[rocprof-compute] Generalize Roofline (#325)

* per kernel analysis Roofline

* added per-kernel eval_metric calculation with display

* fixed typo

* updated tty.py show_all()

* formatting

* fixed ctest failures and updated equations

* formatting

* updated metric descriptoins

* review tweaks

* update docs

* added roofline gui analysis

* updated GUI docs

* updated print statement

* comment tweaks and ran ruff formatting

projects/rocprofiler-compute/docs/how-to/analyze/cli.rst

@@ -19,6 +19,9 @@ This section provides an overview of ROCm Compute Profiler's CLI analysis featur
 * :ref:`Filtering <cli-analysis-options>`: Hone in on a particular kernel,
   GPU ID, or dispatch ID via post-process filtering.
 
+* :ref:`Per-kernel roofline analysis <per-kernel-roofline>`: Detailed arithmetic 
+   intensity and performance analysis for individual kernels.
+
 Run ``rocprof-compute analyze -h`` for more details.
 
 .. _cli-walkthrough:
@@ -32,7 +35,7 @@ There are three high-level GPU analysis views:
 
 * System Speed-of-Light: Key GPU performance metrics to show overall GPU performance and utilization.
 * Memory chart: Shows memory transactions and throughput on each cache hierarchical level.
-* Empirical hierarchical roofline: Roofline model that compares achieved throughput with attainable peak hardware limits, more specifically peak compute throughput and memory bandwidth (on L1/LDS/L2/HBM).
+* Empirical hierarchical roofline: Roofline model that compares achieved throughput with attainable peak hardware limits, more specifically peak compute throughput and memory bandwidth (on L1/LDS/L2/HBM). When combined with kernel filtering, provides detailed per-kernel arithmetic intensity analysis and performance breakdowns.
 
 **System Speed-of-Light:**
 
@@ -67,7 +70,7 @@ There are three high-level GPU analysis views:
 .. note::
    * Visualized memory chart and Roofline chart are only supported in single run analysis. In multiple runs comparison mode, both are switched back to basic table view.
    * Visualized memory chart requires the width of the terminal output to be greater than or equal to 234 to display the whole chart properly.
-   * Visualized Roofline chart is adapted to the initial terminal size only. If it is not clear, you may need to adjust the terminal size and regenerate it to check the display effect.
+   * Visualized Roofline chart is adapted to the initial terminal size only. If it is not clear, you may need to adjust the terminal size and regenerate it to check the display effect. Roofline analysis provides detailed, structured table output with measured empirical peak values for comparison.
 
 .. _cli-list-metrics:
 
@@ -309,6 +312,67 @@ Filter kernels
   You should see your filtered kernels indicated by an asterisk in the **Top
   Stats** table.
 
+.. _per-kernel-roofline:
+
+Per-kernel roofline analysis
+  When analyzing specific kernels, the roofline analysis provides detailed metrics for each filtered kernel:
+
+  .. code-block:: shell-session
+     $ rocprof-compute analyze -p workloads/vcopy/MI200/ -k 0 -b 4
+  This generates enhanced roofline output showing per-kernel performance rates and arithmetic intensity calculations:
+
+  .. code-block:: text
+   ================================================================================
+   4. Roofline
+   ================================================================================
+   (4.1) Per-Kernel Roofline Metrics and (4.2) AI Plot Points
+   --------------------------------------------------------------------------------
+   Kernel 0: vecCopy(double*, double*, double*, int, int) (100.0%)
+      |
+      ├─ 4.1 Roofline Rate Metrics:
+      |   ╒═════════════╤════════════════════╤═══════════════════╤═════════╤════════════════════╕
+      |   │ Metric_ID   │ Metric             │ Value             │ Unit    │   Peak (Empirical) │
+      |   ╞═════════════╪════════════════════╪═══════════════════╪═════════╪════════════════════╡
+      |   │ 4.1.0       │ VALU FLOPs         │                   │ Gflop/s │           61286.40 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.1       │ MFMA FLOPs (F64)   │                   │ Gflop/s │          108544.33 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.2       │ MFMA FLOPs (F32)   │                   │ Gflop/s │          104531.42 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.3       │ MFMA FLOPs (F16)   │                   │ Gflop/s │          709169.38 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.4       │ MFMA FLOPs (BF16)  │ 0.0               │ Gflop/s │          388161.09 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.5       │ MFMA FLOPs (F8)    │ 0.0               │ Gflop/s │         1446089.60 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.6       │ MFMA IOPs (Int8)   │                   │ Giop/s  │          737317.94 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.7       │ HBM Bandwidth      │                   │ Gb/s    │            3231.95 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.8       │ L2 Cache Bandwidth │                   │ Gb/s    │           19096.81 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.9       │ L1 Cache Bandwidth │ 3880.358726762844 │ Gb/s    │           25006.24 │
+      |   ├─────────────┼────────────────────┼───────────────────┼─────────┼────────────────────┤
+      |   │ 4.1.10      │ LDS Bandwidth      │                   │ Gb/s    │           54920.88 │
+      |   ╘═════════════╧════════════════════╧═══════════════════╧═════════╧════════════════════╛
+      ├─ 4.2 Roofline AI Plot Points:
+      |   ╒═════════════╤══════════════════════╤═════════╤════════════╕
+      |   │ Metric_ID   │ Metric               │ Value   │ Unit       │
+      |   ╞═════════════╪══════════════════════╪═════════╪════════════╡
+      |   │ 4.2.0       │ AI HBM               │         │ Flops/byte │
+      |   ├─────────────┼──────────────────────┼─────────┼────────────┤
+      |   │ 4.2.1       │ AI L2                │         │ Flops/byte │
+      |   ├─────────────┼──────────────────────┼─────────┼────────────┤
+      |   │ 4.2.2       │ AI L1                │         │ Flops/byte │
+      |   ├─────────────┼──────────────────────┼─────────┼────────────┤
+      |   │ 4.2.3       │ Performance (GFLOPs) │         │ Gflop/s    │
+      |   ╘═════════════╧══════════════════════╧═════════╧════════════╛
+  The per-kernel analysis uses YAML-based metric evaluation for accurate calculations.
+
+  Analyze multiple kernels for comparison:
+
+  .. code-block:: shell-session
+     $ rocprof-compute analyze -p workloads/vcopy/MI200/ -k 0 1 2 -b 4
 
 Baseline comparison
   .. code-block:: shell

projects/rocprofiler-compute/docs/how-to/analyze/standalone-gui.rst

@@ -83,6 +83,7 @@ application's profiling data:
 #. Top Stats (Top Kernel Statistics)
 #. System Info
 #. System Speed-of-Light
+#. Roofline AI Data Metrics
 
 To dive deeper, use the dropdown menus at the top of the screen to isolate
 particular kernels or dispatches. You should see the web page update with