OSKI

OSKI: Optimized Sparse Kernel Interface. What is OSKI? The Optimized Sparse Kernel Interface (OSKI) Library is a collection of low-level C primitives that provide automatically tuned computational kernels on sparse matrices, for use in solver libraries and applications. OSKI has a BLAS-style interface, providing basic kernels like sparse matrix-vector multiply and sparse triangular solve, among others. The current implementation targets cache-based superscalar uniprocessor machines, though we are developing extensions for vector architectures, SMPs, and large-scale distributed memory machines.


References in zbMATH (referenced in 20 articles )

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  1. El-Kurdi, Yousef; Dehnavi, Maryam Mehri; Gross, Warren J.; Giannacopoulos, Dennis: Parallel finite element technique using Gaussian belief propagation (2015)
  2. Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)
  3. Akbudak, Kadir; Kayaaslan, Enver; Aykanat, Cevdet: Hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplication (2013)
  4. Lee, Che-Rung: Minimal split checkerboard method for exponentiating sparse matrices and its applications in quantum statistical mechanics (2013)
  5. Vannieuwenhoven, Nick; Meerbergen, Karl: IMF: an incomplete multifrontal $LU$-factorization for element-structured sparse linear systems (2013)
  6. Ghysels, P.; Kłosiewicz, P.; Vanroose, W.: Improving the arithmetic intensity of multigrid with the help of polynomial smoothers. (2012)
  7. Wernsing, John R.; Stitt, Greg: Elastic computing: A portable optimization framework for hybrid computers (2012)
  8. Yzelman, Albert-Jan N.; Bisseling, Rob H.: A cache-oblivious sparse matrix-vector multiplication scheme based on the Hilbert curve (2012)
  9. Ballard, Grey; Demmel, James; Holtz, Olga; Schwartz, Oded: Minimizing communication in numerical linear algebra (2011)
  10. Belgin, Mehmet; Back, Godmar; Ribbens, Calvin J.: A library for pattern-based sparse matrix vector multiply (2011)
  11. Bender, Michael A.; Kuszmaul, Bradley C.; Teng, Shang-Hua; Wang, Kebin: Optimal cache-oblivious mesh layouts (2011)
  12. Fursin, Grigori; Kashnikov, Yuriy; Memon, Abdul Wahid; Chamski, Zbigniew; Temam, Olivier; Namolaru, Mircea; Yom-Tov, Elad; Mendelson, Bilha; Zaks, Ayal; Courtois, Eric; Bodin, Francois; Barnard, Phil; Ashton, Elton; Bonilla, Edwin; Thomson, John; Williams, Christopher K.I.; O’Boyle, Michael: Milepost GCC: Machine learning enabled self-tuning compiler (2011)
  13. Yzelman, A.N.; Bisseling, Rob H.: Two-dimensional cache-oblivious sparse matrix-vector multiplication (2011)
  14. Bender, Michael A.; Brodal, Gerth Stølting; Fagerberg, Rolf; Jacob, Riko; Vicari, Elias: Optimal sparse matrix dense vector multiplication in the I/O-model (2010)
  15. Blatt, Markus: A parallel algebraic multigrid method for elliptic problems with highly discontinuous coefficients (2010)
  16. Krotkiewski, M.; Dabrowski, M.: Parallel symmetric sparse matrix-vector product on scalar multi-core CPUs (2010)
  17. Wolf, Michael M.; Heath, Michael T.: Combinatorial optimization of matrix-vector multiplication in finite element assembly (2009)
  18. Youseff, Lamia; Seymour, Keith; You, Haihang; Zagorodnov, Dmitrii; Dongarra, Jack; Wolski, Rich: Paravirtualization effect on single- and multi-threaded memory-intensive linear algebra software (2009)
  19. Yzelman, A.N.; Bisseling, Rob H.: Cache-oblivious sparse matrix-vector multiplication by using sparse matrix partitioning methods (2009)
  20. Nishtala, Rajesh; Vuduc, Richard W.; Demmel, James W.; Yelick, Katherine A.: When cache blocking of sparse matrix vector multiply works and why (2007)