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Prediction of GPU Kernel Performance for Matrix Multiplication

This repository contains the implementation of machine learning models used to predict the computational running time for multiplying two 2048x2048 matrices using a GPU OpenCL SGEMM kernel. The code is based on the research presented in the paper:

Prediction of the Running Time for Multiplying Two 2048x2048 Matrices using a GPU OpenCL SGEMM Kernel

Overview

Matrix multiplication is a fundamental operation in linear algebra, with applications in fields like machine learning, scientific computing, and engineering. This project leverages machine learning to predict the computational performance of a GPU-based SGEMM kernel for matrix multiplication, enabling engineers to evaluate GPU performance efficiently.

Key Features

  • Dataset: 241,600 data points with 14 GPU configuration parameters.
  • Machine Learning Models:
    • Linear Regression
    • Polynomial Regression
    • Decision Trees
    • Random Forests
    • K-Nearest Neighbors (KNN)
  • Ensemble Learning: Combining Random Forest and KNN models.
  • Performance Metrics: R², Mean Absolute Error (MAE), and Mean Squared Error (MSE).

Dataset

The dataset includes the following key parameters:

  • Workgroup Sizes: MWG, NWG
  • Tiling Sizes: KWG
  • Local Memory Allocations: MDIMC, NDIMC, MDIMA, NDIMB
  • Execution Patterns: STRM, STRN, SA, SB
  • Vector Widths: VWM, VWN
  • Kernel Shaping: KWI

The target variable is the logarithmic transformation of the average running time for matrix multiplication tasks.

Getting Started

Prerequisites

To run the code, you need:

  • Python 3.8 or later
  • Libraries:
    • NumPy
    • Pandas
    • Scikit-learn
    • Matplotlib
    • Seaborn

Install the required dependencies using:

pip install -r requirements.txt