Add full documentation for updated metrics (#224)

Co-authored-by: Nick Curtis <nicholas.curtis@amd.com> Signed-off-by: colramos-amd <colramos@amd.com> [ROCm/rocprofiler-compute commit: 4b8f519e9b]
2024-01-31 16:05:12 -06:00
@@ -5,7 +5,7 @@
   :glob:
   :maxdepth: 4
 ```
-Omniperf offers several ways to interact with the metrics it generates from profiling. The option you choose will likey be influnced by your familiarity with the profiled application, computing enviroment, and experience with Omniperf.
+Omniperf offers several ways to interact with the metrics it generates from profiling. The option you choose will likely be influnced by your familiarity with the profiled application, computing enviroment, and experience with Omniperf.

 While analyzing with the CLI offers quick and straightforward access to Omniperf metrics from terminal, the GUI adds an extra layer of styling and interactiveness some users may prefer.

@@ -16,7 +16,7 @@ See sections below for more information on each.

 ### Features

- All Omniperf built-in metrics.
+- All of Omniperf's built-in metrics.
 - Multiple runs base line comparison.
 - Metrics customization: pick up subset of build-in metrics or build your own profiling configuration.
 - Kernel, gpu-id, dispatch-id filters.
@@ -254,7 +254,7 @@ Analyze
 ```
 > **Note:** Some cells may be blank indicating a missing/unavailable hardware counter or NULL value

-3. Optimizatize application, iterate, and re-profile to inspect performance changes.
+3. Optimize application, iterate, and re-profile to inspect performance changes.
 4. Redo a comprehensive analysis with Omniperf CLI at any milestone or at the end.

 ### Demo
@@ -281,7 +281,7 @@ Analyze
  $ omniperf analyze -p workloads/vcopy/mi200/  -b 2  5.1.0
  ```

-  > Note: Users can filter single metric or the whole IP block by its id. In this case, 1 is the id for "system speed of light" and 5.1.0 the id for metric "GPU Busy Cycles".
+  > Note: Users can filter single metric or the whole hardware component by its id. In this case, 1 is the id for "system speed of light" and 5.1.0 the id for metric "GPU Busy Cycles".

 - Filter kernels

@@ -304,7 +304,7 @@ Analyze

  ```

-  Second, select the index of the kernel you'd like to filter (i.e. __vecCopy(double*, double*, double*, int, int) [clone .kd]__ at index __0__). Then, use this index to apply the filter via `-k/--kernels`.
+  Second, select the index of the kernel you would like to filter (i.e. __vecCopy(double*, double*, double*, int, int) [clone .kd]__ at index __0__). Then, use this index to apply the filter via `-k/--kernels`.

  ```shell-session
  $ omniperf -p workloads/vcopy/mi200/ -k 0
@@ -325,7 +325,7 @@ Analyze
  ... ...
  ```
  
-  > Note: You'll see your filtered kernel(s) indicated by a asterisk in the Top Stats table
+  > Note: You will see your filtered kernel(s) indicated by an asterisk in the Top Stats table


 - Baseline comparison
@@ -333,7 +333,7 @@ Analyze
  ```shell
  omniperf analyze -p workload1/path/  -p workload2/path/
  ```
-  > Note: You can also apply diffrent filters to each workload.
+  > Note: You can also apply different filters to each workload.
  
  OR
  ```shell
@@ -414,7 +414,7 @@ When no filters are applied, users will see five basic sections derived from the

 To dive deeper, use the top drop down menus to isolate particular
 kernel(s) or dispatch(s). You will then see the web page update with
-metrics specific to the filter you've applied.
+metrics specific to the filter you have applied.

 Once you have applied a filter, you will also see several additional
 sections become available with detailed metrics specific to that area
@@ -427,22 +427,22 @@ interface](https://amdresearch.github.io/omniperf/analysis.html#grafana-based-gu
 #### Features
 The Omniperf Grafana GUI Analyzer supports the following features to facilitate MI GPU performance profiling and analysis:

- System and IP-Block Speed-of-Light (SOL)
+- System and Hardware Component (IP Block) Speed-of-Light (SOL)
 - Multiple normalization options, including per-cycle, per-wave, per-kernel and per-second.
 - Baseline comparisons 
 - Regex based Dispatch ID filtering
 - Roofline Analysis
- Detailed per IP Block performance counters and metrics
-  - CPC/CPF
-  - SPI
-  - SQ
-  - SQC
-  - TA/TD
-  - TCP
-  - TCC (both aggregated and per-channel perf info)
+- Detailed performance counters and metrics per hardware component, e.g.,
+  - Command Processor - Fetch (CPF) / Command Processor - Controller (CPC)
+  - Workgroup Manager (SPI)
+  - Shader Sequencer (SQ)
+  - Shader Sequencer Controller (SQC)
+  - L1 Address Processing Unit, a.k.a. Texture Addresser (TA) / L1 Backend Data Processing Unit, a.k.a. Texture Data (TD)
+  - L1 Cache (TCP)
+  - L2 Cache (TCC) (both aggregated and per-channel perf info)

 ##### Speed-of-Light
-Speed-of-light panels are provided at both the system and per IP block level to help diagnosis performance bottlenecks. The performance numbers of the workload under testing are compared to the theoretical maximum, (e.g. floating point operations, bandwidth, cache hit rate, etc.), to indicate the available room to further utilize the hardware capability.
+Speed-of-light panels are provided at both the system and per hardware component level to help diagnosis performance bottlenecks. The performance numbers of the workload under testing are compared to the theoretical maximum, (e.g. floating point operations, bandwidth, cache hit rate, etc.), to indicate the available room to further utilize the hardware capability.

 ##### Multi Normalization

@@ -457,24 +457,24 @@ Omniperf enables baseline comparison to allow checking A/B effect. The current r

 For both the Current Workload and the Baseline Workload, one can independently setup the following filters to allow fine grained comparions:
 - Workload Name 
- GPU ID filtering (multi selection)
- Kernel Name filtering (multi selection)
+- GPU ID filtering (multi-selection)
+- Kernel Name filtering (multi-selection)
 - Dispatch ID filtering (Regex filtering)
- Omniperf Panels (multi selection)
+- Omniperf Panels (multi-selection)

 ##### Regex based Dispatch ID filtering
-This release enables regex based dispatch ID filtering to flexibly choose the kernel invocations. One may refer to [Regex Numeric Range Generator](https://3widgets.com/), to generate typical number ranges. 
+This release enables Regular Expression (regex), a standard Linux string matching syntax, based dispatch ID filtering to flexibly choose the kernel invocations. One may refer to [Regex Numeric Range Generator](https://3widgets.com/), to generate typical number ranges.

-For example, if one wants to inspect Dispatch Range from 17 to 48, inclusive, the corresponding regex is : **(1[7-9]|[23]\d|4[0-8])**. The generated express can be copied over for filtering.
+For example, if one wants to inspect Dispatch Range from 17 to 48, inclusive, the corresponding regex is : **(1[7-9]|[23]\d|4[0-8])**. The generated expression can be copied over for filtering.

 ##### Incremental Profiling
 Omniperf supports incremental profiling to significantly speed up performance analysis.

-> Refer to [*IP Block profiling*](https://amdresearch.github.io/omniperf/profiling.html#ip-block-profiling) section for this command. 
+> Refer to [*Hardware Component Filtering*](https://amdresearch.github.io/omniperf/profiling.html#hardware-component-filtering) section for this command.

-By default, the entire application is profiled to collect perfmon counter for all IP blocks, giving a system level view of where the workload stands in terms of performance optimization opportunities and bottlenecks. 
+By default, the entire application is profiled to collect performance counters for all hardware blocks, giving a complete view of where the workload stands in terms of performance optimization opportunities and bottlenecks. 

-After that one may focus on only a few IP blocks, (e.g., L1 Cache or LDS) to closely check the effect of software optimizations, without performing application replay for all other IP Blocks. This saves lots of compute time. In addition, the prior profiling results for other IP blocks are not overwritten. Instead, they can be merged during the import to piece together the system view. 
+After that one may focus on only a few hardware components, (e.g., L1 Cache or LDS) to closely check the effect of software optimizations, without performing application replay for all other hardware components. This saves lots of compute time. In addition, the prior profiling results for other hardware components are not overwritten. Instead, they can be merged during the import to piece together the system view.

 ##### Color Coding
 The uniform color coding is applied to most visualizations (bars, table, diagrams etc). Typically, Yellow color means over 50%, while Red color mean over 90% percent, for easy inspection.
@@ -539,7 +539,7 @@ Connection Options:
  -p , --password                                       The user's password. (will be requested later if it's not set)
  -t , --team                                           Specify Team prefix.
  -w , --workload                                       Specify name of workload (to remove) or path to workload (to import)
-  -k , --kernel-verbose                                 Specify Kernel Name verbose level 1-5. 
+  -k , --kernelVerbose                                  Specify Kernel Name verbose level 1-5. 
                                                        Lower the level, shorter the kernel name. (DEFAULT: 2) (DISABLE: 5)
 ```

@@ -594,7 +594,7 @@ There are currently 18 main panel categories available for analyzing the compute
 - Command Processor
  - Command Processor - Fetch (CPF)
  - Command Processor - Controller (CPC)
- Shader Processing Input (SPI)
+- Workgroup Manager or Shader Processor Input (SPI)
  - SPI Stats
  - SPI Resource Allocations
 - Wavefront Launch
@@ -655,116 +655,357 @@ There are currently 18 main panel categories available for analyzing the compute
  - Per-channel L2-EA Read stall (I/O, GMI, HBM)
  - Per-channel L2-EA Write stall (I/O, GMI, HBM, Starve)

-Most panels are designed around a specific IP block to thoroughly understand its behavior. Additional panels, including custom panels, could also be added to aid the performance analysis.
+Most panels are designed around a specific hardware component block to thoroughly understand its behavior. Additional panels, including custom panels, could also be added to aid the performance analysis.

