vedithal-amd
354fe5f52c
* Show description of metrics during analysis
* Use --include-cols Description show the Description column in analyze mode (this is hidden by default)
* Remove tips field from analysis config
* Align metric names in analysis config and documentation
* Add unified config utils/unified_config.yaml
* Add python script utils/split_config.py to auto generate analysis configuration and documentation metrics description
* Add test case to ensure unified config is older than auto-generated config
* Auto generate analysis config and documentation metrics description
* Update CONTRIBUTING.md to add instructions to build documentation assets
* Add docker image and compose file to build documentation
* Update CHANGELOG and Documentation
* Use jinja template instead of hardcoding metric tables in documentation
[ROCm/rocprofiler-compute commit: bb44e90b2d]
248 řádky
8.7 KiB
ReStructuredText
248 řádky
8.7 KiB
ReStructuredText
.. meta::
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:description: ROCm Compute Profiler performance model: Pipeline metrics
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:keywords: Omniperf, ROCm Compute Profiler, ROCm, profiler, tool, Instinct, accelerator, pipeline, wavefront, metrics, launch, runtime
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VALU, MFMA, instruction mix, FLOPs, arithmetic, operations
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****************
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Pipeline metrics
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****************
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In this section, we describe the metrics available in ROCm Compute Profiler to analyze the
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pipelines discussed in the :doc:`pipeline-descriptions`.
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.. _wavefront:
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Wavefront
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=========
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.. _wavefront-launch-stats:
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Wavefront launch stats
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----------------------
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The wavefront launch stats panel gives general information about the
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kernel launch:
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.. jinja:: wavefront-launch-stats
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:file: _templates/metrics_table.j2
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.. _wavefront-runtime-stats:
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Wavefront runtime stats
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-----------------------
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The wavefront runtime statistics gives a high-level overview of the
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execution of wavefronts in a kernel:
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.. jinja:: wavefront-runtime-stats
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:file: _templates/metrics_table.j2
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.. note::
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As mentioned earlier, the measurement of kernel cycles and time typically
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cannot be directly compared to, for example, wave cycles. This is due to two factors:
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first, the kernel cycles/timings are measured using a counter that is
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impacted by scheduling overhead, this is particularly noticeable for
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"short-running" kernels (less than 1ms) where scheduling overhead forms a
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significant portion of the overall kernel runtime. Secondly, the wave cycles
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metric is incremented per-wavefront scheduled to a SIMD every cycle whereas
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the kernel cycles counter is incremented only once per-cycle when *any*
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wavefront is scheduled.
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.. _instruction-mix:
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Instruction mix
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===============
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The instruction mix panel shows a breakdown of the various types of instructions
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executed by the user’s kernel, and which pipelines on the
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:doc:`CU <compute-unit>` they were executed on. In addition, ROCm Compute Profiler reports
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further information about the breakdown of operation types for the
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:ref:`VALU <desc-valu>`, vector-memory, and :ref:`MFMA <desc-mfma>`
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instructions.
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.. note::
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All metrics in this section count *instructions issued*, and *not* the total
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number of operations executed. The values reported by these metrics will not
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change regardless of the execution mask of the wavefront. Note that even if
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the execution mask is identically zero (meaning that *no lanes are active*)
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the instruction will still be counted, as CDNA accelerators still consider
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these instructions *issued*. See
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:mi200-isa-pdf:`EXECute Mask, section 3.3 of the CDNA2 ISA guide<19>` for
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examples and further details.
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Overall instruction mix
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-----------------------
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This panel shows the total number of each type of instruction issued to
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the :doc:`various compute pipelines </conceptual/pipeline-descriptions>` on the
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:doc:`CU </conceptual/compute-unit>`. These are:
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.. jinja:: instruction-mix
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:file: _templates/metrics_table.j2
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.. note::
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Note, as mentioned in the :ref:`desc-branch` section: branch
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operations are not used for execution mask updates, but only for "whole
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wavefront" control flow changes.
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.. _valu-arith-instruction-mix:
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VALU arithmetic instruction mix
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-------------------------------
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.. warning::
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Not all metrics in this section (for instance, the floating-point instruction
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breakdowns) are available on CDNA accelerators older than the
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:ref:`MI2XX <mixxx-note>` series.
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This panel details the various types of vector instructions that were
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issued to the :ref:`VALU <desc-valu>`. The metrics in this section do *not*
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include :ref:`MFMA <desc-mfma>` instructions using the same precision; for
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instance, the “F16-ADD” metric does not include any 16-bit floating point
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additions executed as part of an MFMA instruction using the same precision.
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.. jinja:: valu-arith-instruction-mix
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:file: _templates/metrics_table.j2
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For an example of these counters in action, refer to
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:ref:`valu-arith-instruction-mix-ex`.
