ÆminiumGPU: An Intelligent Framework for GPU Programming

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

“…-machine learning -decision trees; near neighbors; linear regression; Decision Trees (DT) [7,31], k-Nearest Neighbor (kNN) [19], Cost Sensitive Decision Table (CSDT), Naive Bayes (NB), Support Vector Machine (SVM), Multi-layer Perceptron (MPL) [31], Linear Regression (LR) [31,58], and Logistic Regression (LRPR) [77] machine learning algorithms are used during the code-generation.…”

“…Combination of static code features (extracted at compile time), and dynamic features (extracted at run-time) are also used to determine the most suitable processing device for a specific application [31]. To determine the best workload distribution of a parallel application, Luk et al [58] consider algorithm parameters and hardware configuration parameters.…”

“…Tournavitis et al [95] uses SVM to decide whether to parallelize loop candidates and achieves 96% of the performance of hand-tuned code. Fonseca and Cabral [31] report 92% of prediction accuracy when using SVM to decide whether kernels should be executed on GPU or CPU (k) Nearest Neighbor Liu et al [57] report 5.41% performance improvement compared to hard-coded compiler optimizations. Wang and O'boyle [99] use NN to predict the partitioning structure of applications.…”

“…The authors achieve up to 1.9× speedup compared to the default partitioning strategy, which is 60% of the ideal one. Chen and Long [19] report that 87% of the highest performance improvement can be achieved using NN Ruled Set Induction Cavazos and Moss [17] use RSI to determine whether or not to apply instruction scheduling on a code block and reported achievement of 90% performance improvement compared to schedule always method Regression Based Algorithms Pekhimenko and Brown [77] use regression techniques to determine the optimal heuristic parameters and report two fold speedup of the compilation process while maintaining the same code quality Decision Tables In Fonseca and Cabral [31], the up to 92 % prediction accuracy of DT, NB, and MLP to decide whether kernels should be executed on the GPU or CPU is significant to achieve 65x speedup over sequential Java programs…”

“…Efficient for simple models, but results in lower prediction accuracies for more complex problems [7,19,31] Device-specific code generation; mapping applications to accelerators The prediction model requires significant training data for accurate mapping decisions. Lack of training data may result in performance degradation [19] Generating multi-threaded versions of the loop The size of the executable file may dramatically increase for applications with large parallel code, and hardware architectures that consist of multiple multi-core processing units [5,31,81] Source-to-source transformation Limited to map-reduce operations. The automatic code generation is limited to specific features of Java code.…”