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.

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  1. Gadioli, Davide; Vitali, Emanuele; Palermo, Gianluca; Silvano, Cristina: mARGOt: a dynamic autotuning framework for self-aware approximate computing (2019)
  2. Elafrou, Athena; Karakasis, Vasileios; Gkountouvas, Theodoros; Kourtis, Kornilios; Goumas, Georgios; Koziris, Nectarios: SparseX: a library for high-performance sparse matrix-vector multiplication on multicore platforms (2018)
  3. Tan, Guangming; Liu, Junhong; Li, Jiajia: Design and implementation of adaptive SpMV library for multicore and many-core architecture (2018)
  4. El-Kurdi, Yousef; Dehnavi, Maryam Mehri; Gross, Warren J.; Giannacopoulos, Dennis: Parallel finite element technique using Gaussian belief propagation (2015)
  5. Audet, Charles; Dang, Kien-Cong; Orban, Dominique: Optimization of algorithms with OPAL (2014)
  6. Ballard, G.; Carson, E.; Demmel, J.; Hoemmen, M.; Knight, N.; Schwartz, O.: Communication lower bounds and optimal algorithms for numerical linear algebra (2014)
  7. Akbudak, Kadir; Kayaaslan, Enver; Aykanat, Cevdet: Hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplication (2013)
  8. Lee, Che-Rung: Minimal split checkerboard method for exponentiating sparse matrices and its applications in quantum statistical mechanics (2013)
  9. Vannieuwenhoven, Nick; Meerbergen, Karl: IMF: an incomplete multifrontal (LU)-factorization for element-structured sparse linear systems (2013)
  10. Ghysels, P.; Kłosiewicz, P.; Vanroose, W.: Improving the arithmetic intensity of multigrid with the help of polynomial smoothers. (2012)
  11. Kronbichler, Martin; Kormann, Katharina: A generic interface for parallel cell-based finite element operator application (2012)
  12. Wernsing, John R.; Stitt, Greg: Elastic computing: A portable optimization framework for hybrid computers (2012) ioport
  13. Yzelman, Albert-Jan N.; Bisseling, Rob H.: A cache-oblivious sparse matrix-vector multiplication scheme based on the Hilbert curve (2012)
  14. Ballard, Grey; Demmel, James; Holtz, Olga; Schwartz, Oded: Minimizing communication in numerical linear algebra (2011)
  15. Belgin, Mehmet; Back, Godmar; Ribbens, Calvin J.: A library for pattern-based sparse matrix vector multiply (2011) ioport
  16. Bender, Michael A.; Kuszmaul, Bradley C.; Teng, Shang-Hua; Wang, Kebin: Optimal cache-oblivious mesh layouts (2011)
  17. 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) ioport
  18. Yzelman, A. N.; Bisseling, Rob H.: Two-dimensional cache-oblivious sparse matrix-vector multiplication (2011) ioport
  19. Bender, Michael A.; Brodal, Gerth Stølting; Fagerberg, Rolf; Jacob, Riko; Vicari, Elias: Optimal sparse matrix dense vector multiplication in the I/O-model (2010)
  20. Blatt, Markus: A parallel algebraic multigrid method for elliptic problems with highly discontinuous coefficients (2010)

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