Tuning of loop orders in blocked dense basic linear algebra subroutines

A non-transitory computer readable storage medium comprising instructions that, when executed by a processor, cause the processor to at least:

• generate a plurality of sequence pairs, a first one of the sequence pairs including:
• (i) a first input sequence representing first accesses to first tensors in a first loop nest of a first computer program, and (ii) a first output sequence representing a first tuned loop nest corresponding to the first accesses to the first tensors in the first loop nest;
• train a recurrent neural network using the sequence pairs as training data, the recurrent neural network to be trained to tune loop ordering of a second computer program based on a second input sequence representing second accesses to second tensors in a second loop nest of the second computer program;
• and
• store, in memory, a trained model corresponding to the recurrent neural network.

A method to tune loop ordering in computer programs, the method comprising:

• generating a plurality of sequence pairs, a first one of the sequence pairs including:
• (i) a first input sequence representing first accesses to first tensors in a first loop nest of a first computer program, and (ii) a first output sequence representing a first tuned loop nest corresponding to the first accesses to the first tensors in the first loop nest;
• training a recurrent neural network using the sequence pairs as training data, the recurrent neural network to be trained to tune loop ordering of a second computer program based on a second input sequence representing second accesses to second tensors in a second loop nest of the second computer program;
• and
• storing, in memory, a trained model corresponding to the recurrent neural network.

An apparatus to tune loop ordering in computer programs, the apparatus comprising:

• means for generating a plurality of sequence pairs, a first one of the sequence pairs including:
• (i) a first input sequence representing first accesses to first tensors in a first loop nest of a first computer program, and (ii) a first output sequence representing a first tuned loop nest corresponding to the first accesses to the first tensors in the first loop nest;
• means for training a recurrent neural network using the sequence pairs as training data, the recurrent neural network to be trained to tune loop ordering of a second computer program based on a second input sequence representing second accesses to second tensors in a second loop nest of the second computer program;
• and
• means for storing, in memory, a trained model corresponding to the recurrent neural network.