 ##### System Info Panel
-![System Info Panel](images/System_info_panel.png)
+``` {figure} images/system-info_panel.png
+:alt: System Info
+:figclass: figure
+:align: center
+
+System details logged from host machine.
+```
+
 ##### Kernel Statistics

 ###### Kernel Time Histogram
-![Kernel Time Histogram](images/Kernel_time_histogram.png)
+``` {figure} images/Kernel_time_histogram.png
+:alt: Kernel Time Histogram
+:figclass: figure
+:align: center
+
+Mapping application kernel launches to execution duration.
+```
 ###### Top Bottleneck Kernels
-![Top Bottleneck Kernels](images/Top_bottleneck_kernels.png)
+``` {figure} images/top-stat_panel.png
+:alt: Top Bottleneck Kernels
+:figclass: figure
+:align: center
+
+Top N kernels and relevant statistics. Sorted by total duration.
+```
 ###### Top Bottleneck Dispatches
-![Top Bottleneck Dispatches](images/Top_bottleneck_dispatches.png)
+``` {figure} images/Top_bottleneck_dispatches.png
+:alt: Top Bottleneck Dispatches
+:figclass: figure
+:align: center
+
+Top N kernel dispatches and relevant statistics. Sorted by total duration.
+```
 ###### Current and Baseline Dispatch IDs (Filtered)
-![Current and Baseline Dispatch IDs](images/Current_and_baseline_dispatch_ids.png)
+``` {figure} images/Current_and_baseline_dispatch_ids.png
+:alt: Current and Baseline Dispatch IDs
+:figclass: figure
+:align: center
+
+List of all kernel dispatches.
+```

 ##### System Speed-of-Light
-![System Speed-of-Light](images/System_speed_of_light.png)
+``` {figure} images/sol_panel.png
+:alt: System Speed-of-Light
+:figclass: figure
+:align: center
+
+Key metrics from various sections of Omniperf’s profiling report.
+```

 ##### Memory Chart Analysis
 > Note: The Memory Chart Analysis support multiple normalizations. Due to the space limit, all transactions, when normalized to per-sec, default to unit of Billion transactions per second.

-![Memory Chart Analysis](images/Memory_chart_analysis.png)
+``` {figure} images/memory-chart_panel.png
+:alt: Memory Chart Analysis
+:figclass: figure
+:align: center
+
+A graphical representation of performance data for memory blocks on the GPU.
+```
+
+##### Empirical Roofline Analysis
+``` {figure} images/roofline_panel.png
+:alt: Roofline Analysis
+:figclass: figure
+:align: center
+
+Visualize achieved performance relative to a benchmarked peak performance.
+```

-##### Roofline Analysis
-![Roofline Analysis](images/Roofline_analysis.png)
 ##### Command Processor
-![Command Processor](images/Command_processor.png)
-##### Shader Processing Input (SPI)
-![Shader Processing Input](images/Shader_processing_input.png)
-##### Wavefront Launch
-![Wavefront Launch](images/Wavefront_launch.png)
+###### Command Processor Fetcher
+``` {figure} images/cpc_panel.png
+:alt: Command Processor Fetcher
+:figclass: figure
+:align: center
+
+Fetches commands out of memory to hand them over to the Command Processor Fetcher (CPC) for processing
+```
+###### Command Processor Compute
+``` {figure} images/cpf_panel.png
+:alt: Command Processor Compute
+:figclass: figure
+:align: center
+
+The micro-controller running the command processing firmware that decodes the fetched commands, and (for kernels) passes them to the Workgroup Managers (SPIs) for scheduling.
+```
+
+##### Shader Processor Input (SPI)
+###### SPI Stats
+``` {figure} images/spi-stats_panel.png
+:alt: SPI Stats
+:figclass: figure
+:align: center
+
+TODO: Add caption after merge
+```
+###### SPI Resource Allocation
+``` {figure} images/spi-resource-allocation_panel.png
+:alt: SPI Resource Allocation
+:figclass: figure
+:align: center
+
+TODO: Add caption after merge
+```
+
+##### Wavefront
+###### Wavefront Launch Stats
+``` {figure} images/wavefront-launch-stats_panel.png
+:alt: Wavefront Launch Stats
+:figclass: figure
+:align: center
+
+General information about the kernel launch.
+```
+###### Wavefront Runtime Stats
+``` {figure} images/wavefront-runtime-stats_panel.png
+:alt: Wavefront Runtime Stats
+:figclass: figure
+:align: center
+
+High-level overview of the execution of wavefronts in a kernel.
+```

 ##### Compute Unit - Instruction Mix
 ###### Instruction Mix
-![Instruction Mix](images/Instruction_mix.png)
+``` {figure} images/cu-inst-mix_panel.png
+:alt: Instruction Mix
+:figclass: figure
+:align: center
+
+Breakdown of the various types of instructions executed by the user’s kernel, and which pipelines on the Compute Unit (CU) they were executed on.
+```
 ###### VALU Arithmetic Instruction Mix
-![VALU Arithmetic Instruction Mix](images/VALU_arithmetic_instruction_mix.png)
+``` {figure} images/cu-value-arith-instr-mix_panel.png
+:alt: VALU Arithmetic Instruction Mix
+:figclass: figure
+:align: center
+
+The various types of vector instructions that were issued to the vector arithmetic logic unit (VALU).
+```
 ###### MFMA Arithmetic Instruction Mix
-![MFMA Arithmetic Instruction Mix](images/MFMA_arithmetic_instruction_mix.png)
+``` {figure} images/cu-mafma-arith-instr-mix_panel.png
+:alt: MFMA Arithmetic Instruction Mix
+:figclass: figure
+:align: center
+
+The types of Matrix Fused Multiply-Add (MFMA) instructions that were issued.
+```
 ###### VMEM Arithmetic Instruction Mix
-![VMEM Arithmetic Instruction Mix](images/VMEM_arithmetic_intensity_mix.png)
+``` {figure} images/cu-vmem-instr-mix_panel.png
+:alt: VMEM Arithmetic Instruction Mix
+:figclass: figure
+:align: center
+
+The types of vector memory (VMEM) instructions that were issued.
+```

 ##### Compute Unit - Compute Pipeline
 ###### Speed-of-Light
-![Speed-of-Light](images/Comp_pipe_sol.png)
-###### Compute Pipeline Stats
-![Compute Pipeline Stats](images/Compute_pipeline_stats.png)
+``` {figure} images/cu-sol_panel.png
+:alt: Speed-of-Light
+:figclass: figure
+:align: center
+
+The number of floating-point and integer operations executed on the vector arithmetic logic unit (VALU) and Matrix Fused Multiply-Add (MFMA) units in various precisions.
+```
+###### Pipeline Stats
+``` {figure} images/cu-pipeline-stats_panel.png
+:alt: Pipeline Stats
+:figclass: figure
+:align: center
+
+More detailed metrics to analyze the several independent pipelines found in the Compute Unit (CU).
+```
 ###### Arithmetic Operations
-![Arithmetic Operations](images/Arithmetic_operations.png)
-###### Memory Latencies
-![Memory Latencies](images/Memory_latencies.png)
+``` {figure} images/cu-arith-ops_panel.png
+:alt: Arithmetic Operations
+:figclass: figure
+:align: center
+
+The total number of floating-point and integer operations executed in various precisions.
+```

 ##### Local Data Share (LDS)
 ###### Speed-of-Light
-![Speed-of-Light](images/LDS_sol.png)
+``` {figure} images/lds-sol_panel.png
+:alt: Speed-of-Light
+:figclass: figure
+:align: center
+
+Key metrics for the Local Data Share (LDS) as a comparison with the peak achievable values of those metrics.
+```
 ###### LDS Stats
-![LDS Stats](images/LDS_stats.png)
+``` {figure} images/lds-stats_panel.png
+:alt: LDS Stats
+:figclass: figure
+:align: center
+
+More detailed view of the Local Data Share (LDS) performance.
+```

