Updated links in documentation. (#328)

Updated to reflect new GitHub organization. Fixed broken links to GitHub pages. Signed-off-by: David Galiffi <David.Galiffi@amd.com> [ROCm/rocprofiler-compute commit: f5712875aa]
2024-03-21 11:14:37 -04:00
@@ -12,7 +12,7 @@ While analyzing with the CLI offers quick and straightforward access to Omniperf
 See sections below for more information on each.

 ## CLI Analysis
-> Profiling results from the [aforementioned vcopy workload](https://amdresearch.github.io/omniperf/profiling.html#workload-compilation) will be used in the following sections to demonstrate the use of Omniperf in MI GPU performance analysis. Unless otherwise noted, the performance analysis is done on the MI200 platform.
+> Profiling results from the [aforementioned vcopy workload](https://rocm.github.io/omniperf/profiling.html#workload-compilation) will be used in the following sections to demonstrate the use of Omniperf in MI GPU performance analysis. Unless otherwise noted, the performance analysis is done on the MI200 platform.

 ### Features

@@ -171,7 +171,7 @@ $ omniperf analyze -p workloads/vcopy/mi200/ --list-metrics gfx90a
 ├─────────┼─────────────────────────────┤
 ...
 ```
- 2. Choose your own customized subset of metrics with `-b` (a.k.a. `--metric`), or build your own config following [config_template](https://github.com/AMDResearch/omniperf/blob/main/src/omniperf_analyze/configs/panel_config_template.yaml). Below shows how to generate a report containing only metric 2 (a.k.a. System Speed-of-Light).
+ 2. Choose your own customized subset of metrics with `-b` (a.k.a. `--metric`), or build your own config following [config_template](https://github.com/ROCm/omniperf/blob/main/src/omniperf_analyze/configs/panel_config_template.yaml). Below shows how to generate a report containing only metric 2 (a.k.a. System Speed-of-Light).
 ```shell-session
 $ omniperf analyze -p workloads/vcopy/mi200/ -b 2
 --------
@@ -351,7 +351,7 @@ be generated directly from the command-line. This option is provided
 as an alternative for users wanting to explore profiling results
 graphically, but without the additional setup requirements or
 server-side overhead of Omniperf's detailed [Grafana
-interface](https://amdresearch.github.io/omniperf/analysis.html#grafana-based-gui)
+interface](https://rocm.github.io/omniperf/analysis.html#grafana-based-gui)
 option.  The standalone GUI analyzer is provided as simple
 [Flask](https://flask.palletsprojects.com/en/2.2.x/) application
 allowing users to view results from within a web browser.
@@ -365,7 +365,7 @@ between the desired web browser host (e.g. login node or remote workstation) and
 required. Alternatively, users may find it more convenient to download
 profiled workloads to perform analysis on their local system.

-See [FAQ](https://amdresearch.github.io/omniperf/faq.html) for more details on SSH tunneling.
+See [FAQ](https://rocm.github.io/omniperf/faq.html) for more details on SSH tunneling.
 ```

 #### Usage
@@ -420,7 +420,7 @@ Once you have applied a filter, you will also see several additional
 sections become available with detailed metrics specific to that area
 of AMD hardware. These detailed sections mirror the data displayed in
 Omniperf's [Grafana
-interface](https://amdresearch.github.io/omniperf/analysis.html#grafana-based-gui).
+interface](https://rocm.github.io/omniperf/analysis.html#grafana-based-gui).

 ### Grafana-based GUI

@@ -470,7 +470,7 @@ For example, if one wants to inspect Dispatch Range from 17 to 48, inclusive, th
 ##### 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 [*IP Block profiling*](https://rocm.github.io/omniperf/profiling.html#ip-block-profiling) 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. 

@@ -74,7 +74,7 @@ Modes change the fundamental behavior of the Omniperf command line tool. Dependi
 - **Database**: Our detailed Grafana GUI is built on a MongoDB database. `--import` profiling results to the DB to interact with the workload in Grafana or `--remove` the workload from the DB.

    Connection options will need to be specified. See the [*Grafana
-    Analysis*](https://amdresearch.github.io/omniperf/analysis.html#grafana-gui-import) import section
+    Analysis*](https://rocm.github.io/omniperf/analysis.html#grafana-gui-import) import section
    for more details on this.

    ```shell
@@ -6,7 +6,7 @@
   :maxdepth: 4
 ```

