Using machine learning to focus iterative optimization

By Felix V. Agakov, Edwin V. Bonilla, John Cavazos, Björn Franke, G. Fursin, Michael F. P. O'Boyle, John Thomson, Marc Toussaint, Christopher K. I. Williams. IEEE/ACM International Symposium on Code Generation and Optimization, 2006

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End-to-end Deep Learning of Optimization Heuristics (2017, unknown): “Embedding __kernel void A(int a, int b, __global float* c, __global float* d, __global float* e, __global float* f, __global float* g, __global float4* h) { float i[4]; float j[4]; // .”
End-to-End Deep Learning of Optimization Heuristics (2017, unknown): “Embedding __kernel void A(int a, int b, __global float* c, __global float* d, __global float* e, __global float* f, __global float* g, __global float4* h) { float i[4]; float j[4]; // .”
OpenTuner: An extensible framework for program autotuning (2014, methodology): “The area of iterative compilation contains many projects that use different machine learning techniques to optimize lower level compiler optimizations [2, 15, 26, 1].”
Program optimization space pruning for a multithreaded gpu (2008, background): “These range from those based on analytical models [9, 17] to those that use statistical models [13] and those that utilize adaptive learning and intelligent search techniques [3, 4, 26, 27] to find an optimal configuration.”
ParamILS: An Automatic Algorithm Configuration Framework (2014, methodology): “Approaches for setting parameters on a per-instance basis have been, for example, introduced in (Patterson and Kautz, 2001; Cavazos and O’Boyle, 2006; Agakov et al., 2006; Hutter et al., 2006).”