Call for Participation

Migrating compiler and programming systems research for HPC into practice has always been difficult, due to the complexity of this technology, the continuous evolution of programming languages for HPC such as C++ and Fortran, the relatively small HPC market, and capability gaps between open source compilers such as Clang/LLVM and hardware vendor compilers. If we look at where industry is making extensive investments in compiler technology, it is for deep learning applications. There is significant overlap between requirements for deep learning and HPC applications: (1) abundant parallelism; (2) large data sets demanding optimizations to manage data movement; (3) a diversity of target architectures; and, (4) need for scalability. Among the efforts focused on deep learning compilers, of particular interest is Google's recent introduction of the MLIR intermediate representation. MLIR, part of the Google Tensor Flow framework, has the capability to lower MLIR to LLVM, thus making it compatible with a widely-used open source compiler ecosystem. A key idea in MLIR is a set of higher-level abstractions (e.g., tensors) that permit MLIR to perform higher-level array and loop optimizations common to parallelizing compilers more naturally than at the C-like IR abstractions offered in LLVM. MLIR has recently committed to contributing to the LLVM Foundation (see link) . At present, there are significant gaps in MLIR capability, but as it is new, this is an ideal time to envision how it might support HPC applications in the future.

This workshop will gather researchers from the LCPC community interested in advancing the availlability of state-of-the-art compiler technology for parallel computing in open source compiler technology. The format of the workshop will be a series of brief presentations highlighting their experiences and identifying requirements for MLIR to support HPC applications. In the second part of the workshop, the participants will outline a path forward.