 ##### Instruction Cache
 ###### Speed-of-Light
-![Speed-of-Light](images/Instruc_cache_sol.png)
+``` {figure} images/instr-cache-sol_panel.png
+:alt: Speed-of-Light
+:figclass: figure
+:align: center
+
+Key metrics of the L1 Instruction (L1I) cache as a comparison with the peak achievable values of those metrics.
+```
 ###### Instruction Cache Stats
-![Instruction Cache Stats](images/Instruction_cache_stats.png)
+``` {figure} images/instr-cache-accesses_panel.png
+:alt: Instruction Cache Stats
+:figclass: figure
+:align: center
+
+More detail on the hit/miss statistics of the L1 Instruction (L1I) cache.
+```

 ##### Scalar L1D Cache
 ###### Speed-of-Light
-![](images/L1D_sol.png)
-###### Constant Cache Stats
-![Constant Cache Stats](images/Vec_L1D_cache_accesses.png)
-###### Constant Cache - L2 Interface
-![Constant Cache - L2 Interface](images/Constant_cache_l2_interface.png)
+``` {figure} images/sl1d-sol_panel.png
+:alt: Speed-of-Light
+:figclass: figure
+:align: center
+
+Key metrics of the Scalar L1 Data (sL1D) cache as a comparison with the peak achievable values of those metrics.
+```
+###### Scalar L1D Cache Accesses
+``` {figure} images/sl1d-cache-accesses_panel.png
+:alt: Scalar L1D Cache Accesses
+:figclass: figure
+:align: center
+
+More detail on the types of accesses made to the Scalar L1 Data (sL1D) cache, and the hit/miss statistics.
+```
+###### Scalar L1D Cache - L2 Interface
+``` {figure} images/sl1d-l12-interface_panel.png
+:alt: Scalar L1D Cache - L2 Interface
+:figclass: figure
+:align: center
+
+More detail on the data requested across the Scalar L1 Data (sL1D) cache <-> L2 interface.
+```

 ##### Texture Address and Texture Data
-###### Texture Address (TA)
-![Texture Address](images/Texture_address.png)
-###### Texture Data (TD)
-![Texture Data](images/Texture_data.png)
+###### Texture Addresser
+``` {figure} images/ta_panel.png
+:alt: Texture Addresser
+:figclass: figure
+:align: center

-##### Vector L1D Cache
+Metric specific to texture addresser (TA) which receives commands (e.g., instructions) and write/atomic data from the Compute Unit (CU), and coalesces them into fewer requests for the cache to process.
+```
+###### Texture Data
+``` {figure} images/td_panel.png
+:alt: Texture Data
+:figclass: figure
+:align: center
+
+Metrics specific to texture data (TD) which routes data back to the requesting Compute Unit (CU).
+```
+
+##### Vector L1 Data Cache
 ###### Speed-of-Light
-![Speed-of-Light](images/Vec_L1D_cache_sol.png)
-###### Vector L1D Cache Accesses
-![Vector L1D Cache Accesses](images/Vec_L1D_cache_accesses.png)
-###### L1 Cache Stalls
-![L1 Cache Stalls](images/L1_cache_stalls.png)
-###### L1 - L2 Transactions
-![L1 - L2 Transactions](images/L1_l2_transactions.png)
-###### L1 - UTCL1 Interface Stats
-![L1 - UTCL1 Interface Stats](images/L1_utcl1_transactions.png)
+``` {figure} images/vl1d-sol_panel.png
+:alt: Speed-of-Light
+:figclass: figure
+:align: center
+
+Key metrics of the vector L1 data (vL1D) cache as a comparison with the peak achievable values of those metrics.
+```
+###### L1D Cache Stalls
+``` {figure} images/vl1d-cache-stalls_panel.png
+:alt: L1D Cache Stalls
+:figclass: figure
+:align: center
+
+More detail on where vector L1 data (vL1D) cache is stalled in the pipeline, which may indicate performance limiters of the cache.
+```
+###### L1D Cache Accesses
+``` {figure} images/vl1d-cache-accesses_panel.png
+:alt: L1D Cache Accesses
+:figclass: figure
+:align: center
+
+The type of requests incoming from the cache frontend, the number of requests that were serviced by the vector L1 data (vL1D) cache, and the number & type of outgoing requests to the L2 cache.
+```
+###### L1D - L2 Transactions
+``` {figure} images/vl1d-l2-transactions_panel.png
+:alt: L1D - L2 Transactions
+:figclass: figure
+:align: center
+
+A more granular look at the types of requests made to the L2 cache.
+```
+###### L1D Addr Translation
+``` {figure} images/vl1d-addr-translation_panel.png
+:alt: L1D Addr Translation
+:figclass: figure
+:align: center
+
+After a vector memory instruction has been processed/coalesced by the address processing unit of the vector L1 data (vL1D) cache, it must be translated from a virtual to physical address. These metrics provide more details on the L1 Translation Lookaside Buffer (TLB) which handles this process.
+```

 ##### L2 Cache
 ###### Speed-of-Light
-![Speed-of-Light](images/L2_cache_sol.png)
-###### L2 Cache Accesses
-![L2 Cache Accesses](images/L2_cache_accesses.png)
-###### L2 - EA Transactions
-![L2 - EA Transactions](images/L2_ea_transactions.png)
-###### L2 - EA Stalls
-![L2 - EA Stalls](images/L2_ea_stalls.png)
+``` {figure} images/l2-sol_panel.png
+:alt: Speed-of-Light
+:figclass: figure
+:align: center

-##### L2 Cache Per Channel Performance
-###### L1-L2 Transactions
-![L1-L2 Transactions](images/L1_l2_transactions_per_channel.png)
-###### L2-EA Transactions
-![L2-EA Transactions](images/L2_ea_transactions_per_channel.png)
-###### L2-EA Latencies
-![L2-EA Latencies](images/L2_ea_latencies_per_channel.png)
-###### L2-EA Stalls
-![L2-EA Stalls](images/L2_ea_stalls_per_channel.png)
-###### L2-EA Write Stalls
-![L2-EA Write Stalls](images/L2_ea_write_stalls_per_channel.png)
-###### L2-EA Write Starvation
-![L2-EA Write Starvation](images/L2_ea_write_starvation_per_channel.png)
+Key metrics about the performance of the L2 cache, aggregated over all the L2 channels, as a comparison with the peak achievable values of those metrics.
+```
+###### L2 Cache Accesses
+``` {figure} images/l2-accesses_panel.png
+:alt: L2 Cache Accesses
+:figclass: figure
+:align: center
+
+Incoming requests to the L2 cache from the vector L1 data (vL1D) cache and other clients (e.g., the sL1D and L1I caches).
+```
+###### L2 - Fabric Transactions
+``` {figure} images/l2-fabric-transactions_panel.png
+:alt: L2 - Fabric Transactions
+:figclass: figure
+:align: center
+
+More detail on the flow of requests through Infinity Fabric™.
+```
+###### L2 - Fabric Interface Stalls
+``` {figure} images/l2-fabric-interface-stalls_panel.png
+:alt: L2 - Fabric Interface Stalls
+:figclass: figure
+:align: center
+
+A breakdown of what types of requests in a kernel caused a stall (e.g., read vs write), and to which locations (e.g., to the accelerator’s local memory, or to remote accelerators/CPUs).
+```
+
+##### L2 Cache Per Channel
+###### Aggregate Stats
+``` {figure} images/l2-per-channel-agg-stats_panel.png
+:alt: Aggregate Stats
+:figclass: figure
+:align: center
+
+L2 Cache per channel performance at a glance. Metrics are aggregated over all available channels.
+```
@@ -32,13 +32,13 @@ def install(package):
 # -- Project information -----------------------------------------------------

 project = "Omniperf"
-copyright = "2022, Audacious Software Group"
+copyright = "2023-2024, Audacious Software Group"
 author = "Audacious Software Group"

 # The short X.Y version
 version = repo_version
 # The full version, including alpha/beta/rc tags
-release = ""
+release = repo_version

 # -- General configuration ---------------------------------------------------

@@ -52,9 +52,12 @@ extensions = [
    "myst_parser",
 ]

-myst_heading_anchors = 2
+show_authors = True
+
+myst_heading_anchors = 4
 # enable replacement of (tm) & friends
-myst_enable_extensions = ["replacements"]
+myst_enable_extensions = ["replacements", "dollarmath"]
+

 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
@@ -112,6 +115,10 @@ html_theme = "sphinx_rtd_theme"
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ["_static"]

+latex_elements = {
+   "sphinxsetup": 'verbatimwrapslines=true, verbatimforcewraps=true',
+}
+

 # -- Options for HTMLHelp output ---------------------------------------------

@@ -130,7 +137,7 @@ html_theme_options = {
    # Toc options
    "collapse_navigation": True,
    "sticky_navigation": True,
-    "navigation_depth": 4,
+    "navigation_depth": 5,
    "includehidden": True,
    "titles_only": False,
 }
@@ -162,6 +169,7 @@ def setup(app):
    app.add_transform(AutoStructify)
    app.add_config_value("docstring_replacements", {}, True)
    app.connect("source-read", replaceString)
+    app.add_css_file("css/custom.css")


