Data locality is of extreme importance in programming distributed-memory architectures due to its implications on latency and energy consumption. Automated compiler and runtime system optimization studies have attempted to improve data locality exploitation without burdening the programmer. However, due to the difficulty of static code analysis, conservatism in compiler optimizations to avoid errors, and cost of dynamic analysis, the efficacy of automated optimizations is limited. Therefore, programmers need to spend significant effort in optimizing locality while creating applications for distributed memory parallel systems. We present a machine-learning based framework to automatically exploit locality in distributed memory applications. This framework takes application source whose time-critical blocks are marked by pragmas, and produces optimized source code that uses a regressor for efficient data movement. The regressor is trained with automatically-collected application profiles with very small input data sizes. We integrate our prototype in the Chapel language stack. In our experiments, we show that the Elastic Net model is the ideal regressor for our case and applications that utilize Elastic Net can perform very similarly to programmer-optimized versions. We also show that such regressors can be trained within few minutes on a cluster or within 30 minutes on a workstation, including data collection.

Data locality is of extreme importance in programming distributed-memory architectures due to its implications on latency and energy consumption. Automated compiler and runtime system optimization studies have attempted to improve data locality exploitation without burdening the programmer. However, due to the difficulty of static code analysis, conservatism in compiler optimizations to avoid errors, and cost of dynamic analysis, the efficacy of automated optimizations is limited. Therefore, programmers need to spend significant effort in optimizing locality. In this work, we present an automated code optimization framework that trains neural networks using application profiles for small data sizes that exhibit similar patterns to larger cases. The application is then modified to use the neural network to improve data locality exploitation. We prototype our framework for the Chapel language and integrate with the language stack. We experimentally demonstrate that our framework can learn access patterns and create optimized executables in minutes. The resulting executables perform more than one order of magnitude faster than unoptimized code, and are comparable to manual locality optimization without burdening the programmer and hindering productivity.