Qilin

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

“…The runtime can switch between the available algorithms during program execution. Luk et al [58] introduce Qilin that enables source-to-source transformation from C++ to TBB and CUDA. It uses machine learning to find the optimal work distribution between the CPU and GPU on a heterogeneous system.…”

“…-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. Pekhimenko and Brown [77] consider general and loop-based features to determine the list of program method transformation during code generation that would reduce the compilation time.…”

“…Limitations of the compile-time software optimization approaches that use machine learning or meta-heuristics are listed in Table 7, which include: (1) limitation to a specific programming language or model [95], (2) forcing developers to use extra annotations on their code [58], or use not widely known parallel programming languages [5], (3) focusing on single or simpler aspects of optimizations techniques (ex: loop unrolling, unrolling factor, instruction scheduling) [17,69,87], whereas more complex compiler optimizations (that are compute-intensive) are not addressed sufficiently.…”

“…Greedy Algorithm and Hill Climbing Cooper et al [21] use meta-heuristics to find the optimal compilation parameters while reducing the number of evaluations during search space exploration from 10000 to a single one using profiling data and estimated virtual execution Genetic Algorithm Sandrieser et al [88] obtain speedup of 23% for hyper-block formation Parallel Rank Ordering Tiwari and Hollingsworth [93] and Tiwari et al [92] use PRO for automatic tuning of compilation process and report 46% performance improvement compared to the original code [57,58,99] Determining the best partitioning strategy Assumes that for any two functions with similar features, the same partitioning strategy can be used [95] Determining loops that benefit from parallelization and their best scheduling policy Targets OpenMP loop constructs only. Uses profiling to detect loop candidates, which may significantly increase the compilation time [1,21] Adaptive tuning of the compilation process Profiling data needs to be collected to perform the virtual executions.…”