GPU Power Prediction

This repository provides the implementation and resources used in the research paper:
"Estimating Power Consumption of GPU Application Using Machine Learning Tool"
by Gargi Alavani Prabhu, Tanish Desai, Sharvil Potdar, Nayan Gogari, Snehanshu Saha and Santonu Sarkar Cite as:
Gargi Alavani Prabhu, Tanish Desai, Sharvil Potdar, Nayan Gogari, Snehanshu Saha and Santonu Sarkar, "Estimating Power Consumption of GPU Application Using Machine Learning Tool", 2024. [Google Scholar]

Abstract

Accurately predicting power consumption of GPU kernels is crucial for optimizing performance and energy efficiency in high-performance computing. This project presents a benchmark suite and a PowerAPI tool for profiling and predicting the power usage of GPU kernels based on machine learning techniques. The suite includes a diverse set of CUDA kernels and datasets, enabling robust training and evaluation of power models. PowerAPI provides an easy-to-use interface for instrumenting CUDA applications and collecting power/energy data, facilitating automated and reproducible measurements for research and development.

Repository Structure

PowerAPI/: Source code and interface for GPU power measurement.
Benchmark Suite/: Collection of sample CUDA kernels and benchmarks.
- Each sample folder contains a README with build/run instructions and key concepts.
Datasets/: Example datasets for training and evaluating power prediction models.
Supplementary File.pdf: Additional details and supporting material.

Tools Used

CUDA Toolkit 11.0+: For compiling and running GPU benchmarks.
PowerAPI: Custom API for measuring GPU power and energy.
C++/C/CUDA/SWIG/Makefile/Python/Shell: Multi-language implementation for flexibility and extensibility.

How to Use PowerAPI

Add PowerAPI to the library path

LD_LIBRARY_PATH="/path/of/Folder/PowerAPI"

Import PowerAPI in your CUDA file
At the start of your .cu file, add:
```
#include "GPUDevice.h"
```

Instrument your kernel code
Create a GPUDevice object and wrap your kernel calls:

GPUDevice g1 = GPUDevice(<GPU Device ID>,<Kernel Name>,<Grid Size>,<Block Size>);
g1.startReading();
// <<Cuda Kernel Calls>>
g1.stopReading();

Run your code
Output is saved in a text file named after the kernel.

Power in Watts
Energy in milliJoules
Sample output:

KernelName,GridSize,BlockSize,MaxPower,MinPower,AvgPower,Time,Energy,
reluKernel, 86436, 256, 84, 64, 76 , 67603944.000000, 5154,

Benchmark Suite Usage

Windows: Use provided Visual Studio solution files (*_vs<version>.sln).
Linux: Use makefiles in each sample directory:
```
cd <sample_dir>
make
```
Options:
- TARGET_ARCH=<arch>: Target specific CPU architecture
- dbg=1: Build with debug symbols
- SMS="A B ...": Build for specific SM architectures
- HOST_COMPILER=<host_compiler>: Use a custom host compiler

See individual sample READMEs in Benchmark Suite/ for details and supported architectures.

References

For questions or contributions, please refer to the repository issues or contact the authors via their GitHub profiles.

tanish111/Power-Extraction

README