The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: LLVM-HPC2025: The 11th Workshop on the LLVM Compiler Infrastructure in HPC
Authors: Miheer Vaidya, Shreya Singh, and Devanshu Mantri (University of Utah); Michael Shannon Eydenberg, Brian Michael Kelley, and Sivasankaran Rajamanickam (Sandia National Laboratories); Atanas Rountev (Ohio State University); and P. Sadayappan (University of Utah)
Abstract: MLIR (Multi-Level Intermediate Representation) is a popular framework for implementing domain-specific compilers for optimizing matrix/tensor computations. However, no support currently exists in MLIR for distributed sparse tensor computations. In this paper, we describe the design and implementation of a new MLIR dialect for high-level specification of distributed sparse tensor computations. This specification is then lowered to MPI-based code for distributed execution. We illustrate the expressiveness of the new dialect by implementing a Graph Attention Network computation with multiple sparse tensor operators.
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