MLAM: A Machine Learning-Aided Architectural Bottleneck Analysis Model for x86 Architectures

The architectural analysis tools that output bottleneck information do not allow knowledge transfer to other applications or architectures. So, we propose a novel tool that can predict an application’s bottlenecks for unavailable architectures. We (i) identify the bottleneck characteristics of 44 applications and use this as the dataset for our ML/DL model; (ii) identify the correlations between metrics and bottlenecks to create our tool’s initial feature list; (iii) propose an architectural bottleneck analysis model – MLAM – that employs random forest regression (RFR) and multi-layer perceptron (MLP) regression; (iv) present results that indicate MLAM tool can achieve 0.70 (RFR) and 0.72 (MLP) R² inference accuracy in predicting bottlenecks; (v) present five versions of MLAM, four of which are trained with single architecture data, and one of which is trained with multiple architecture data, to predict bottlenecks for new architectures.