Predicting workload behavior during workload execution is essential for dynamic resource optimization in multi-processor systems. Recent studies have proposed advanced machine learning techniques for dynamic workload prediction. Workload prediction can be cast as a time series forecasting problem. However, traditional forecasting models struggle to predict abrupt workload changes. These changes occur because workloads are known to go through phases. Prior work has investigated machine learning-based approaches for phase detection and prediction, but such approaches have not been studied in the context of dynamic workload forecasting. In this paper, we propose phase-aware CPU workload forecasting as a novel approach that applies long-term phase prediction to improve the accuracy of short-term workload forecasting. Phase-aware forecasting requires machine learning models for phase classification, phase prediction, and phase-based forecasting that have not been explored in this combination before. Furthermore, existing prediction approaches have only been studied in single-core settings. This work explores phase-aware workload forecasting with multi-threaded workloads running on multi-core systems. We propose different multi-core settings differentiated by the number of cores they access and whether they produce specialized or global outputs per core. We study various advanced machine learning models for phase classification, phase prediction, and phase-based forecasting in isolation and different combinations for each setting. We apply our approach to forecasting of multi-threaded Parsec and SPEC workloads running on an 8-core Intel Core-i9 platform. Our results show that combining GMM clustering with LSTMs for phase prediction and phase-based forecasting yields the best phase-aware forecasting results. An approach that uses specialized models per core achieves an average error of 23% with up to 22% improvement in prediction accuracy compared to a phase-unaware setup.
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Learning-Based Workload Phase Classification and Prediction Using Performance Monitoring Counters
Predicting coarse-grain variations in workload behavior during execution is essential for dynamic resource optimization of processor systems. Researchers have proposed various methods to first classify workloads into phases and then
learn their long-term phase behavior to predict and anticipate phase changes. Early studies on phase prediction proposed table-based phase predictors. More recently, simple learning-based techniques such as decision trees have been explored. However, more recent advances in machine learning have not been applied to phase prediction so far. Furthermore, existing phase predictors have been studied only in connection with specific phase classifiers even though there is a wide range of classification methods. Early work in phase classification proposed various clustering methods that required access to source code. Some later studies used performance monitoring counters, but they only evaluated classifiers for specific contexts such as thermal modeling.
In this work, we perform a comprehensive study of source-oblivious phase classification and prediction methods using hardware counters. We adapt classification techniques that were used with different inputs in the past and compare them to state-of-the-art hardware counter based classifiers. We further evaluate the accuracy of various phase predictors when coupled with different
phase classifiers and evaluate a range of advanced machine learning techniques, including SVMs and LSTMs for workload phase prediction. We apply classification and prediction approaches to SPEC workloads running on an Intel Core-i9 platform. Results show that a two-level kmeans clustering combined with SVM-based phase change prediction provides the best tradeoff between accuracy and long-term stability. Additionally, the SVM predictor
reduces the average prediction error by 80% when compared to a table-based predictor.
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- Award ID(s):
- 1763848
- NSF-PAR ID:
- 10299961
- Date Published:
- Journal Name:
- ACM/IEEE Workshop on Machine Learning for CAD (MLCAD)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Predicting workload behavior during execution is essential for dynamic resource optimization of processor systems. Early studies used simple prediction algorithms such as a history tables. More recently, researchers have applied advanced machine learning regression techniques. Workload prediction can be cast as a time series forecasting problem. Time series forecasting is an active research area with recent advances that have not been studied in the context of workload prediction. In this paper, we first perform a comparative study of representative time series forecasting techniques to predict the dynamic workload of applications running on a CPU. We adapt state-of-the-art matrix profile and dynamic linear models (DLMs) not previously applied to workload prediction and compare them against traditional SVM and LSTM models that have been popular for handling non-stationary data. We find that all time series forecasting models struggle to predict abrupt workload changes. These changes occur because workloads go through phases, where prior work has studied workload phase detection, classification and prediction. We propose a novel approach that combines time series forecasting with phase prediction. We process each phase as a separate time series and train one forecasting model per phase. At runtime, forecasts from phase-specific models are selected and combined based on the predicted phase behavior. We apply our approach to forecasting of SPEC workloads running on a state-of-the-art Intel machine. Our results show that an LSTM-based phase-aware predictor can forecast workload CPI with less than 8% mean absolute error while reducing CPI error by more than 12% on average compared to a non-phase-aware approach.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MLCAD52597.2021.9531161