 # function to replace version string througout documentation
@@ -6,7 +6,7 @@
   :maxdepth: 4
 ```

-**1. How do I export profiling data I've already generated using Omniperf?**
+**1. How do I export profiling data I have already generated using Omniperf?**

 In order to interact with the Grafana GUI you must sync data with the MongoDB backend. This interaction is done through ***database*** mode.

@@ -35,11 +35,23 @@ $ export LANG=C.UTF-8

 1. Open MobaXterm
 2. In the top ribbon, select `Tunneling`
-![Tunnel Button](images/tunnel_demo1.png)
+``` {image} images/tunnel_demo1.png
+:alt: MobaXterm Tunnel Button
+:class: bg-primary
+:align: center
+```
 This pop up will appear
-![Pop up](images/tunnel_demo2.png)
+``` {image} images/tunnel_demo2.png
+:alt: MobaXterm Pop Up
+:class: bg-primary
+:align: center
+```
 3. Press `New SSH tunnel`
-![Pop up](images/tunnel_demo3.png)
+``` {image} images/tunnel_demo3.png
+:alt: MobaXterm Pop Up
+:class: bg-primary
+:align: center
+```
 4. Configure tunnel accordingly

   Local clients
@@ -52,4 +64,4 @@ This pop up will appear
   SSH Server
   - SSH server: Name of the server one is connecting to
   - SSH login: Username to login to the server
-   - SSH port: 22
+   - SSH port: 22
@@ -10,13 +10,13 @@

 1. **Launch & Profile the target application with the command line profiler**
   
-    The command line profiler launches the target application, calls the rocProfiler API, and collects profile results for the specified kernels, dispatches, and/or IP blocks.  If not specified, Omniperf will default to collecting all available counters for all kernels/dispatches launched by the user's executable.
+    The command line profiler launches the target application, calls the rocProfiler API via the rocProf binary, and collects profile results for the specified kernels, dispatches, and/or hardware components.  If not specified, Omniperf will default to collecting all available counters for all kernels/dispatches launched by the user's executable.

    To collect the default set of data for all kernels in the target application, launch, e.g.:
    ```shell
-    $ omniperf profile -n vcopy_data -- ./vcopy -n 1048576 -b 256
+    $ omniperf profile -n vcopy_data -- ./vcopy 1048576 256
    ```
-    The app runs, each kernel is launched, and profiling results are generated. By default, results are written to (e.g.,) ./workloads/vcopy_data (configurable via the `-n` argument). To collect all requested profile information, it may be required to replay kernels multiple times.
+    The app runs, each kernel is launched, and profiling results are generated. By default, results are written to e.g., ./workloads/vcopy_data (configurable via the `-n` argument). To collect all requested profile information, it may be required to replay kernels multiple times.

 2. **Customize data collection**
    
@@ -25,19 +25,20 @@

    Some common filters include:

-    - `-k`/`--kernel` enables filtering kernels by name. `-d`/`--dispatch` enables filtering based on dispatch ID
-    - `-b`/`--ipblocks` enables collects metrics for only the specified (one or more) IP Blocks.
+    - `-k`/`--kernel` enables filtering kernels by name. 
+    - `-d`/`--dispatch` enables filtering based on dispatch ID.
+    - `-b`/`--ipblocks` enables collects metrics for only the specified (one or more) hardware component blocks.

-    To view available metrics by IP Block you can use the `--list-metrics` argument to view a list of all available metrics organized by IP Block.
+    To view available metrics by IP Block you can use the `--list-metrics` argument:
    ```shell
    $ omniperf analyze --list-metrics <sys_arch>
    ```

 3. **Analyze at the command line**
   
-   After generating a local output folder (./workloads/\<name>), the command line tool can also be used to quickly interface with profiling results. View different metrics derived from your profiled results and get immediate access all metrics organized by IP block.
+   After generating a local output folder (./workloads/\<name>), the command line tool can also be used to quickly interface with profiling results. View different metrics derived from your profiled results and get immediate access all metrics organized by IP blocks.

-   If no kernel, dispatch, or ipblock filters are applied at this stage, analysis will be reflective of the entirety of the profiling data.
+   If no kernel, dispatch, or hardware block filters are applied at this stage, analysis will be reflective of the entirety of the profiling data.

   To interact with profiling results from a different session, users just provide the workload path.  `-p`/`--path` enables users to analyze existing profiling data in the Omniperf CLI.

@@ -55,7 +56,7 @@
 ### Modes
 Modes change the fundamental behavior of the Omniperf command line tool. Depending on which mode is chosen, different command line options become available.

- **Profile**: Target application is launched on the local system utilizing AMD’s [ROC Profiler](https://github.com/ROCm-Developer-Tools/rocprofiler). Depending on the profiling options chosen, selected kernels, dispatches, and/or IP Blocks in the application are profiled and results are stored locally in an output folder (./workloads/\<name>).
+- **Profile**: Target application is launched on the local system using AMD’s [ROC Profiler](https://github.com/ROCm-Developer-Tools/rocprofiler). Depending on the profiling options chosen, selected kernels, dispatches, and/or hardware components in the application are profiled and results are stored locally in an output folder (./workloads/\<name>).

    ```shell
    $ omniperf profile --help
@@ -65,7 +66,7 @@ Modes change the fundamental behavior of the Omniperf command line tool. Dependi

    To gererate a lightweight GUI interface users can add the `--gui` flag to their analysis command.

-    This mode is designed to be a middle ground to the highly detailed Omniperf Grafana GUI and is great for users who want immediate access to an IP Block they’re already familiar with.
+    This mode is designed to be a middle ground to the highly detailed Omniperf Grafana GUI and is great for users who want immediate access to a hardware component they’re already familiar with.

    ```shell
    $ omniperf analyze --help
@@ -90,4 +91,4 @@ Standalone roofline analysis | profile | `--name`, `--roof-only`, `-- <profile_c
 Import a workload to database | database | `--import`, `--host`, `--username`, `--workload`, `--team`
 Remove a workload from database | database | `--remove`, `--host`, `--username`, `--workload`, `--team`
 Launch standalone GUI from CLI | analyze | `--path`, `--gui`
-Interact with profiling results from CLI | analyze | `--path`
+Interact with profiling results from CLI | analyze | `--path`
@@ -8,10 +8,10 @@

 The [Omniperf](https://github.com/AMDResearch/omniperf) Tool is architecturally composed of three major components, as shown in the following figure.

- **Omniperf Profiling**: Acquire raw performance counters via application replay based on the [rocProfiler](https://rocm.docs.amd.com/projects/rocprofiler/en/latest/rocprof.html).  The counters are stored in a comma-seperated value, for further analyis. A set of MI200 specific micro benchmarks are also run to acquire the hierarchical roofline data. The roofline model is not available on earlier accelerators.
+- **Omniperf Profiling**: Acquire raw performance counters via application replay based on [rocProf](https://rocm.docs.amd.com/projects/rocprofiler/en/latest/rocprof.html).  The counters are stored in a comma-seperated value, for further analysis. A set of MI200 specific micro benchmarks are also run to acquire the hierarchical roofline data. The roofline model is not available on earlier accelerators.

 - **Omniperf Grafana Analyzer**: 
-  - *Grafana database import*: All raw performance counters are imported into the backend MongoDB database for Grafana GUI analysis and visualization. Compatibility of previously generated data between Omniperf versions is not necessarily guarenteed.
+  - *Grafana database import*: All raw performance counters are imported into the backend MongoDB database for Grafana GUI analysis and visualization. Compatibility of previously generated data between Omniperf versions is not necessarily guaranteed.
  - *Grafana GUI Analyzer*: A Grafana dashboard is designed to retrieve the raw counters info from the backend database. It also creates the relevant performance metrics and visualization.
 - **Omniperf Standalone GUI Analyzer**: A standalone GUI is provided to enable performance analysis without importing data into the backend database.

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@@ -12,5 +12,6 @@
   getting_started
   profiling
   analysis
+   performance_model
   faq
 ```
@@ -17,7 +17,7 @@ Omniperf is broken into two installation components:
   - Mongo DB backend + Grafana instance
   - Packaged in a Docker container for easy setup

-Determine what you need to install based on how you'd like to interact with Omniperf. See the decision tree below to help determine what installation is right for you.
+Determine what you need to install based on how you would like to interact with Omniperf. See the decision tree below to help determine what installation is right for you.

