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Found 5 papers, 1 papers with code
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives
However, given the constant emergence of new DNN architectures, creating hand optimized code is expensive, slow and is not scalable.
PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives
In this paper, we develop a hybrid solution to the development of deep learning kernels that achieves the best of both worlds: the expert coded microkernels are utilized for the innermost loops of kernels and we use the advanced polyhedral technology to automatically tune the outer loops for performance.
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support
We model the computation and communication costs of a dataflow graph that embodies the neural network training process and then, partition the graph using heuristics in such a manner that the communication between compute devices is minimal and we have a good load balance.
Categorization of Program Regions for Agile Compilation using Machine Learning and Hardware Support
Consequently, the performance of the produced code will match the performance when all the aggressive optimization passes are applied over the entire input program.
A Data Analytics Framework for Aggregate Data Analysis
In this paper, we present an end-to-end pipeline for processing of aggregate data to derive individual level statistics, and then using the inferred data to train machine learning models to answer questions of interest.