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

@@ -1,4 +1,4 @@
-# Welcome to the [Omniperf](https://github.com/AMDResearch/omniperf) Documentation!
+# Welcome to the [Omniperf](https://github.com/ROCm/omniperf) Documentation!

 ```eval_rst
 .. toctree::
@@ -51,7 +51,7 @@ defined as follows:

 A typical install will begin by downloading the latest release tarball
 available from the
-[Releases](https://github.com/AMDResearch/omniperf/releases) section
+[Releases](https://github.com/ROCm/omniperf/releases) section
 of the Omniperf development site. From there, untar and descend into
 the top-level directory as follows:

@@ -6,11 +6,11 @@
   :maxdepth: 4
 ```

-[Browse Omniperf source code on Github](https://github.com/AMDResearch/omniperf)
+[Browse Omniperf source code on Github](https://github.com/ROCm/omniperf)

 ## Scope

-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.
+MI Performance Profiler ([Omniperf](https://github.com/ROCm/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.

 ## Features

@@ -6,7 +6,7 @@
   :maxdepth: 5
 ```

-The [Omniperf](https://github.com/AMDResearch/omniperf) repository
+The [Omniperf](https://github.com/ROCm/omniperf) repository
 includes source code for a sample GPU compute workload,
 __vcopy.cpp__. A copy of this file is available in the `share/sample`
 subdirectory after a normal Omniperf installation, or via the
@@ -37,7 +37,7 @@ Releasing CPU memory
 ```

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

 **omniperf help:**
 ```shell-session
@@ -12,7 +12,7 @@ While analyzing with the CLI offers quick and straightforward access to Omniperf
 See sections below for more information on each.

 ## CLI Analysis
-> Profiling results from the [aforementioned vcopy workload](https://amdresearch.github.io/omniperf/profiling.html#workload-compilation) will be used in the following sections to demonstrate the use of Omniperf in MI GPU performance analysis. Unless otherwise noted, the performance analysis is done on the MI200 platform.
+> Profiling results from the [aforementioned vcopy workload](https://rocm.github.io/omniperf/profiling.html#workload-compilation) will be used in the following sections to demonstrate the use of Omniperf in MI GPU performance analysis. Unless otherwise noted, the performance analysis is done on the MI200 platform.

 ### Features

@@ -266,7 +266,7 @@ Analysis mode = cli
                2.1.30 -> L1I Fetch Latency
 ...
 ```
-3. Choose your own customized subset of metrics with `-b` (a.k.a. `--block`), or build your own config following [config_template](https://github.com/AMDResearch/omniperf/blob/main/src/omniperf_analyze/configs/panel_config_template.yaml). Below shows how to generate a report containing only metric 2 (a.k.a. System Speed-of-Light).
+3. Choose your own customized subset of metrics with `-b` (a.k.a. `--block`), or build your own config following [config_template](https://github.com/ROCm/omniperf/blob/main/src/omniperf_analyze/configs/panel_config_template.yaml). Below shows how to generate a report containing only metric 2 (a.k.a. System Speed-of-Light).
 ```shell-session
 $ omniperf analyze -p workloads/vcopy/MI200/ -b 2
 --------
@@ -363,7 +363,7 @@ be generated directly from the command-line. This option is provided
 as an alternative for users wanting to explore profiling results
 graphically, but without the additional setup requirements or
 server-side overhead of Omniperf's detailed [Grafana
-interface](https://amdresearch.github.io/omniperf/analysis.html#grafana-based-gui)
+interface](https://rocm.github.io/omniperf/analysis.html#grafana-based-gui)
 option.  The standalone GUI analyzer is provided as simple
 [Flask](https://flask.palletsprojects.com/en/2.2.x/) application
 allowing users to view results from within a web browser.
@@ -377,7 +377,7 @@ between the desired web browser host (e.g. login node or remote workstation) and
 required. Alternatively, users may find it more convenient to download
 profiled workloads to perform analysis on their local system.

-See [FAQ](https://amdresearch.github.io/omniperf/faq.html) for more details on SSH tunneling.
+See [FAQ](https://rocm.github.io/omniperf/faq.html) for more details on SSH tunneling.
 ```

 #### Usage
@@ -437,7 +437,7 @@ Once you have applied a filter, you will also see several additional
 sections become available with detailed metrics specific to that area
 of AMD hardware. These detailed sections mirror the data displayed in
 Omniperf's [Grafana
-interface](https://amdresearch.github.io/omniperf/analysis.html#grafana-based-gui).
+interface](https://rocm.github.io/omniperf/analysis.html#grafana-based-gui).