 ![Omniperf Installtion Decision Tree](images/install_decision_tree.png)

@@ -55,14 +55,14 @@ available from the
 of the Omniperf development site. From there, untar and descend into
 the top-level directory as follows:

-```shell
+```shell-session
 $ tar xfz omniperf-v{__VERSION__}.tar.gz
 $ cd omniperf-v{__VERSION__}
 ```

 Next, install Python dependencies and complete the Omniperf configuration/install process as follows:

-```shell
+```shell-session
 # define top-level install path
 $ export INSTALL_DIR=<your-top-level-desired-install-path>

@@ -87,7 +87,7 @@ do not have write access to the chosen install path.


 After completing these steps, a successful top-level installation directory looks as follows:
-```shell
+```shell-session
 $ ls $INSTALL_DIR
 modulefiles  {__VERSION__}  python-libs
 ```
@@ -102,7 +102,7 @@ follows:



-```shell
+```shell-session
 $ module use $INSTALL_DIR/modulefiles
 $ module load omniperf
 $ which omniperf
@@ -125,14 +125,14 @@ To use Omniperf without the companion modulefile, update your `PATH`
 settings to enable access to the command-line binary. If you installed Python
 dependencies in a shared location, update your `PYTHONPATH` config as well:

-```shell
+```shell-session
 export PATH=$INSTALL_DIR/{__VERSION__}/bin:$PATH
 export PYTHONPATH=$INSTALL_DIR/python-libs
 ```

 ### rocProf

-Omniperf relies on a rocprof binary during the profiling
+Omniperf relies on a rocProf binary during the profiling
 process. Normally the path to this binary will be detected
 automatically, but it can also be overridden via the setting the
 optional `ROCPROF` environment variable to the path of the binary the user
@@ -162,9 +162,9 @@ Omniperf server-side requires the following basic software dependencies prior to

 The recommended process for enabling the server-side of Omniperf is to use the provided Docker file to build the Grafana and MongoDB instance.

-Once you've decided which machine you'd like to use to host the Grafana and MongoDB instance, please follow the set up instructions below.
+Once you have decided which machine you would like to use to host the Grafana and MongoDB instance, please follow the set up instructions below.

-### 1) Install MongoDB Utils
+### Install MongoDB Utils
 Omniperf uses [mongoimport](https://www.mongodb.com/docs/database-tools/mongoimport/) to upload data to Grafana's backend database. Install for Ubuntu 20.04 is as follows:

 ```bash 
@@ -173,7 +173,7 @@ $ sudo apt install ./mongodb-database-tools-ubuntu2004-x86_64-100.6.1.deb
 ```
 > Installation instructions for alternative distributions can be found [here](https://www.mongodb.com/download-center/database-tools/releases/archive)

-### 2) Persistent Storage
+### Persistent Storage

 The user will also bind MongoDB to a directory on the host OS to create a local backup in case of a crash or reset. In the Docker world, this is known as "creating a persistent volume":

@@ -184,24 +184,24 @@ $ sudo docker volume create --driver local --opt type=none --opt device=/usr/loc
 $ sudo docker volume create --driver local --opt type=none --opt device=/usr/local/persist/mongodb --opt o=bind grafana-mongo-db
 ```

-### 3) Build and Launch
+### Build and Launch

-We're now ready to build our Docker file. Navigate to your Omniperf install directory to begin.
+We are now ready to build our Docker file. Navigate to your Omniperf install directory to begin.
 ```bash
 $ sudo docker-compose build
 $ sudo docker-compose up -d
 ```
 > Note that TCP ports for Grafana (4000) and MongoDB (27017) in the docker container are mapped to 14000 and 27018, respectively, on the host side.

-### 4) Setup Grafana Instance
-Once you've launced your docker container you should be able to reach Grafana at **http://\<host-ip>:14000**. The default login credentials for the first-time Grafana setup are:
+### Setup Grafana Instance
+Once you have launched your docker container you should be able to reach Grafana at **http://\<host-ip>:14000**. The default login credentials for the first-time Grafana setup are:

 - Username: **admin**
 - Password: **admin**

 ![Grafana Welcome Page](images/grafana_welcome.png)

-MongoDB Datasource Configuration
+### MongoDB Datasource Configuration

 The MongoDB Datasource must be configured prior to the first-time use. Navigate to Grafana's Configuration page (shown below) to add the **Omniperf Data** connection.

@@ -219,7 +219,7 @@ After properly configuring these fields click **Save & Test** (as shown below) t

 ![Datasource Settings](images/datasource_settings.jpg)

-Omniperf Dashboard Import
+### Omniperf Dashboard Import

 From *Create* → *Import*, (as shown below) upload the dashboard file, `/dashboards/Omniperf_v{__VERSION__}_pub.json`, from the Omniperf tarball.

@@ -227,17 +227,17 @@ Edit both the Dashboard Name and the Unique Identifier (UID) to uniquely identif

 ![Import Dashboard](images/import_dashboard.png)

-Using your dashboard
+### Using your dashboard

-Once you've imported a dashboard you're ready to begin! Start by browsing available dashboards and selecting the dashboard you've just imported.
+Once you have imported a dashboard you are ready to begin! Start by browsing available dashboards and selecting the dashboard you have just imported.

 ![Opening your dashboard](images/opening_dashboard.png)

-Remeber, you'll need to upload workload data to the DB backend before analyzing in your Grafana interface. We provide a detailed example of this in our [Analysis section](./analysis.md#grafana-gui-import).
+Remeber, you will need to upload workload data to the DB backend before analyzing in your Grafana interface. We provide a detailed example of this in our [Analysis section](./analysis.md#grafana-gui-import).

 After a workload has been successfully uploaded, you should be able to select it from the workload dropdown located at the top of your Grafana dashboard.

 ![Selecting Grafana workload](images/grafana_workload_selection.png)

-For more information on how to use the Grafana interface for anlysis please see the [Grafana section](./analysis.md#grafana-based-gui) in the Analyze Mode tab.
+For more information on how to use the Grafana interface for analysis please see the [Grafana section](./analysis.md#grafana-based-gui) in the Analyze Mode tab.

@@ -6,15 +6,19 @@
   :maxdepth: 4
 ```

+This documentation was created to provide a detailed breakdown of all facets of Omniperf. In addition to a full deployment guide with installation instructions, we also explain the design of the tool and each of its components. If you are new to Omniperf, these chapters can be followed in order to gradually acquaint you with the tool and progressively introduce its more advanced features.
+
+This project is proudly open source, and we welcome all feedback! For more details on how to contribute, please see our Contribution Guide.
+
 [Browse Omniperf source code on Github](https://github.com/AMDResearch/omniperf)

-## Scope
+## What is Omniperf

-MI Performance Profiler ([Omniperf](https://github.com/AMDResearch/omniperf)) is a system performance profiling tool for Machine Learning/HPC workloads running on AMD Instinct (tm) Accelerators. It is currently built on top of the [rocProfiler](https://rocm.docs.amd.com/projects/rocprofiler/en/latest/rocprof.html) to monitor hardware performance counters. The Omniperf tool primarily targets accelerators in the MI100 and MI200 families. Development is in progress to support MI300 and Radeon (tm) RDNA (tm) GPUs.
+Omniperf is a kernel level profiling tool for Machine Learning/HPC workloads running on AMD Instinct (tm) MI accelerators. AMD's Instinct (tm) MI accelerators are Data Center GPUs designed for compute and with some graphics functions disabled or removed. Omniperf is currently built on top of [rocProf](https://rocm.docs.amd.com/projects/rocprofiler/en/latest/rocprof.html) to monitor hardware performance counters. The Omniperf tool primarily targets accelerators in the MI100 and MI200 families. Development is in progress to support AMD Instinct (tm) MI300 and Radeon (tm) RDNA (tm) GPUs.

 ## Features

-The Omniperf tool performs system profiling based on all available hardware counters for the target accelerator. It provides high level performance analysis features including System Speed-of-Light, IP block Speed-of-Light, Memory Chart Analysis, Roofline Analysis, Baseline Comparisons, and more...
+The Omniperf tool performs profiling based on all available hardware counters for the target accelerator. It provides high level performance analysis features including System Speed-of-Light, Hardware block level Speed-of-Light, Memory Chart Analysis, Roofline Analysis, Baseline Comparisons, and more...
  
 Both command line analysis and GUI analysis are supported. 

@@ -32,25 +36,25 @@ Detailed Feature List:
 - System Speed-of-Light Panel
 - Kernel Statistic Panel
 - Memory Chart Analysis Panel
- Roofline Analysis Panel (*Supported on MI200 only, SLES 15 SP3 or RHEL8*)
+- Roofline Analysis Panel (*Supported on MI200 only, Ubuntu 20.04, SLES 15 SP3 or RHEL8*)
 - Command Processor (CP) Panel
- Shader Processing Input (SPI) Panel
+- Workgroup Manager (SPI) Panel
 - Wavefront Launch Panel
 - Compute Unit - Instruction Mix Panel
 - Compute Unit - Pipeline Panel
 - Local Data Share (LDS) Panel
 - Instruction Cache Panel
 - Scalar L1D Cache Panel
- Texture Addresser and Data Panel
+- L1 Address Processing Unit, a.k.a. Texture Addresser (TA) / L1 Backend Data Processing Unit, a.k.a. Texture Data (TD) panel(s)
 - Vector L1D Cache Panel
 - L2 Cache Panel
 - L2 Cache (per-Channel) Panel

-## Compatible SOCs
+## Compatible SoCs

 | Platform | Status         |
 | :------- | :------------- |
-| Vega 20 (MI-50/60)  | No  |
+| Vega 20 (MI50/60)  | No  |
 | MI100    | Supported      |
 | MI200    | Supported      |
 | MI300    | In development |
@@ -23,7 +23,7 @@ the MI200 platform.
 $ hipcc vcopy.cpp -o vcopy
 $ ls
 vcopy   vcopy.cpp
-$ ./vcopy -n 1048576 -b 256
+$ ./vcopy 1048576 256
 Finished allocating vectors on the CPU
 Finished allocating vectors on the GPU
 Finished copying vectors to the GPU
@@ -37,49 +37,50 @@ Releasing CPU memory
 ```

 ## Omniperf Profiling
-The *omniperf* script, available through the [Omniperf](https://github.com/AMDResearch/omniperf) repository, is used to aquire all necessary perfmon data through analysis of compute workloads.
+The *omniperf* script, available through the Omniperf repository, is used to aquire all necessary performance monitoring data through analysis of compute workloads.

