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Selected Regression Algorithms Applied to Spatial Computation

For this project, we have formulated an architecture problem as a machine learning problem. The problem, stemming from the WaveScalar project, is to map operations in a dataflow graph onto a grid of processing elements (where they are executed). An unsolved and widely applicable problem in computer architecture involves finding a mapping that performs best when executed. A function that evaluates a mapping could be used as a heuristic to optimize application layout. This is an apt problem for machine learning as analytical models previously developed to predict performance leave room for improvement (because the exact performance characteristics of this new processor design are unknown). We study five separate machine learning algorithms to model our simulation data. We observed four features of each mapping: operand latency, operand bandwidth, distributed data cache miss rate, and contention of processing elements. These four properties became our independent variables used as bases for the learning algorithms that attempted to predict our dependent variable: IPC (instructions per cycle). We found that all five regression learning algorithms: Regression Trees, Cascade Correlation, Multivariate Adaptive Regression Splines (MARS), Multiple Regression, and Weighted Contribution, gave very similar correlations of predicted to actual IPC. Since a broad range of regression algorithms were used, this similarity indicates a limiting factor that is not part of any regression algorithm, but rather stems from the application.