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Modeling Performance of Tensor Transpose using Regression Techniques

Srivastava, Rohit Kumar

Abstract Details

2018, Master of Science, Ohio State University, Computer Science and Engineering.
Tensor transposition is an important primitive in many tensor algebra libraries. For example, tensor contractions are implemented using TTGT(Transpose-Transpose-GEMM-Transpose) approach. Performing efficient transpose of an arbitrary tensor requires different optimization techniques depending on the required permutation. Exhaustive evaluation of all parameter choices like slice size and blocking is prohibitively expensive. We present an approach to model the performance of different kernels inside TTLG, a Tensor Transpose Library for GPUs, for different parameters like slice size, blocking, and resultant warp efficiency etc. Predictions made by this model are then used to guide in kernel and its parameter selection.
Ponnuswamy Sadayappan (Advisor)
Radu Teodorescu (Committee Member)
55 p.

Recommended Citations

Citations

  • Srivastava, R. K. (2018). Modeling Performance of Tensor Transpose using Regression Techniques [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524080824154753

    APA Style (7th edition)

  • Srivastava, Rohit Kumar. Modeling Performance of Tensor Transpose using Regression Techniques. 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1524080824154753.

    MLA Style (8th edition)

  • Srivastava, Rohit Kumar. "Modeling Performance of Tensor Transpose using Regression Techniques." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524080824154753

    Chicago Manual of Style (17th edition)