagnose Performance Issues
The -all-stats command line option enables extra statistics and perfor-
mance counters for the dispatch logic, the reorder buffer, the retire
control unit, and the register file.
Below is an example of -all-stats output generated by llvm-mca for 300
iterations of the dot-product example discussed in the previous sec-
tions.
Dynamic Dispatch Stall Cycles:
RAT - Register unavailable: 0
RCU - Retire tokens unavailable: 0
SCHEDQ - Scheduler full: 272 (44.6%)
LQ - Load queue full: 0
SQ - Store queue full: 0
GROUP - Static restrictions on the dispatch group: 0
Dispatch Logic - number of cycles where we saw N micro opcodes dispatched:
[# dispatched], [# cycles]
0, 24 (3.9%)
1, 272 (44.6%)
2, 314 (51.5%)
Schedulers - number of cycles where we saw N micro opcodes issued:
[# issued], [# cycles]
0, 7 (1.1%)
1, 306 (50.2%)
2, 297 (48.7%)
Scheduler's queue usage:
[1] Resource name.
[2] Average number of used buffer entries.
[3] Maximum number of used buffer entries.
[4] Total number of buffer entries.
[1] [2] [3] [4]
JALU01 0 0 20
JFPU01 17 18 18
JLSAGU 0 0 12
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% )
Register File statistics:
Total number of mappings created: 900
Max number of mappings used: 35
* Register File #1 -- JFpuPRF:
Number of physical registers: 72
Total number of mappings created: 900
Max number of mappings used: 35
* Register File #2 -- JIntegerPRF:
Number of physical registers: 64
Total number of mappings created: 0
Max number of mappings used: 0
If we look at the Dynamic Dispatch Stall Cycles table, we see the
counter for SCHEDQ reports 272 cycles. This counter is incremented ev-
ery time the dispatch logic is unable to dispatch a full group because
the scheduler’s queue is full.
Looking at the Dispatch Logic table, we see that the pipeline was only
able to dispatch two micro opcodes 51.5% of the time. The dispatch
group was limited to one micro opcode 44.6% of the cycles, which corre-
sponds to 272 cycles. The dispatch statistics are displayed by either
using the command option -all-stats or -dispatch-stats.
The next table, Schedulers, presents a histogram displaying a count,
representing the number of micro opcodes issued on some number of cy-
cles. In this case, of the 610 simulated cycles, single opcodes were
issued 306 times (50.2%) and there were 7 cycles where no opcodes were
issued.
The Scheduler’s queue usage table shows that the average and maximum
number of buffer entries (i.e., scheduler queue entries) used at run-
time. Resource JFPU01 reached its maximum (18 of 18 queue entries).
Note that AMD Jaguar implements three schedulers:
• JALU01 - A scheduler for ALU instructions.
• JFPU01 - A scheduler floating point operations.
• JLSAGU - A scheduler for address generation.
The dot-product is a kernel of three floating point instructions (a
vector multiply followed by two horizontal adds). That explains why
only the floating point scheduler appears to be used.
A full scheduler queue is either caused by data dependency chains or by
a sub-optimal usage of hardware resources. Sometimes, resource pres-
sure can be mitigated by rewriting the kernel using different instruc-
tions that consume different scheduler resources. Schedulers with a
small queue are less resilient to bottlenecks caused by the presence of
long data dependencies. The scheduler statistics are displayed by us-
ing the command option -all-stats or -scheduler-stats.
The next table, Retire Control Unit, presents a histogram displaying a
count, representing the number of instructions retired on some number
of cycles. In this case, of the 610 simulated cycles, two instructions
were retired during the same cycle 399 times (65.4%) and there were 109
cycles where no instructions were retired. The retire statistics are
displayed by using the command option -all-stats or -retire-stats.
The last table presented is Register File statistics. Each physical
register file (PRF) used by the pipeline is presented in this table.