 **omniperf help:**
 ```shell-session
 $ omniperf profile --help
-usage: 
-            
+ROC Profiler:  /usr/bin/rocprof
+
+usage:
+
 omniperf profile --name <workload_name> [profile options] [roofline options] -- <profile_cmd>

-            
+

 -------------------------------------------------------------------------------
-            
+
 Examples:
-            
-        omniperf profile -n vcopy_all -- ./vcopy -n 1048576 -b 256
-            
-        omniperf profile -n vcopy_SPI_TCC -b SQ TCC -- ./vcopy -n 1048576 -b 256
-            
-        omniperf profile -n vcopy_kernel -k vecCopy -- ./vcopy -n 1048576 -b 256
-            
-        omniperf profile -n vcopy_disp -d 0 -- ./vcopy -n 1048576 -b 256
-            
-        omniperf profile -n vcopy_roof --roof-only -- ./vcopy -n 1048576 -b 256
-            
+
+        omniperf profile -n vcopy_all -- ./vcopy 1048576 256
+
+        omniperf profile -n vcopy_SPI_TCC -b SQ TCC -- ./vcopy 1048576 256
+
+        omniperf profile -n vcopy_kernel -k vecCopy -- ./vcopy 1048576 256
+
+        omniperf profile -n vcopy_disp -d 0 -- ./vcopy 1048576 256
+
+        omniperf profile -n vcopy_roof --roof-only -- ./vcopy 1048576 256
+
 -------------------------------------------------------------------------------

-        
+

 Help:
-  -h, --help                       show this help message and exit
+  -h, --help                      show this help message and exit

 General Options:
-  -v, --version                    show program's version number and exit
-  -V, --verbose                    Increase output verbosity
+  -v, --version                   show program's version number and exit
+  -V, --verbose                   Increase output verbosity

 Profile Options:
  -n , --name                                           Assign a name to workload.
  -p , --path                                           Specify path to save workload.
-                                                        (DEFAULT: {os.getcwd()}/workloads/<name>)
+                                                        (DEFAULT: /home/colramos/GitHub/omniperf/workloads/<name>)
  -k  [ ...], --kernel  [ ...]                          Kernel filtering.
-  -d  [ ...], --dispatch  [ ...]                        Dispatch ID filtering.
-  -b  [ ...], --ipblocks  [ ...]                        IP block filtering:
+  -b  [ ...], --ipblocks  [ ...]                        Hardware block filtering:
                                                           SQ
                                                           SQC
                                                           TA
@@ -89,19 +90,16 @@ Profile Options:
                                                           SPI
                                                           CPC
                                                           CPF
-  --join-type                                           Choose how to join rocprof runs: (DEFAULT: grid)
-                                                           kernel (i.e. By unique kernel name dispatches)
-                                                           grid (i.e. By unique kernel name + grid size dispatches)
+  -d  [ ...], --dispatch  [ ...]                        Dispatch ID filtering.
  --no-roof                                             Profile without collecting roofline data.
  -- [ ...]                                             Provide command for profiling after double dash.
-  --kernel-verbose                                      Specify Kernel Name verbose level 1-5. Lower the level, shorter the kernel name. (DEFAULT: 2) (DISABLE: 5)

 Standalone Roofline Options:
  --roof-only                                           Profile roofline data only.
  --sort                                                Overlay top kernels or top dispatches: (DEFAULT: kernels)
                                                           kernels
                                                           dispatches
-  -m  [ ...], --mem-level  [ ...]                       Filter by memory level: (DEFAULT: ALL)
+  -m , --mem-level                                      Filter by memory level: (DEFAULT: ALL)
                                                           HBM
                                                           L2
                                                           vL1D
@@ -110,45 +108,47 @@ Standalone Roofline Options:
  --kernel-names                                        Include kernel names in roofline plot.
 ```

+- The `-k` \<kernel> flag allows for kernel filtering, which is compatible with the current rocProf utility.
+
+- The `-d` \<dispatch> flag allows for dispatch ID filtering,  which is compatible with the current rocProf utility.
+
+- The `-b` \<ipblocks> allows system profiling on one or more selected hardware components to speed up the profiling process. One can gradually include more hardware components, without overwriting performance data acquired on other hardware components.
+
+
 The following sample command profiles the *vcopy* workload.

 **vcopy profiling:**
 ```shell-session
 $ omniperf profile --name vcopy -- ./vcopy 1048576 256
-ROC Profiler: /opt/rocm-5.2.1/bin/rocprof
-Execution mode = profile
-  ___                  _                  __ 
- / _ \ _ __ ___  _ __ (_)_ __   ___ _ __ / _|
-| | | | '_ ` _ \| '_ \| | '_ \ / _ \ '__| |_ 
-| |_| | | | | | | | | | | |_) |  __/ |  |  _|
- \___/|_| |_| |_|_| |_|_| .__/ \___|_|  |_|  
-                        |_|                  
+Resolving rocprof
+ROC Profiler:  /usr/bin/rocprof

-SoC = mi200
-Profiler choice = rocprofv1
-omniperf ver: 1.0.10
-Path: {os.getcwd()}/workloads/vcopy/mi200
-Target: mi200
-Command: ./vcopy -n 1048576 -b 256
-Kernel Selection: None
-Dispatch Selection: None
+-------------
+Profile only
+-------------
+
+omniperf ver:  1.0.8-PR1
+Path:  /home/colramos/GitHub/omniperf-pub/workloads
+Target:  mi200
+Command:  /home/colramos/vcopy 1048576 256
+Kernel Selection:  None
+Dispatch Selection:  None
 IP Blocks: All
-KernelName verbose: 2
+Log:  /home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/log.txt

-Current input file: {os.getcwd()}/workloads/vcopy/mi200/perfmon/pmc_perf_2.txt
-RPL: on '231120_142934' from '/opt/rocm-5.2.1' in '{os.getcwd()}'
-RPL: profiling '""./vcopy -n 1048576 -b 256""'
-RPL: input file '{os.getcwd()}/workloads/vcopy/mi200/perfmon/pmc_perf_2.txt'
-RPL: output dir '/tmp/rpl_data_231120_142934_262088'
-RPL: result dir '/tmp/rpl_data_231120_142934_262088/input0_results_231120_142934'
-ROCProfiler: input from "/tmp/rpl_data_231120_142934_262088/input0.xml"
+/home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/perfmon/SQ_INST_LEVEL_SMEM.txt
+RPL: on '230411_165021' from '/opt/rocm-5.2.1' in '/home/colramos/GitHub/omniperf-pub'
+RPL: profiling '""/home/colramos/vcopy 1048576 256""'
+RPL: input file '/home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/perfmon/SQ_INST_LEVEL_SMEM.txt'
+RPL: output dir '/tmp/rpl_data_230411_165021_26406'
+RPL: result dir '/tmp/rpl_data_230411_165021_26406/input0_results_230411_165021'
+Finished allocating vectors on the CPU
+ROCProfiler: input from "/tmp/rpl_data_230411_165021_26406/input0.xml"
  gpu_index = 
  kernel = 
  range = 
-  26 metrics
-    SQ_INSTS_VALU_TRANS_F16, SQ_INSTS_VALU_ADD_F32, SQ_INSTS_VALU_MUL_F32, SQ_INSTS_VALU_FMA_F32, SQ_INSTS_VALU_TRANS_F32, SQ_INSTS_VALU_ADD_F64, SQ_INSTS_VALU_MUL_F64, SQ_INSTS_VALU_FMA_F64, TCP_VOLATILE_sum, TCP_TOTAL_ACCESSES_sum, TCP_TOTAL_READ_sum, TCP_TOTAL_WRITE_sum, TA_BUFFER_ATOMIC_WAVEFRONTS_sum, TA_BUFFER_TOTAL_CYCLES_sum, TD_ATOMIC_WAVEFRONT_sum, TD_STORE_WAVEFRONT_sum, SPI_RA_REQ_NO_ALLOC, SPI_RA_REQ_NO_ALLOC_CSN, CPC_CPC_STAT_STALL, CPC_UTCL1_STALL_ON_TRANSLATION, CPF_CPF_STAT_IDLE, CPF_CPF_TCIU_IDLE, TCC_REQ_sum, TCC_STREAMING_REQ_sum, TCC_HIT_sum, TCC_MISS_sum
-vcopy testing on GCD 0
-Finished allocating vectors on the CPU
+  3 metrics
+    SQ_INSTS_SMEM, SQ_INST_LEVEL_SMEM, SQ_ACCUM_PREV_HIRES
 Finished allocating vectors on the GPU
 Finished copying vectors to the GPU
 sw thinks it moved 1.000000 KB per wave 
@@ -158,11 +158,10 @@ Finished executing kernel
 Finished copying the output vector from the GPU to the CPU
 Releasing GPU memory
 Releasing CPU memory
- 
-...

-Empirical Roofline Calculation
-Copyright © 2022  Advanced Micro Devices, Inc. All rights reserved.
+... ...
+ROCPRofiler: 1 contexts collected, output directory /tmp/rpl_data_220527_130317_1787038/input_results_220527_130317
+File 'workloads/vcopy/mi200/timestamps.csv' is generating
 Total detected GPU devices: 2
 GPU Device 0: Profiling...
 99% [||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ]
@@ -211,14 +210,14 @@ Peak MFMA FLOPs (F64), GPU ID: 1, workgroupSize:256, workgroups:16384, experimen
 99% [||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ]
 Peak MFMA IOPs (I8), GPU ID: 1, workgroupSize:256, workgroups:16384, experiments:100, IOP:2147483648000, duration:14.3 ms, mean:150317.8 GOPS, stdev=203.5 GOPS
 ```
-You'll notice two stages in *default* Omniperf profiling. The first stage collects all the counters needed for Omniperf analysis (omitting any filters you've provided). The second stage collects data for the roofline analysis (this stage can be disabled using `--no-roof`)
+You will notice two stages in *default* Omniperf profiling. The first stage collects all the counters needed for Omniperf analysis (omitting any filters you have provided). The second stage collects data for the roofline analysis (this stage can be disabled using `--no-roof`)

