Workshop on MLIR for HPC abstract submission
Migrating compiler and programming languages 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. 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 experience and requirements for MLIR.  In the second part of the workshop, we will outline a path forward.  

PLEASE FILL OUT THE FORM BELOW TO REGISTER FOR THE WORKSHOP OR SUBMIT AN ABSTRACT FOR A TALK.  DEADLINE FOR SUBMISSIONS IS SEPTEMBER 27, 2019.  
Sign in to Google to save your progress. Learn more
Name
Institutional Affiliation
Email address
Submission or Registration Only
Clear selection
Are you a student?
Clear selection
Talk or Poster Title
Abstract
Submit
Clear form
Never submit passwords through Google Forms.
This content is neither created nor endorsed by Google. - Terms of Service - Privacy Policy

Does this form look suspicious? Report