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.. _vmem-instruction-mix:
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VMEM instruction mix
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--------------------
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This section breaks down the types of vector memory (VMEM) instructions
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that were issued. Refer to the
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:ref:`Instruction Counts metrics section <ta-instruction-counts>` under address
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processor front end of the vL1D cache for descriptions of these VMEM
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instructions.
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.. _mfma-instruction-mix:
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MFMA instruction mix
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--------------------
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.. warning::
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The metrics in this section are only available on CDNA2
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(:ref:`MI2XX <mixxx-note>`) accelerators and newer.
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This section details the types of Matrix Fused Multiply-Add
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(:ref:`MFMA <desc-mfma>`) instructions that were issued. Note that
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MFMA instructions are classified by the type of input data they operate on, and
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*not* the data type the result is accumulated to.
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.. jinja:: mfma-instruction-mix
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:file: _templates/metrics_table.j2
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Compute pipeline
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================
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.. _metrics-flop-count:
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FLOP counting conventions
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-------------------------
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ROCm Compute Profiler’s conventions for VALU FLOP counting are as follows:
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* Addition or multiplication: 1 operation
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* Transcendentals: 1 operation
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* Fused multiply-add (FMA): 2 operations
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Integer operations (IOPs) do not use this convention. They are counted
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as a single operation regardless of the instruction type.
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.. note::
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Packed operations which operate on multiple operands in the same instruction
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are counted identically to the underlying instruction type. For example, the
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``v_pk_add_f32`` instruction on :ref:`MI2XX <mixxx-note>`, which performs an
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add operation on two pairs of aligned 32-bit floating-point operands is
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counted only as a single addition -- that is, 1 operation.
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As discussed in the :ref:`instruction-mix` section, the FLOP/IOP
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metrics in this section do not take into account the execution mask of
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the operation, and will report the same value even if the execution mask
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is identically zero.
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For example, a FMA instruction operating on 32-bit floating-point
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operands (such as ``v_fma_f32`` on a :ref:`MI2XX <mixxx-note>` accelerator)
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would be counted as 128 total FLOPs: 2 operations (due to the
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instruction type) multiplied by 64 operations (because the wavefront is
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composed of 64 work-items).
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.. _compute-speed-of-light:
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Compute Speed-of-Light
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----------------------
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.. warning::
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The theoretical maximum throughput for some metrics in this section are
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currently computed with the maximum achievable clock frequency, as reported
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by ``rocminfo``, for an accelerator. This may not be realistic for all
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workloads.
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This section reports the number of floating-point and integer operations
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executed on the :ref:`VALU <desc-valu>` and :ref:`MFMA <desc-mfma>` units in
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various precisions. We note that unlike the
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:ref:`VALU instruction mix <valu-arith-instruction-mix>` and
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:ref:`MFMA instruction mix <mfma-instruction-mix>` sections, the metrics here
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are reported as FLOPs and IOPs, that is, the total number of operations
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executed.
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.. jinja:: compute-speed-of-light
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:file: _templates/metrics_table.j2
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.. _pipeline-stats:
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Pipeline statistics
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-------------------
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This section reports a number of key performance characteristics of
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various execution units on the :doc:`CU <compute-unit>`. Refer to
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:ref:`ipc-example` for a detailed dive into these metrics, and the
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:ref:`scheduler <desc-scheduler>` the for a high-level overview of execution
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units and instruction issue.
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.. jinja:: pipeline-stats
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:file: _templates/metrics_table.j2
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.. note::
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The branch utilization reported in this section also includes time spent in
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other instruction types (namely: ``s_endpgm``) that are *typically* a very
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small percentage of the overall kernel execution. This complication is
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omitted for simplicity, but may result in small amounts of branch utilization
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(typically less than 1%) for otherwise branch-less kernels.
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.. _arithmetic-operations:
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Arithmetic operations
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---------------------
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This section reports the total number of floating-point and integer
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operations executed in various precisions. Unlike the
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:ref:`compute-speed-of-light` panel, this section reports both
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:ref:`VALU <desc-valu>` and :ref:`MFMA <desc-mfma>` operations of the same precision
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(e.g., F32) in the same metric. Additionally, this panel lets the user
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control how the data is normalized (i.e., control the
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:ref:`normalization unit <normalization-units>`), while the speed-of-light panel does
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not. For more detail on how operations are counted see the
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:ref:`FLOP counting convention <metrics-flop-count>` section.
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.. warning::
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As discussed in :ref:`instruction-mix`, the metrics in this section do not
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take into account the execution mask of the operation, and will report the
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same value even if EXEC is identically zero.
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.. jinja:: arithmetic-operations
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:file: _templates/metrics_table.j2 |