 ### Grafana-based GUI

@@ -487,7 +487,7 @@ For example, if one wants to inspect Dispatch Range from 17 to 48, inclusive, th
 ##### Incremental Profiling
 Omniperf supports incremental profiling to significantly speed up performance analysis.

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

 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. 

@@ -75,7 +75,7 @@ Modes change the fundamental behavior of the Omniperf command line tool. Dependi
 - **Database**: Our detailed Grafana GUI is built on a MongoDB database. `--import` profiling results to the DB to interact with the workload in Grafana or `--remove` the workload from the DB.

    Connection options will need to be specified. See the [*Grafana
-    Analysis*](https://amdresearch.github.io/omniperf/analysis.html#grafana-gui-import) import section
+    Analysis*](https://rocm.github.io/omniperf/analysis.html#grafana-gui-import) import section
    for more details on this.

    ```shell
@@ -6,7 +6,7 @@
   :maxdepth: 4
 ```

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

@@ -1,4 +1,4 @@
-# Welcome to the [Omniperf](https://github.com/AMDResearch/omniperf) Documentation!
+# Welcome to the [Omniperf](https://github.com/ROCm/omniperf) Documentation!

 ```eval_rst
 .. toctree::
@@ -51,7 +51,7 @@ defined as follows:

 A typical install will begin by downloading the latest release tarball
 available from the
-[Releases](https://github.com/AMDResearch/omniperf/releases) section
+[Releases](https://github.com/ROCm/omniperf/releases) section
 of the Omniperf development site. From there, untar and descend into
 the top-level directory as follows:

@@ -10,7 +10,7 @@ This documentation was created to provide a detailed breakdown of all facets of

 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)
+[Browse Omniperf source code on Github](https://github.com/ROCm/omniperf)

 ## What is Omniperf

@@ -2178,7 +2178,7 @@ A good discussion of coarse and fine grained memory allocations and what type of
 (VALU_inst_mix_example)=
 ## VALU Arithmetic Instruction Mix

-For this example, we consider the [instruction mix sample](https://github.com/AMDResearch/omniperf/blob/dev/sample/instmix.hip) distributed as a part of Omniperf.
+For this example, we consider the [instruction mix sample](https://github.com/ROCm/omniperf/blob/dev/sample/instmix.hip) distributed as a part of Omniperf.

 ```{note}
 This example is expected to work on all CDNA accelerators, however the results in this section were collected on an [MI2XX](2xxnote) accelerator
@@ -2269,7 +2269,7 @@ shows that we have exactly one of each type of VALU arithmetic instruction, by c
 (Fabric_transactions_example)=
 ## Infinity-Fabric(tm) transactions

-For this example, we consider the [Infinity Fabric(tm) sample](https://github.com/AMDResearch/omniperf/blob/dev/sample/fabric.hip) distributed as a part of Omniperf.
+For this example, we consider the [Infinity Fabric(tm) sample](https://github.com/ROCm/omniperf/blob/dev/sample/fabric.hip) distributed as a part of Omniperf.
 This code launches a simple read-only kernel, e.g.:

 ```c++
@@ -2826,7 +2826,7 @@ On an AMD [MI2XX](2xxnote) accelerator, for FP32 values this will generate a `gl
 (flatmembench)=
 ### Global / Generic (FLAT)