-At the end of the profiling, all resulting csv files should be located in a SOC specific target directory, e.g.:
-  - "mi200" for the AMD Instinct (tm) MI-200 family of accelerators
-  - "mi100" for the AMD Instinct (tm) MI-100 family of accelerators
-etc.  The SOC names are generated as a part of Omniperf, and do not necessarily distinguish between different accelerators in the same family (e.g., an AMD Instinct (tm) MI-210 vs an MI-250)
+In this document, we use the term System on Chip (SoC) to refer to a particular family of accelerators. At the end of profiling, all resulting csv files should be located in a SoC specific target directory, e.g.:
+  - "mi200" for the AMD Instinct (tm) MI200 family of accelerators
+  - "mi100" for the AMD Instinct (tm) MI100 family of accelerators
+etc.  The SoC names are generated as a part of Omniperf, and do not necessarily distinguish between different accelerators in the same family (e.g., an AMD Instinct (tm) MI210 vs an MI250)

-> Note: Additionally, you'll notice a few extra files. An SoC parameters file, *sysinfo.csv*, is created to reflect the target device settings. All profiling output is stored in *log.txt*. Roofline specific benchmark results are stored in *roofline.csv*.
+> Note: Additionally, you will notice a few extra files. An SoC parameters file, *sysinfo.csv*, is created to reflect the target device settings. All profiling output is stored in *log.txt*. Roofline specific benchmark results are stored in *roofline.csv*.

 ```shell-session
 $ ls workloads/vcopy/mi200/
@@ -239,17 +238,17 @@ drwxrwxr-x 2 colramos colramos  4096 Apr 11 16:42 perfmon
 ```

 ### Filtering
-To reduce profiling time and the counters collected one may use profiling filters. Profiling filters and their functionality depend on the underlying profiler being used. While Omniperf is profiler agnostic, we've provided a detailed description of profiling filters available when using Omniperf with [rocProfiler](https://rocm.docs.amd.com/projects/rocprofiler/en/latest/rocprof.html) below.
+To reduce profiling time and the counters collected one may use profiling filters. Profiling filters and their functionality depend on the underlying profiler being used. While Omniperf is profiler agnostic, we have provided a detailed description of profiling filters available when using Omniperf with [rocProf](https://rocm.docs.amd.com/projects/rocprofiler/en/latest/rocprof.html) below.



 Filtering Options:

- The `-k` \<kernel> flag allows for kernel filtering. Useage is equivalent with the current rocprof utility ([see details below](#kernel-filtering)).
+- The `-k` \<kernel> flag allows for kernel filtering. Useage is equivalent with the current rocProf utility ([see details below](#kernel-filtering)).

- The `-d` \<dispatch> flag allows for dispatch ID filtering. Useage is equivalent with the current rocprof utility ([see details below](#dispatch-filtering)).
+- The `-d` \<dispatch> flag allows for dispatch ID filtering. Useage is equivalent with the current rocProf utility ([see details below](#dispatch-filtering)).

- The `-b` \<ipblocks> allows system profiling on one or more selected IP blocks to speed up the profiling process. One can gradually incorporate more IP blocks, without overwriting performance data acquired on other IP blocks.
+- The `-b` \<ipblocks> allows system profiling on one or more selected hardware components to speed up the profiling process. One can gradually include more hardware components, without overwriting performance data acquired on other hardware components.

 ```{note}
 Be cautious while combining different profiling filters in the same call. Conflicting filters may result in error.
@@ -257,75 +256,80 @@ Be cautious while combining different profiling filters in the same call. Confli
 i.e. filtering dispatch X, but dispatch X does not match your kernel name filter
 ```

-#### IP Block Filtering
-One can profile a selected IP Block to speed up the profiling process. All profiling results are accumulated in the same target directory, without overwriting those for other IP blocks, hence enabling the incremental profiling and analysis.
+#### Hardware Component Filtering
+One can profile specific hardware components to speed up the profiling process. In Omniperf, we use the term IP block to refer to a hardware component or a group of hardware components. All profiling results are accumulated in the same target directory, without overwriting those for other hardware components, hence enabling the incremental profiling and analysis.

-The following example only gathers hardware counters for SQ and TCC, skipping all other IP Blocks:
+The following example only gathers hardware counters for the Shader Sequencer (SQ) and L2 Cache (TCC) components, skipping all other hardware components:
 ```shell-session
-$ omniperf profile --name vcopy -b SQ TCC -- ./sample/vcopy -n 1048576 -b 256
-ROC Profiler: /opt/rocm-5.2.1/bin/rocprof
-Execution mode = profile
-  ___                  _                  __ 
- / _ \ _ __ ___  _ __ (_)_ __   ___ _ __ / _|
-| | | | '_ ` _ \| '_ \| | '_ \ / _ \ '__| |_ 
-| |_| | | | | | | | | | | |_) |  __/ |  |  _|
- \___/|_| |_| |_|_| |_|_| .__/ \___|_|  |_|  
-                        |_|                  
+$ omniperf profile --name vcopy -b SQ TCC -- ./sample/vcopy 1048576 256
+Resolving rocprof
+ROC Profiler:  /usr/bin/rocprof

-SoC = mi200
-Profiler choice = rocprofv1
-fname: pmc_cpc_perf: Skipped
-fname: pmc_spi_perf: Skipped
-fname: pmc_cpf_perf: Skipped
-fname: pmc_tcp_perf: Skipped
-fname: pmc_sq_perf4: Added
-fname: pmc_tcc_perf: Added
-fname: pmc_sq_perf8: Added
-fname: pmc_ta_perf: Skipped
-fname: pmc_sq_perf1: Added
-fname: pmc_sq_perf3: Added
+
+-------------
+Profile only
+-------------
+
+omniperf ver:  1.0.8-PR1
+Path:  /home/colramos/GitHub/omniperf-pub/workloads
+Target:  mi200
+Command:  /home/colramos/vcopy 1048576 256
+Kernel Selection:  None
+Dispatch Selection:  None
+IP Blocks:  ['SQ', 'TCC']
+fname: pmc_sq_perf2: Added
 fname: pmc_td_perf: Skipped
 fname: pmc_tcc2_perf: Skipped
+fname: pmc_tcp_perf: Skipped
+fname: pmc_spi_perf: Skipped
+fname: pmc_sq_perf4: Added
 fname: pmc_sqc_perf1: Skipped
+fname: pmc_tcc_perf: Added
+fname: pmc_cpf_perf: Skipped
+fname: pmc_sq_perf8: Added
+fname: pmc_cpc_perf: Skipped
+fname: pmc_sq_perf1: Added
+fname: pmc_ta_perf: Skipped
+fname: pmc_sq_perf3: Added
 fname: pmc_sq_perf6: Added
-fname: pmc_sq_perf2: Added
-omniperf ver: 1.0.10
-Path: {os.getcwd()}/vcopy_/mi200
-Target: mi200
-Command: ./vcopy -n 1048576 -b 256
-Kernel Selection: None
-Dispatch Selection: None
-IP Blocks: ['sq', 'tcc']
-KernelName verbose: 2
+Log:  /home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/log.txt
 ...
 ```

 #### Kernel Filtering
-Kernel filtering is based on the name of the kernel(s) you'd like to isolate. Use a kernel name substring list to isolate desired kernels.
+Kernel filtering is based on the name of the kernel(s) you would like to isolate. Use a kernel name substring list to isolate desired kernels.

