Abstract-Autotuning systems intelligently navigate a search space of possible implementations of a computation to find the implementation(s) that best meets a specific optimization criteria, usually performance. This paper describes Nitro, a programmer-directed autotuning framework that facilitates tuning of code variants, or alternative implementations of the same computation. Nitro provides a library interface that permits programmers to express code variants along with metainformation that aids the system in selecting among the set of variants at run time. Machine learning is employed to build a model through training on this meta-information, so that when a new input is presented, Nitro can consult the model to select the appropriate variant. In experiments with five real-world irregular GPU benchmarks from sparse numerical methods, graph computations and sorting, Nitro-tuned variants achieve over 93% of the performance of variants selected through exhaustive search. Further, we describe optimizations and heuristics in Nitro that substantially reduce training time and other overheads.
Muralidharan and others have recently built a programmer-directed autotuning framework named Nitro for selecting the appropriate code variants at runtime REF .
Nitro: A Framework for Adaptive Code Variant Tuning
2014 IEEE 28th International Parallel and Distributed Processing Symposium
2014 IEEE 28th International Parallel and Distributed Processing Symposium
Computer Science