readme updates

README.md

@@ -3,16 +3,19 @@
 TransferBench is a simple utility capable of benchmarking simultaneous copies between user-specified
 CPU and GPU devices.
 
+Documentation for TransferBench is available at
+[https://rocm.docs.amd.com/projects/TransferBench/en/latest/index.html](https://rocm.docs.amd.com/projects/TransferBench/en/latest/index.html).
+
 ## Requirements
 
-1. ROCm stack installed on the system (HIP runtime)
-2. libnuma installed on system
+* You must have a ROCm stack installed on your system (HIP runtime)
+* You must have `libnuma` installed on your system
 
 ## Documentation
 
-Run the steps below to build documentation locally.
+To build documentation locally, use the following code:
 
-```
+```shell
 cd docs
 
 pip3 install -r .sphinx/requirements.txt
@@ -20,48 +23,54 @@ pip3 install -r .sphinx/requirements.txt
 python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
 ```
 
-## Building
+## Building TransferBench
 
-  To build TransferBench using Makefile:
+You can build TransferBench using Makefile or CMake.
 
-```shell
-make
-```
+* Makefile:
 
-To build TransferBench using CMake:
+  ```shell
+  make
+  ```
 
-```shell
-mkdir build
-cd build
-CXX=/opt/rocm/bin/hipcc cmake ..
-make
-```
+* CMake:
 
-  If ROCm is installed in a folder other than `/opt/rocm/`, set ROCM_PATH appropriately
+  ```shell
+  mkdir build
+  cd build
+  CXX=/opt/rocm/bin/hipcc cmake ..
+  make
+  ```
+
+  If ROCm is not installed in `/opt/rocm/`, you must set `ROCM_PATH` to the correct location.
 
 ## NVIDIA platform support
 
-TransferBench may also be built to run on NVIDIA platforms either via HIP, or native nvcc
+You can build TransferBench to run on NVIDIA platforms via HIP or native NVCC.
 
-To build with HIP for NVIDIA (requires HIP-compatible CUDA version installed e.g. CUDA 11.5):
-```
-   CUDA_PATH=<path_to_CUDA> HIP_PLATFORM=nvidia make`
-```
+Use the following code to build with HIP for NVIDIA (note that you must have a HIP-compatible CUDA
+version installed, e.g., CUDA 11.5):
 
-To build with native nvcc: (Builds TransferBenchCuda)
+```shell
+CUDA_PATH=<path_to_CUDA> HIP_PLATFORM=nvidia make`
 ```
-   make
+
+Use the following code to build with native NVCC (builds `TransferBenchCuda`):
+
+```shell
+make
 ```
 
-## Hints and suggestions
-- Running TransferBench with no arguments will display usage instructions and detected topology information
-- There are several preset configurations that can be used instead of a configuration file
-  including:
-  - p2p    - Peer to peer benchmark test
-  - sweep  - Sweep across possible sets of Transfers
-  - rsweep - Random sweep across possible sets of Transfers
-- When using the same GPU executor in multiple simultaneous Transfers, performance may be
-  serialized due to the maximum number of hardware queues available.
-  - The number of maximum hardware queues can be adjusted via GPU_MAX_HW_QUEUES
-  - Alternatively, running in single stream mode (USE_SINGLE_STREAM=1) may avoid this issue
-    by launching all Transfers on a single stream instead of individual streams
+## Things to note
+
+* Running TransferBench with no arguments displays usage instructions and detected topology
+  information
+* You can use several preset configurations instead of a configuration file:
+  * `p2p`: Peer-to-peer benchmark test
+  * `sweep`: Sweep across possible sets of transfers
+  * `rsweep`: Random sweep across possible sets of transfers
+* When using the same GPU executor in multiple simultaneous transfers, performance may be
+  serialized due to the maximum number of hardware queues available
+  * The number of maximum hardware queues can be adjusted via `GPU_MAX_HW_QUEUES`
+  * Alternatively, running in single-stream mode (`USE_SINGLE_STREAM`=1) may avoid this issue
+    by launching all transfers on a single stream, rather than on individual streams