Hotspot-Detection

Hotspot-Detection is an open-source tool that helps software developers to detect hotspots in their programs. This allows to focus optimization efforts to where it really matters.

Code regions (loops and functions) are categorized into three classes (HOT, WARM, COLD) according to the following criteria:

A) the code region contributes a lot to the total runtime of the program.
B) the runtime of the code region increases a lot when using different program parameters.

• HOT code regions fulfill both (A and B)
• WARM code regions fulfill only one (A xor B)
• COLD code regions fulfill neither (not(A or B))

Installation

Install the requirements:

sudo apt install git build-essential cmake libclang-11-dev clang-11 llvm-11 python3

Install the Hotspot-Detection:

git clone git@github.com:discopop-project/Hotspot-Detection.git
cd Hotspot-Detection
mkdir build
cd build
cmake ..
make

Usage

The Hotspot-Detection is built on the llvm project and has two core components:

• The llvm optimizer pass modifies the program during compilation. With these modifications we automatically monitor how much time your program spends in any code region.
• A python tool analyzes the measured runtimes of the code regions and reports hotspots.

It is possible to manually use these components on (almost) any project. However we also provide a script that wraps the CMake build process to automatically apply the llvm optimizer pass for you. Simply perform the following steps to analyze any project that is built using CMake.

1) Build your project and apply the hotspot-detection instrumentation

cd <your_project_directory>
mkdir build
cd build
<HOTSPOT_DETECTION_BUILD>/scripts/CMAKE_wrapper.sh ..
make

Note that it is possible to add your own custom flags for the cmake build.

2) Run the instrumented program

Run your program multiple times with varying parameters.

./<your_program_name> <your_program arguments_1>
./<your_program_name> <your_program arguments_2>
./<your_program_name> <your_program arguments_3>
# ...

3) Analyze the results

Change your working directory so you are inside the .discopop directory. By default it is located inside the build directory.

hotspot_analyzer

You can now find the analysis results inside .discopop/hotspot_detection.

4) Convenience

For a more convenient management of the process and inspection of the results, please consider using our Visual Studio Code Extension.

Publication

1. Seyed Ali Mohammadi, Lukas Rothenberger, Gustavo de Morais, Bertin Nico Görlich, Erik Lille, Hendrik Rüthers, and Felix Wolf. 2023. Filtering and Ranking of Code Regions for Parallelization via Hotspot Detection and OpenMP Overhead Analysis. In Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC-W '23). Association for Computing Machinery, New York, NY, USA, 1368–1379. https://doi.org/10.1145/3624062.3624206

Citation

Please cite in your publications if it helps your research:

@inproceedings{10.1145/3624062.3624206,
	author = {Mohammadi, Seyed Ali and Rothenberger, Lukas and de Morais, Gustavo and G\"{o}rlich, Bertin Nico and Lille, Erik and R\"{u}thers, Hendrik and Wolf, Felix},
	title = {Filtering and Ranking of Code Regions for Parallelization via Hotspot Detection and OpenMP Overhead Analysis},
	year = {2023},
	isbn = {9798400707858},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3624062.3624206},
	doi = {10.1145/3624062.3624206},
	booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis},
	pages = {1368–1379},
	numpages = {12},
	keywords = {parallelization overhead, expected benefits, OpenMP microbenchmarks, Hotspot detection, ranking, performance analysis},
	location = {Denver, CO, USA},
	series = {SC-W '23}
}