Section: New Results

Automatic Adaptive Approximation for Stencil Computations

Participants : Maxime Schmitt, Cédric Bastoul.

This work has been done in collaboration with Philippe Helluy (TONUS).

Approximate computing is necessary to meet deadlines in some compute-intensive applications like simulation. Building them requires a high level of expertise from the application designers as well as a significant development effort. Some application programming interfaces greatly facilitate their conception but they still heavily rely on the developer's domain-specific knowledge and require many modifications to successfully generate an approximate version of the program. In this work we designed new techniques to semi-automatically discover relevant approximate computing parameters. We believe that superior compiler-user interaction is the key to improved productivity. After pinpointing the region of interest to optimize, the developer is guided by the compiler in making the best implementation choices. Static analysis and runtime monitoring are used to infer approximation parameter values for the application. We evaluated these techniques on multiple application kernels that support approximation and show that with the help of our method, we achieve similar performance as non-assisted, hand-tuned version while requiring minimal intervention from the user.

These techiques and the underlying compiler infrastructure are a significant output of collaboration with the Inria Nancy - Grand Est team TONUS, specialized on applied mathematics (contact: Philippe Helluy), to bring models and techniques from this field to compilers. A paper presenting these extensions has been accepted to the CC international conference [18].

Maxime Schmitt has defended his PhD thesis on Sep. 30th, 2019 [8].