BLIS: A Framework for Rapidly Instantiating BLAS Functionality. The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in ISO C99 so that it can be reused and/or reparameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL)

References in zbMATH (referenced in 17 articles , 1 standard article )

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  1. Rizzi, Francesco; Parish, Eric J.; Blonigan, Patrick J.; Tencer, John: A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems (2021)
  2. Van Zee, Field G.; Parikh, Devangi N.; Geijn, Robert A. Van De: Supporting mixed-domain mixed-precision matrix multiplication within the BLIS framework (2021)
  3. Frison, Gianluca; Sartor, Tommaso; Zanelli, Andrea; Diehl, Moritz: The BLAS API of BLASFEO: optimizing performance for small matrices (2020)
  4. Van Zee, Field G.: Implementing high-performance complex matrix multiplication via the 1M method (2020)
  5. Rodríguez-Sánchez, Rafael; Catalán, Sandra; Herrero, José R.; Quintana-Ortí, Enrique S.; Tomás, Andrés E.: Look-ahead in the two-sided reduction to compact band forms for symmetric eigenvalue problems and the SVD (2019)
  6. Huang, Jianyu; Matthews, Devin A.; van de Geijn, Robert A.: Strassen’s algorithm for tensor contraction (2018)
  7. Matthews, Devin A.: High-performance tensor contraction without transposition (2018)
  8. Springer, Paul; Bientinesi, Paolo: Design of a high-performance GEMM-like tensor-tensor multiplication (2018)
  9. Gianluca Frison, Dimitris Kouzoupis, Andrea Zanelli, Moritz Diehl: BLASFEO: Basic linear algebra subroutines for embedded optimization (2017) arXiv
  10. Martinsson, Per-Gunnar; Quintana Ortí, Gregorio; Heavner, Nathan; van de Geijn, Robert: Householder QR factorization with randomization for column pivoting (HQRRP) (2017)
  11. Paul Springer, Tong Su, Paolo Bientinesi: HPTT: A High-Performance Tensor Transposition C++ Library (2017) arXiv
  12. Jianyu Huang, Robert A. van de Geijn: BLISlab: A Sandbox for Optimizing GEMM (2016) arXiv
  13. Low, Tze Meng; Igual, Francisco D.; Smith, Tyler M.; Quintana-Orti, Enrique S.: Analytical modeling is enough for high-performance BLIS (2016)
  14. Paszyński, Maciej: Fast solvers for mesh-based computations (2016)
  15. Van Zee, Field G.; van de Geijn, Robert A.: BLIS: a framework for rapidly instantiating BLAS functionality (2015)
  16. Willenbring, James M.: Replicated computational results (RCR) report for “BLIS: a framework for rapidly instantiating BLAS functionality” (2015)
  17. Di Napoli, Edoardo; Fabregat-Traver, Diego; Quintana-Ortí, Gregorio; Bientinesi, Paolo: Towards an efficient use of the BLAS library for multilinear tensor contractions (2014)