 The following example demonstrates profiling isolating the kernel matching substring "vecCopy":
 ```shell-session
-$ omniperf profile --name vcopy -k vecCopy -- ./vcopy -n 1048576 -b 256
-ROC Profiler: /opt/rocm-5.2.1/bin/rocprof
-Execution mode = profile
-  ___                  _                  __ 
- / _ \ _ __ ___  _ __ (_)_ __   ___ _ __ / _|
-| | | | '_ ` _ \| '_ \| | '_ \ / _ \ '__| |_ 
-| |_| | | | | | | | | | | |_) |  __/ |  |  _|
- \___/|_| |_| |_|_| |_|_| .__/ \___|_|  |_|  
-                        |_|                  
+$ omniperf profile --name vcopy -k vecCopy -- ./vcopy 1048576 256
+Resolving rocprof
+ROC Profiler:  /usr/bin/rocprof

-SoC = mi200
-Profiler choice = rocprofv1
-omniperf ver: 1.0.10
-Path: {os.getcwd()}/workloads/vcopy/mi200
-Target: mi200
-Command: ./vcopy -n 1048576 -b 256
-Kernel Selection: ['vecCopy']
-Dispatch Selection: None
+-------------
+Profile only
+-------------
+
+omniperf ver:  1.0.8-PR1
+Path:  /home/colramos/GitHub/omniperf-pub/workloads
+Target:  mi200
+Command:  /home/colramos/vcopy 1048576 256
+Kernel Selection:  ['vecCopy']
+Dispatch Selection:  None
 IP Blocks: All
-KernelName verbose: 2
-...
+Log:  /home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/log.txt
+
+/home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/perfmon/SQ_INST_LEVEL_SMEM.txt
+RPL: on '230411_170300' from '/opt/rocm-5.2.1' in '/home/colramos/GitHub/omniperf-pub'
+RPL: profiling '""/home/colramos/vcopy 1048576 256""'
+RPL: input file '/home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/perfmon/SQ_INST_LEVEL_SMEM.txt'
+RPL: output dir '/tmp/rpl_data_230411_170300_29696'
+RPL: result dir '/tmp/rpl_data_230411_170300_29696/input0_results_230411_170300'
+Finished allocating vectors on the CPU
+ROCProfiler: input from "/tmp/rpl_data_230411_170300_29696/input0.xml"
+  gpu_index = 
+  kernel = vecCopy
+
+... ...
 ```

 #### Dispatch Filtering
@@ -333,70 +337,76 @@ Dispatch filtering is based on the *global* dispatch index of kernels in a run.

 The following example profiles only the 0th dispatched kernel in execution of the application:
 ```shell-session
-$ omniperf profile --name vcopy -d 0 -- ./vcopy -n 1048576 -b 256
-ROC Profiler: /opt/rocm-5.2.1/bin/rocprof
-Execution mode = profile
-  ___                  _                  __ 
- / _ \ _ __ ___  _ __ (_)_ __   ___ _ __ / _|
-| | | | '_ ` _ \| '_ \| | '_ \ / _ \ '__| |_ 
-| |_| | | | | | | | | | | |_) |  __/ |  |  _|
- \___/|_| |_| |_|_| |_|_| .__/ \___|_|  |_|  
-                        |_|                  
+$ omniperf profile --name vcopy -d 0 -- ./vcopy 1048576 256
+Resolving rocprof
+ROC Profiler:  /usr/bin/rocprof

-SoC = mi200
-Profiler choice = rocprofv1
-omniperf ver: 1.0.10
-Path: {os.getcwd()}/workloads/vcopy/mi200
-Target: mi200
-Command: ./vcopy -n 1048576 -b 256
-Kernel Selection: None
-Dispatch Selection: ['0']
+-------------
+Profile only
+-------------
+
+omniperf ver:  1.0.8-PR1
+Path:  /home/colramos/GitHub/omniperf-pub/workloads
+Target:  mi200
+Command:  /home/colramos/vcopy 1048576 256
+Kernel Selection:  None
+Dispatch Selection:  ['0']
 IP Blocks: All
-KernelName verbose: 2
+Log:  /home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/log.txt
+
+/home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/perfmon/SQ_INST_LEVEL_SMEM.txt
+RPL: on '230411_170356' from '/opt/rocm-5.2.1' in '/home/colramos/GitHub/omniperf-pub'
+RPL: profiling '""/home/colramos/vcopy 1048576 256""'
+RPL: input file '/home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200/perfmon/SQ_INST_LEVEL_SMEM.txt'
+RPL: output dir '/tmp/rpl_data_230411_170356_30314'
+RPL: result dir '/tmp/rpl_data_230411_170356_30314/input0_results_230411_170356'
+Finished allocating vectors on the CPU
+ROCProfiler: input from "/tmp/rpl_data_230411_170356_30314/input0.xml"
+  gpu_index = 
+  kernel = 
+  range = 0
 ...
 ```


-
 ### Standalone Roofline
-If you're only interested in generating roofline analysis data try using `--roof-only`. This will only collect counters relevent to roofline, as well as generate a standalone .pdf output of your roofline plot. 
+If you are only interested in generating roofline analysis data try using `--roof-only`. This will only collect counters relevant to roofline, as well as generate a standalone .pdf output of your roofline plot. 

 Standalone Roofline Options:

- The `--sort` \<desired_sort> allows you to specify whether you'd like to overlay top kernel or top dispatch data in your roofline plot.
+- The `--sort` \<desired_sort> allows you to specify whether you would like to overlay top kernel or top dispatch data in your roofline plot.

- The `-m` \<cache_level> allows you to specify specific level(s) of cache you'd like to include in your roofline plot.
+- The `-m` \<cache_level> allows you to specify specific level(s) of cache you would like to include in your roofline plot.

 - The `--device` \<gpu_id> allows you to specify a device id to collect performace data from when running our roofline benchmark on your system.

- If you'd like to distinguish different kernels in your .pdf roofline plot use `--kernel-names`. This will give each kernel a unique marker identifiable from the plot's key.
+- If you would like to distinguish different kernels in your .pdf roofline plot use `--kernel-names`. This will give each kernel a unique marker identifiable from the plot's key.


 #### Roofline Only
 The following example demonstrates profiling roofline data only:
 ```shell-session
-$ omniperf profile --name vcopy --roof-only -- ./vcopy -n 1048576 -b 256
-ROC Profiler: /opt/rocm-5.2.1/bin/rocprof
-Execution mode = profile
-  ___                  _                  __ 
- / _ \ _ __ ___  _ __ (_)_ __   ___ _ __ / _|
-| | | | '_ ` _ \| '_ \| | '_ \ / _ \ '__| |_ 
-| |_| | | | | | | | | | | |_) |  __/ |  |  _|
- \___/|_| |_| |_|_| |_|_| .__/ \___|_|  |_|  
-                        |_|                  
+$ omniperf profile --name vcopy --roof-only -- ./vcopy 1048576 256
+Resolving rocprof
+ROC Profiler:  /usr/bin/rocprof

-SoC = mi200
-Profiler choice = rocprofv1
-[roofline] Generating pmc_perf.csv
-omniperf ver: 1.0.10
-Path: {os.getcwd()}/workloads/vcopy/mi200
-Target: mi200
-Command: ./vcopy -n 1048576 -b 256
-Kernel Selection: None
-Dispatch Selection: None
-IP Blocks: All
-KernelName verbose: 2
-...
+
+--------
+Roofline only
+--------
+
+Checking for roofline.csv in  /home/colramos/GitHub/omniperf-pub/workloads/vcopy/mi200
+No roofline data found. Generating...
+Empirical Roofline Calculation
+Copyright © 2022  Advanced Micro Devices, Inc. All rights reserved.
+Total detected GPU devices: 4
+GPU Device 0: Profiling...
+ 99% [||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ]
+ ... ...
+Checking for roofline.csv in  /home/colramos/GitHub/omniperf-pub/workloads/mix/mi200
+Checking for sysinfo.csv in  /home/colramos/GitHub/omniperf-pub/workloads/mix/mi200
+Checking for pmc_perf.csv in  /home/colramos/GitHub/omniperf-pub/workloads/mix/mi200
+Empirical Roofline PDFs saved!
 ```
 An inspection of our workload output folder shows .pdf plots were generated successfully
 ```shell-session
@@ -415,4 +425,4 @@ drwxrwxr-x 2 colramos colramos  4096 Apr 11 17:16 perfmon
 ```
 A sample *empirRoof_gpu-ALL_fp32.pdf* looks something like this:

-![Sample Standalone Roof Plot](images/sample-roof-plot.png)
+![Sample Standalone Roof Plot](images/sample-roof-plot.png)