In the case of AMD Jaguar, there are two register files, one for float-
ing-point registers (JFpuPRF) and one for integer registers (JInte-
gerPRF). The table shows that of the 900 instructions processed, there
were 900 mappings created. Since this dot-product example utilized
only floating point registers, the JFPuPRF was responsible for creating
the 900 mappings. However, we see that the pipeline only used a maxi-
mum of 35 of 72 available register slots at any given time. We can con-
clude that the floating point PRF was the only register file used for
the example, and that it was never resource constrained. The register
file statistics are displayed by using the command option -all-stats or
-register-file-stats.
In this example, we can conclude that the IPC is mostly limited by data
dependencies, and not by resource pressure.
Instruction Flow
This section describes the instruction flow through the default pipe-
line of llvm-mca, as well as the functional units involved in the
process.
The default pipeline implements the following sequence of stages used
to process instructions.
• Dispatch (Instruction is dispatched to the schedulers).
• Issue (Instruction is issued to the processor pipelines).
• Write Back (Instruction is executed, and results are written back).
• Retire (Instruction is retired; writes are architecturally commit-
ted).
The in-order pipeline implements the following sequence of stages: *
InOrderIssue (Instruction is issued to the processor pipelines). * Re-
tire (Instruction is retired; writes are architecturally committed).
llvm-mca assumes that instructions have all been decoded and placed
into a queue before the simulation start. Therefore, the instruction
fetch and decode stages are not modeled. Performance bottlenecks in the
frontend are not diagnosed. Also, llvm-mca does not model branch pre-
diction.
Instruction Dispatch
During the dispatch stage, instructions are picked in program order
from a queue of already decoded instructions, and dispatched in groups
to the simulated hardware schedulers.
The size of a dispatch group depends on the availability of the simu-
lated hardware resources. The processor dispatch width defaults to the
value of the IssueWidth in LLVM’s scheduling model.
An instruction can be dispatched if:
• The size of the dispatch group is smaller than processor’s dispatch
width.
• There are enough entries in the reorder buffer.
• There are enough physical registers to do register renaming.
• The schedulers are not full.
Scheduling models can optionally specify which register files are
available on the processor. llvm-mca uses that information to initial-
ize register file descriptors. Users can limit the number of physical
registers that are globally available for register renaming by using
the command option -register-file-size. A value of zero for this op-
tion means unbounded. By knowing how many registers are available for
renaming, the tool can predict dispatch stalls caused by the lack of
physical registers.
The number of reorder buffer entries consumed by an instruction depends
on the number of micro-opcodes specified for that instruction by the
target scheduling model. The reorder buffer is responsible for track-
ing the progress of instructions that are “in-flight”, and retiring
them in program order. The number of entries in the reorder buffer de-
faults to the value specified by field MicroOpBufferSize in the target
scheduling model.
Instructions that are dispatched to the schedulers consume scheduler
buffer entries. llvm-mca queries the scheduling model to determine the
set of buffered resources consumed by an instruction. Buffered re-
sources are treated like scheduler resources.
Instruction Issue
Each processor scheduler implements a buffer of instructions. An in-
struction has to wait in the scheduler’s buffer until input register
operands become available. Only at that point, does the instruction
becomes eligible for execution and may be issued (potentially
out-of-order) for execution. Instruction latencies are computed by
llvm-mca with the help of the scheduling model.
llvm-mca’s scheduler is designed to simulate multiple processor sched-
ulers. The scheduler is responsible for tracking data dependencies,
and dynamically selecting which processor resources are consumed by in-
structions. It delegates the management of processor resource units
and resource groups to a resource manager. The resource manager is re-
sponsible for selecting resource units that are consumed by instruc-
tions. For example, if an instruction consumes 1cy of a resource
group, the resource manager selects one of the available units from the
group; by default, the resource manager uses a round-robin selector to
guarantee that resource usage is uniformly distributed between all
units of a group.
llvm-mca’s scheduler internally groups instructions into three sets:
• WaitSet: a set of instructions whose operands are not ready.
• ReadySet: a set of instructions ready to execute.
• IssuedSet: a set of instructions executing.
Depending on the operands availability, instructions that are dis-
patched to the scheduler are either placed into the WaitSet or into the
ReadySet.