-For this example, we consider the [vector-memory sample](https://github.com/AMDResearch/omniperf/blob/dev/sample/vmem.hip) distributed as a part of Omniperf.
+For this example, we consider the [vector-memory sample](https://github.com/ROCm/omniperf/blob/dev/sample/vmem.hip) distributed as a part of Omniperf.
 This code launches many different versions of a simple read/write/atomic-only kernels targeting various address spaces, e.g. below is our simple `global_write` kernel:

 ```c++
@@ -2976,7 +2976,7 @@ The assembly in these experiments were generated for an [MI2XX](2xxnote) acceler
 Next, we examine a generic write.
 As discussed [previously](Flat_design), our `generic_write` kernel uses an address space cast to _force_ the compiler to choose our desired address space, regardless of other optimizations that may be possible.

-We also note that the `filter` parameter passed in as a kernel argument (see [example](https://github.com/AMDResearch/omniperf/blob/dev/sample/vmem.hip), or [design note](Flat_design)) is set to zero on the host, such that we always write to the 'local' (LDS) memory allocation `lds`.
+We also note that the `filter` parameter passed in as a kernel argument (see [example](https://github.com/ROCm/omniperf/blob/dev/sample/vmem.hip), or [design note](Flat_design)) is set to zero on the host, such that we always write to the 'local' (LDS) memory allocation `lds`.

 Examining this kernel in the VMEM Instruction Mix table yields: 

@@ -3339,7 +3339,7 @@ Next we examine the use of 'Spill/Scratch' memory.
 On current CDNA accelerators such as the [MI2XX](2xxnote), this is implemented using the [private](mspace) memory space, which maps to ['scratch' memory](https://llvm.org/docs/AMDGPUUsage.html#amdgpu-address-spaces) in AMDGPU hardware terminology.
 This type of memory can be accessed via different instructions depending on the specific architecture targeted. However, current CDNA accelerators such as the [MI2XX](2xxnote) use so called `buffer` instructions to access private memory in a simple (and typically) coalesced manner.  See [Sec. 9.1, 'Vector Memory Buffer Instructions' of the CDNA2 ISA guide](https://www.amd.com/system/files/TechDocs/instinct-mi200-cdna2-instruction-set-architecture.pdf) for further reading on this instruction type.

-We develop a [simple kernel](https://github.com/AMDResearch/omniperf/blob/dev/sample/stack.hip) that uses stack memory:
+We develop a [simple kernel](https://github.com/ROCm/omniperf/blob/dev/sample/stack.hip) that uses stack memory:
 ```c++
 #include <hip/hip_runtime.h>
 __global__ void knl(int* out, int filter) {
@@ -3404,7 +3404,7 @@ Here we see a single write to the stack (10.3.6), which corresponds to an L1-L2
 (IPC_example)=
 ## Instructions-per-cycle and Utilizations example

-For this section, we use the instructions-per-cycle (IPC) [example](https://github.com/AMDResearch/omniperf/blob/dev/sample/ipc.hip) included with Omniperf.
+For this section, we use the instructions-per-cycle (IPC) [example](https://github.com/ROCm/omniperf/blob/dev/sample/ipc.hip) included with Omniperf.

 This example is compiled using `c++17` support:

@@ -3824,7 +3824,7 @@ Finally, we note that our branch utilization (11.2.5) has increased slightly fro

 ## LDS Examples

-For this example, we consider the [LDS sample](https://github.com/AMDResearch/omniperf/blob/dev/sample/lds.hip) distributed as a part of Omniperf.
+For this example, we consider the [LDS sample](https://github.com/ROCm/omniperf/blob/dev/sample/lds.hip) distributed as a part of Omniperf.
 This code contains two kernels to explore how both [LDS](lds) bandwidth and bank conflicts are calculated in Omniperf.

 This example was compiled and run on an MI250 accelerator using ROCm v5.6.0, and Omniperf v2.0.0.
@@ -4037,7 +4037,7 @@ The bank conflict rate linearly increases with the number of work-items within a
 ## Occupancy Limiters Example 


-In this [example](https://github.com/AMDResearch/omniperf/blob/dev/sample/occupancy.hip), we will investigate the use of the resource allocation panel in the [Workgroup Manager](SPI)'s metrics section to determine occupancy limiters.
+In this [example](https://github.com/ROCm/omniperf/blob/dev/sample/occupancy.hip), we will investigate the use of the resource allocation panel in the [Workgroup Manager](SPI)'s metrics section to determine occupancy limiters.
 This code contains several kernels to explore how both various kernel resources impact achieved occupancy, and how this is reported in Omniperf.

 This example was compiled and run on a MI250 accelerator using ROCm v5.6.0, and Omniperf v2.0.0:
@@ -6,7 +6,7 @@
   :maxdepth: 5
 ```

-The [Omniperf](https://github.com/AMDResearch/omniperf) repository
+The [Omniperf](https://github.com/ROCm/omniperf) repository
 includes source code for a sample GPU compute workload,
 __vcopy.cpp__. A copy of this file is available in the `share/sample`
 subdirectory after a normal Omniperf installation, or via the