https://www.osti.gov/biblio/3013883 Skip to main content Sign In Create Account U.S. Department of Energy Office of Scientific and Technical Information U.S. Department of Energy Office of Scientific and Technical Information Search terms: [ ] [?] Submit Research Results Search Tools Public Access Policy PIDs Services & Dev Tools About FAQs News Sign In Create Account OSTI.GOV Technical Report: Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers Technical Report * 01 September 2025 DOI:https://doi.org/10.2172/3013883* OSTI ID:3013883 Rajamanickam, Sivasankaran * Search OSTI.GOV for author "Rajamanickam, Sivasankaran" * Search OSTI.GOV for ORCID "0000-0002-5854-409X" * View ORCID profile ^[1]; Kelley, Brian Michael * Search OSTI.GOV for author "Kelley, Brian Michael" * Search OSTI.GOV for ORCID "0000-0003-3607-360X" * View ORCID profile ^[1]; Sadayappan, Ponnuswamy ^[2]; Rountev, Atanas ^[3]; Roose, Jonathan ^[1]; Eydenberg, Michael Shannon * Search OSTI.GOV for author "Eydenberg, Michael Shannon" * Search OSTI.GOV for ORCID "0000-0002-5400-8089" * View ORCID profile ^[1]; Alvey-Blanco, Addison Jordan * Search OSTI.GOV for author "Alvey-Blanco, Addison Jordan" * Search OSTI.GOV for ORCID "0009-0002-7528-7301" * View ORCID profile ^[1]; Vaidya, Miheer ^[2]; Singh, Shreya ^[2]; Mantri, Devanshu ^[2] --------------------------------------------------------------------- 1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States) 2. Univ. of Utah, Salt Lake City, UT (United States) 3. The Ohio State Univ., Columbus, OH (United States) + Show Author Affiliations This project developed the LAPIS compiler framework, built on the Multilevel Intermediate Representation (MLIR), to optimize sparse linear algebra operations and support performance portability across diverse architectures. The main innovation of LAPIS is the Kokkos dialect, which allows for lowering codes from a high productivity language to different architectures in an elegant way. The dialect also allows the conversion of lower-level MLIR code to C++ Kokkos code, facilitating the integration of scientific machine learning (SciML) models into applications. To extend LAPIS for distributed memory architectures, a new partition dialect was created to manage the distribution of sparse tensors and express communication patterns for sparse linear algebra operations. This dialect also supports the distributed execution of operators and includes algorithmic optimizations to minimize communication to improve performance. The project also demonstrates that MLIR can enable effective linear algebra-level optimizations, improving performance on different GPUs for both sparse and dense linear algebra kernels. Key applications of LAPIS include sparse linear algebra and graph kernels, TenSQL, a relational database management solution built on GraphBLAS, and the development of subgraph isomorphism and monomorphism kernels, showcasing performance portability. In summary, the LAPIS framework supports productivity, performance, portability, and distributed memory execution, while also enabling linear algebra-level optimizations that are challenging in traditional programming languages, with successful applications ranging from simple sparse linear algebra to complex graph kernels. View Technical Report Cite Citation Formats * MLA * APA * Chicago * BibTeX Rajamanickam, Sivasankaran, et al. "Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers." , Sep. 2025. https://doi.org/10.2172/3013883 Copy to clipboard Rajamanickam, Sivasankaran, Kelley, Brian Michael, Sadayappan, Ponnuswamy, Rountev, Atanas, Roose, Jonathan, Eydenberg, Michael Shannon, Alvey-Blanco, Addison Jordan, Vaidya, Miheer, Singh, Shreya, & Mantri, Devanshu (2025). Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers. https://doi.org/10.2172/3013883 Copy to clipboard Rajamanickam, Sivasankaran, Kelley, Brian Michael, Sadayappan, Ponnuswamy, et al., "Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers," (2025), https://doi.org/10.2172/ 3013883 Copy to clipboard @techreport{osti_3013883, author = {Rajamanickam, Sivasankaran and Kelley, Brian Michael and Sadayappan, Ponnuswamy and Rountev, Atanas and Roose, Jonathan and Eydenberg, Michael Shannon and Alvey-Blanco, Addison Jordan and Vaidya, Miheer and Singh, Shreya and Mantri, Devanshu}, title = {Enabling Efficient Sparse Computations using Linear Algebra Aware Compilers}, institution = {Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)}, annote = {This project developed the LAPIS compiler framework, built on the Multilevel Intermediate Representation (MLIR), to optimize sparse linear algebra operations and support performance portability across diverse architectures. The main innovation of LAPIS is the Kokkos dialect, which allows for lowering codes from a high productivity language to different architectures in an elegant way. The dialect also allows the conversion of lower-level MLIR code to C++ Kokkos code, facilitating the integration of scientific machine learning (SciML) models into applications. To extend LAPIS for distributed memory architectures, a new partition dialect was created to manage the distribution of sparse tensors and express communication patterns for sparse linear algebra operations. This dialect also supports the distributed execution of operators and includes algorithmic optimizations to minimize communication to improve performance. The project also demonstrates that MLIR can enable effective linear algebra-level optimizations, improving performance on different GPUs for both sparse and dense linear algebra kernels. Key applications of LAPIS include sparse linear algebra and graph kernels, TenSQL, a relational database management solution built on GraphBLAS, and the development of subgraph isomorphism and monomorphism kernels, showcasing performance portability. In summary, the LAPIS framework supports productivity, performance, portability, and distributed memory execution, while also enabling linear algebra-level optimizations that are challenging in traditional programming languages, with successful applications ranging from simple sparse linear algebra to complex graph kernels.}, doi = {10.2172/3013883}, url = {https://www.osti.gov/biblio/3013883}, place = {United States}, year = {2025}, month = {09}} Copy to clipboard Export * Endnote * RIS * CSV/Excel * XML * JSON Share * Facebook * Twitter / X * LinkedIn * Email Save You must Sign In or Create an Account in order to save documents to your library. Print Details Similar Records / Subjects Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States) Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program DOE Contract Number: NA0003525 OSTI ID: 3013883 Report Number(s): SAND--2025-11870R; 1789932 Country of Publication: United States Language: English --------------------------------------------------------------------- Similar Records A High Performance Sparse Tensor Algebra Compiler in MLIR Conference * 2021 * OSTI ID:1855960 --------------------------------------------------------------------- Automatic Code Generation for High-Performance Graph Algorithms Conference * 2023 * OSTI ID:2376153 --------------------------------------------------------------------- An implementation of SISAL for distributed-memory architectures Thesis/Dissertation * 1995 * OSTI ID:176572 Related Subjects 97 MATHEMATICS AND COMPUTING --------------------------------------------------------------------- Footer logo * Website Policies / Important Links * Contact Us * * Vulnerability Disclosure Program * * Facebook * X / Twitter * YouTube