Every cycle, the scheduler checks if instructions can be moved from the
WaitSet to the ReadySet, and if instructions from the ReadySet can be
issued to the underlying pipelines. The algorithm prioritizes older in-
structions over younger instructions.
Write-Back and Retire Stage
Issued instructions are moved from the ReadySet to the IssuedSet.
There, instructions wait until they reach the write-back stage. At
that point, they get removed from the queue and the retire control unit
is notified.
When instructions are executed, the retire control unit flags the in-
struction as “ready to retire.”
Instructions are retired in program order. The register file is noti-
fied of the retirement so that it can free the physical registers that
were allocated for the instruction during the register renaming stage.
Load/Store Unit and Memory Consistency Model
To simulate an out-of-order execution of memory operations, llvm-mca
utilizes a simulated load/store unit (LSUnit) to simulate the specula-
tive execution of loads and stores.
Each load (or store) consumes an entry in the load (or store) queue.
Users can specify flags -lqueue and -squeue to limit the number of en-
tries in the load and store queues respectively. The queues are un-
bounded by default.
The LSUnit implements a relaxed consistency model for memory loads and
stores. The rules are:
1. A younger load is allowed to pass an older load only if there are no
intervening stores or barriers between the two loads.
2. A younger load is allowed to pass an older store provided that the
load does not alias with the store.
3. A younger store is not allowed to pass an older store.
4. A younger store is not allowed to pass an older load.
By default, the LSUnit optimistically assumes that loads do not alias
(-noalias=true) store operations. Under this assumption, younger loads
are always allowed to pass older stores. Essentially, the LSUnit does
not attempt to run any alias analysis to predict when loads and stores
do not alias with each other.
Note that, in the case of write-combining memory, rule 3 could be re-
laxed to allow reordering of non-aliasing store operations. That being
said, at the moment, there is no way to further relax the memory model
(-noalias is the only option). Essentially, there is no option to
specify a different memory type (e.g., write-back, write-combining,
write-through; etc.) and consequently to weaken, or strengthen, the
memory model.
Other limitations are:
• The LSUnit does not know when store-to-load forwarding may occur.
• The LSUnit does not know anything about cache hierarchy and memory
types.
• The LSUnit does not know how to identify serializing operations and
memory fences.
The LSUnit does not attempt to predict if a load or store hits or
misses the L1 cache. It only knows if an instruction “MayLoad” and/or
“MayStore.” For loads, the scheduling model provides an “optimistic”
load-to-use latency (which usually matches the load-to-use latency for
when there is a hit in the L1D).
llvm-mca does not (on its own) know about serializing operations or
memory-barrier like instructions. The LSUnit used to conservatively
use an instruction’s “MayLoad”, “MayStore”, and unmodeled side effects
flags to determine whether an instruction should be treated as a mem-
ory-barrier. This was inaccurate in general and was changed so that now
each instruction has an IsAStoreBarrier and IsALoadBarrier flag. These
flags are mca specific and default to false for every instruction. If
any instruction should have either of these flags set, it should be
done within the target’s InstrPostProcess class. For an example, look
at the X86InstrPostProcess::postProcessInstruction method within
llvm/lib/Target/X86/MCA/X86CustomBehaviour.cpp.
A load/store barrier consumes one entry of the load/store queue. A
load/store barrier enforces ordering of loads/stores. A younger load
cannot pass a load barrier. Also, a younger store cannot pass a store
barrier. A younger load has to wait for the memory/load barrier to ex-
ecute. A load/store barrier is “executed” when it becomes the oldest
entry in the load/store queue(s). That also means, by construction, all
of the older loads/stores have been executed.
In conclusion, the full set of load/store consistency rules are:
1. A store may not pass a previous store.
2. A store may not pass a previous load (regardless of -noalias).
3. A store has to wait until an older store barrier is fully executed.
4. A load may pass a previous load.
5. A load may not pass a previous store unless -noalias is set.
6. A load has to wait until an older load barrier is fully executed.
In-order Issue and Execute
In-order processors are modelled as a single InOrderIssueStage stage.
