Keyword Analysis & Research: sgemm


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sgemm0.180.57004155

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  • SGEMM, DGEMM, CGEMM, and ZGEMM - IBM

    ibm.com

    https://www.ibm.com/docs/en/essl/6.3?topic=mos-sgemm-dgemm-cgemm-zgemm-combined-matrix-multiplication-addition-general-matrices-their-transposes-conjugate-transposes

    WEBSGEMM, DGEMM, CGEMM, and ZGEMM (Combined Matrix Multiplication and Addition for General Matrices, Their Transposes, or Conjugate Transposes) Purpose. SGEMM and DGEMM can perform any one of the following combined matrix computations, using scalars α and β, matrices A and B or their transposes, and matrix C:

    DA: 27 PA: 51 MOZ Rank: 17

  • OpenCL matrix-multiplication SGEMM tutorial - GitHub Pages

    github.io

    https://cnugteren.github.io/tutorial/pages/page1.html

    WEBThis article describes a GPU OpenCL implementation of single-precision matrix-multiplication (SGEMM) in a step-by-step approach. We'll start with the most basic version, but we'll quickly move on towards more advanced code. Each step introduces a new optimisation - and best of all - working OpenCL code.

    DA: 67 PA: 15 MOZ Rank: 25

  • SGEMM - OpenGenus IQ

    opengenus.org

    https://iq.opengenus.org/sgemm/

    WEBSGEMM stands for "Single Precision General Matrix Multiplication". It is a standard gemm routine in BLAS and BLIS libraries like OpenBLAS and is used to do Matrix Multiplication. It performs the standard GEMM operation that is Matrix Matrix multiplication with the matrices being of datatype FLOAT 32 bits.

    DA: 12 PA: 68 MOZ Rank: 68

  • siboehm/SGEMM_CUDA: Fast CUDA matrix multiplication from scratch - GitHub

    github.com

    https://github.com/siboehm/SGEMM_CUDA

    WEBFast CUDA SGEMM from Scratch Step-by-step optimization of matrix multiplication, implemented in CUDA. For an explanation of each kernel, see siboehm.com/CUDA-MMM .

    DA: 60 PA: 29 MOZ Rank: 53

  • LAPACK: sgemm

    netlib.org

    https://netlib.org/lapack/explore-html/dd/d09/group__gemm_ga8cad871c590600454d22564eff4fed6b.html

    WEBSGEMM. Purpose: SGEMM performs one of the matrix-matrix operations. C := alpha*op( A )*op( B ) + beta*C, where op( X ) is one of. op( X ) = X or op( X ) = X**T, alpha and beta are scalars, and A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix and C an m by n matrix. Parameters.

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  • OpenCL matrix-multiplication SGEMM tutorial - GitHub Pages

    github.io

    https://cnugteren.github.io/tutorial/pages/page2.html

    WEBOpenCL matrix-multiplication SGEMM tutorial. Tutorial: OpenCL SGEMM tuning for Kepler. Note: the complete source-code is available at GitHub. Note2: a tuned OpenCL BLAS library based on this tutorial is now available at GitHub. Note3: a WebGL2 demo of this tutorial is available at: https://www.ibiblio.org/e-notes/webgl/gpu/mul/sgemm.htm.

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  • Tips to Measure the Performance of Matrix Multiplication Using ... - Intel

    intel.com

    https://www.intel.com/content/www/us/en/developer/articles/technical/a-simple-example-to-measure-the-performance-of-an-intel-mkl-function.html

    WEBDec 24, 2017 · In this article, we explain how to design and measure of the performance using Intel MKL SGEMM, and outline about 7 tips to help developers to perform performance tests and quickly evaluate the floating pointing computing capability (FLOPS) on a specified processor.

    DA: 51 PA: 86 MOZ Rank: 45

  • scipy.linalg.blas.sgemm — SciPy v1.13.0 Manual

    scipy.org

    https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.blas.sgemm.html

    WEBscipy.linalg.blas.sgemm(alpha, a, b[, beta, c, trans_a, trans_b, overwrite_c]) = <fortran function sgemm> #. Wrapper for sgemm. Parameters: alphainput float. ainput rank-2 array (‘f’) with bounds (lda,ka) binput rank-2 array (‘f’) with bounds (ldb,kb) Returns: crank-2 array (‘f’) with bounds (m,n) Other Parameters:

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  • lapack-blas/sgemm.html - University of Utah

    utah.edu

    https://www.math.utah.edu/software/lapack/lapack-blas/sgemm.html

    WEBNAME SGEMM - perform one of the matrix-matrix operations C := alpha*op( A )*op( B ) + beta*C, SYNOPSIS SUBROUTINE SGEMM ( TRANSA, TRANSB, M, N, K, ALPHA, A, LDA, B, LDB, BETA, C, LDC ) CHARACTER*1 TRANSA, TRANSB INTEGER M, N, K, LDA, LDB, LDC REAL ALPHA, BETA REAL A( LDA, * ), B( LDB, * ), C( LDC, * ) PURPOSE …

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  • GitHub - ScaleMP/SEG_SGEMM: This SGEMM workload …

    github.com

    https://github.com/ScaleMP/SEG_SGEMM

    WEBStar 1. master. README. SEGMENTED SGEMM BENCHMARK FOR LARGE MEMORY SYSTEMS. INTRODUCTION. This SEGMENTED SGEMM workload demonstrates the multiplication of very large (single precision) matrices, which is the corner stone of many algorithms, leveraging Intel (R) Math Kernel Library (MKL).

    DA: 53 PA: 29 MOZ Rank: 74