Domain > 15745.bearcmu.com

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Date	IP Address
2024-10-25	104.21.78.180 (ClassC)
2025-05-24	104.21.112.1 (ClassC)
2025-07-31	104.21.16.1 (ClassC)

HTTP/1.1 200 OKDate: Sat, 24 May 2025 22:37:35 GMTContent-Type: text/html; charsetutf-8Transfer-Encoding: chunkedConnection: keep-aliveNel: {report_to:cf-nel,success_fraction:0.0,max_age:604800}Server

!DOCTYPE html>html>head>   title>Automatic Generation of Efficient Sparse Tensor Format Conversion      Routines/title>   meta charsetUTF-8 />   meta namegenerator contentTeX4ht (http://www.cse.ohio-state.edu/~gurari/TeX4ht/) />   link relstylesheet typetext/css hrefmain.css />   !-- for beautifying -->   link relstylesheet typetext/css hrefsite.css />   script      typetext/x-mathjax-config> MathJax.Hub.Config({ extensions: tex2jax.js, jax: input/TeX, output/HTML-CSS, tex2jax: { inlineMath:  $,$, $,$ , displayMath:  $$,$$, \,\ , processEscapes: true }, HTML-CSS: { availableFonts: TeX } }); /script>   script typetext/javascript srchttps://cdn.mathjax.org/mathjax/latest/MathJax.js?configTeX-AMS-MML_HTMLorMML>/script>/head>body>   div classmaketitle>      h2 classtitleHead>Automatic Generation of Efficient Sparse Tensor         Format Conversion Routines/h2>      div classauthor>span classcmr-12>Roland Liu, Bear Xiong/span>/div>br />      div classdate>span classcmr-12>December 2024/span>/div>   /div>   h3 classsectionHead>span classtitlemark>1 /span> a idx1-10001>/a>Introduction/h3>   p>   /p>   h4 classsubsectionHead>span classtitlemark>1.1 /span> a idx1-20001.1>/a>Background/h4>   p>Sparse Tensors are n-dimensional vectors with the property of having a significant      portion of its elements being zero. Sparse matrix algorithms play a crucial role in      modern artificial intelligence and deep learning systems, which is built on      the top of linear algebra algorithms. In many scenarios, less than 0.01% of      a sparse tensor’s entries may be non-zero. As a result, it is more memory      efficient to record ONLY the locations and values of the non-zero entries. One      real-life example lies in neural network pruning, where a large portion of      model weights can be zeroed out without accuracy loss. To fully realize the      benefits of neural network pruning, efficient sparse matrix algorithms are      essential.   /p>   p> There are various methods of representing Sparse Tensors: Compressed Sparse

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