It bypasses Dispatch, Scheduler and Load/Store unit. Instructions are
issued as soon as their operand registers are available and resource
requirements are met. Multiple instructions can be issued in one cycle
according to the value of the IssueWidth parameter in LLVM’s scheduling
model.
Once issued, an instruction is moved to IssuedInst set until it is
ready to retire. llvm-mca ensures that writes are committed in-order.
However, an instruction is allowed to commit writes and retire
out-of-order if RetireOOO property is true for at least one of its
writes.
Custom Behaviour
Due to certain instructions not being expressed perfectly within their
scheduling model, llvm-mca isn’t always able to simulate them per-
fectly. Modifying the scheduling model isn’t always a viable option
though (maybe because the instruction is modeled incorrectly on purpose
or the instruction’s behaviour is quite complex). The CustomBehaviour
class can be used in these cases to enforce proper instruction modeling
(often by customizing data dependencies and detecting hazards that
llvm-mca has no way of knowing about).
llvm-mca comes with one generic and multiple target specific CustomBe-
haviour classes. The generic class will be used if the -disable-cb flag
is used or if a target specific CustomBehaviour class doesn’t exist for
that target. (The generic class does nothing.) Currently, the CustomBe-
haviour class is only a part of the in-order pipeline, but there are
plans to add it to the out-of-order pipeline in the future.
CustomBehaviour’s main method is checkCustomHazard() which uses the
current instruction and a list of all instructions still executing
within the pipeline to determine if the current instruction should be
dispatched. As output, the method returns an integer representing the
number of cycles that the current instruction must stall for (this can
be an underestimate if you don’t know the exact number and a value of 0
represents no stall).
If you’d like to add a CustomBehaviour class for a target that doesn’t
already have one, refer to an existing implementation to see how to set
it up. The classes are implemented within the target specific backend
(for example /llvm/lib/Target/AMDGPU/MCA/) so that they can access
backend symbols.
Instrument Manager
On certain architectures, scheduling information for certain instruc-
tions do not contain all of the information required to identify the
most precise schedule class. For example, data that can have an impact
on scheduling can be stored in CSR registers.
One example of this is on RISCV, where values in registers such as
vtype and vl change the scheduling behaviour of vector instructions.
Since MCA does not keep track of the values in registers, instrument
comments can be used to specify these values.
InstrumentManager’s main function is getSchedClassID() which has access
to the MCInst and all of the instruments that are active for that
MCInst. This function can use the instruments to override the schedule
class of the MCInst.
On RISCV, instrument comments containing LMUL information are used by
getSchedClassID() to map a vector instruction and the active LMUL to
the scheduling class of the pseudo-instruction that describes that base
instruction and the active LMUL.
Custom Views
llvm-mca comes with several Views such as the Timeline View and Summary
View. These Views are generic and can work with most (if not all) tar-
gets. If you wish to add a new View to llvm-mca and it does not require
any backend functionality that is not already exposed through MC layer
classes (MCSubtargetInfo, MCInstrInfo, etc.), please add it to the
/tools/llvm-mca/View/ directory. However, if your new View is target
specific AND requires unexposed backend symbols or functionality, you
can define it in the /lib/Target/<TargetName>/MCA/ directory.
To enable this target specific View, you will have to use this target’s
CustomBehaviour class to override the CustomBehaviour::getViews() meth-
ods. There are 3 variations of these methods based on where you want
your View to appear in the output: getStartViews(), getPostInstrIn-
foViews(), and getEndViews(). These methods returns a vector of Views
so you will want to return a vector containing all of the target spe-
cific Views for the target in question.
Because these target specific (and backend dependent) Views require the
CustomBehaviour::getViews() variants, these Views will not be enabled
if the -disable-cb flag is used.
Enabling these custom Views does not affect the non-custom (generic)
Views. Continue to use the usual command line arguments to enable /
disable those Views.
AUTHOR
Maintained by the LLVM Team (https://llvm.org/).
COPYRIGHT
2003-2023, LLVM Project
15 2023-10-16 LLVM-MCA(1)
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