Add profiling guide (rapidsai#20292)

Adds a profiling guide to the C++ developer documentation, with the most commonly desired flags for `nsys profile` commands.

Authors:
  - Bradley Dice (https://github.com/bdice)

Approvers:
  - David Wendt (https://github.com/davidwendt)
  - Paul Mattione (https://github.com/pmattione-nvidia)

URL: rapidsai#20292

Author: bdice

cpp/doxygen/Doxyfile

@@ -861,6 +861,7 @@ INPUT                  = main_page.md \
                          developer_guide/BENCHMARKING.md \
                          developer_guide/DOCUMENTATION.md \
                          developer_guide/DEVELOPER_GUIDE.md \
+                         developer_guide/PROFILING.md \
                          developer_guide/TESTING.md \
                          ../include \
                          ../include/cudf_test/column_wrapper.hpp \

cpp/doxygen/developer_guide/DEVELOPER_GUIDE.md

@@ -6,6 +6,7 @@ to these additional files for further documentation of libcudf best practices.
 * [Documentation Guide](DOCUMENTATION.md) for guidelines on documenting libcudf code.
 * [Testing Guide](TESTING.md) for guidelines on writing unit tests.
 * [Benchmarking Guide](BENCHMARKING.md) for guidelines on writing unit benchmarks.
+* [Profiling Guide](PROFILING.md) for guidelines on profiling libcudf code.
 
 # Overview
 

cpp/doxygen/developer_guide/PROFILING.md

@@ -0,0 +1,46 @@
+# Profiling libcudf
+
+Profiling is essential for understanding performance characteristics and identifying bottlenecks in libcudf. This guide covers GPU profiling using NVIDIA Nsight Systems.
+
+## NVIDIA Nsight Systems
+
+[NVIDIA Nsight Systems](https://developer.nvidia.com/nsight-systems) is a system-wide performance analysis tool that provides detailed timeline views of CPU and GPU activity.
+It's the recommended tool for profiling CUDA applications and understanding kernel execution, memory transfers, and API calls.
+
+### Installation
+
+Nsight Systems is included with the CUDA Toolkit, or can be downloaded from https://developer.nvidia.com/nsight-systems. The command-line tool is `nsys`. Verify installation:
+
+```bash
+nsys --version
+```
+
+### Recommended Profile Command
+
+When profiling cuDF workloads, use the following flags:
+
+```bash
+nsys profile --trace=nvtx,cuda,osrt --cuda-memory-usage=true --gpu-metrics-devices=0 --nvtx-domain-exclude=CCCL python script.py
+```
+
+**Options explained:**
+- `--trace=nvtx,cuda,osrt`: Trace NVTX ranges, CUDA API calls, and OS runtime libraries
+- `--cuda-memory-usage=true`: Track CUDA memory allocation and usage
+- `--gpu-metrics-devices=0`: Collect GPU metrics from device 0
+- `--nvtx-domain-exclude=CCCL`: Exclude verbose CCCL (CUDA C++ Core Libraries) NVTX ranges
+
+### Profiling Specific GPUs
+
+When working with multi-GPU systems, you may want to profile a specific GPU.
+To profile GPUs other than device 0, use both `--gpu-metrics-devices=N` and `--env-var CUDA_VISIBLE_DEVICES=N` to ensure the application and profiler target the same device.
+
+For example, modify the flags like this for profiling GPU 4:
+
+```bash
+nsys profile --trace=nvtx,cuda,osrt --cuda-memory-usage=true --gpu-metrics-devices=4 --env-var CUDA_VISIBLE_DEVICES=4 python script.py
+```
+
+### Analyzing Results
+
+After profiling, open the `.nsys-rep` file in the Nsight Systems GUI to analyze CPU and GPU activity over time.
+The interface shows individual kernel launches and durations, memory allocations and transfers, and metrics like memory bandwidth utilization.