Merge pull request #16 from tensor-compiler/new-website

New website

Author: stephenchouca

.gitignore

@@ -1,2 +1,6 @@
+.DS_Store
 *.swp
-*.DS_Store
+*.swo
+*.swn
+.jekyll-cache/
+_site/

Gemfile

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+gem "webrick", "~> 1.7"
+group :jekyll_plugins do
+  gem "jekyll-remote-theme"
+  gem "jekyll-feed", "~> 0.6"
+  gem "jekyll-sitemap"
+  gem "jekyll-paginate"
+  gem "jekyll-seo-tag"
+  gem 'jekyll-redirect-from'
+end

Gemfile.lock

@@ -0,0 +1,88 @@
+GEM
+  specs:
+    addressable (2.8.0)
+      public_suffix (>= 2.0.2, < 5.0)
+    colorator (1.1.0)
+    concurrent-ruby (1.1.10)
+    em-websocket (0.5.3)
+      eventmachine (>= 0.12.9)
+      http_parser.rb (~> 0)
+    eventmachine (1.2.7)
+    ffi (1.15.5)
+    forwardable-extended (2.6.0)
+    http_parser.rb (0.8.0)
+    i18n (1.10.0)
+      concurrent-ruby (~> 1.0)
+    jekyll (4.2.2)
+      addressable (~> 2.4)
+      colorator (~> 1.0)
+      em-websocket (~> 0.5)
+      i18n (~> 1.0)
+      jekyll-sass-converter (~> 2.0)
+      jekyll-watch (~> 2.0)
+      kramdown (~> 2.3)
+      kramdown-parser-gfm (~> 1.0)
+      liquid (~> 4.0)
+      mercenary (~> 0.4.0)
+      pathutil (~> 0.9)
+      rouge (~> 3.0)
+      safe_yaml (~> 1.0)
+      terminal-table (~> 2.0)
+    jekyll-feed (0.15.1)
+      jekyll (>= 3.7, < 5.0)
+    jekyll-paginate (1.1.0)
+    jekyll-redirect-from (0.16.0)
+      jekyll (>= 3.3, < 5.0)
+    jekyll-remote-theme (0.4.3)
+      addressable (~> 2.0)
+      jekyll (>= 3.5, < 5.0)
+      jekyll-sass-converter (>= 1.0, <= 3.0.0, != 2.0.0)
+      rubyzip (>= 1.3.0, < 3.0)
+    jekyll-sass-converter (2.2.0)
+      sassc (> 2.0.1, < 3.0)
+    jekyll-seo-tag (2.8.0)
+      jekyll (>= 3.8, < 5.0)
+    jekyll-sitemap (1.4.0)
+      jekyll (>= 3.7, < 5.0)
+    jekyll-watch (2.2.1)
+      listen (~> 3.0)
+    kramdown (2.4.0)
+      rexml
+    kramdown-parser-gfm (1.1.0)
+      kramdown (~> 2.0)
+    liquid (4.0.3)
+    listen (3.7.1)
+      rb-fsevent (~> 0.10, >= 0.10.3)
+      rb-inotify (~> 0.9, >= 0.9.10)
+    mercenary (0.4.0)
+    pathutil (0.16.2)
+      forwardable-extended (~> 2.6)
+    public_suffix (4.0.7)
+    rb-fsevent (0.11.1)
+    rb-inotify (0.10.1)
+      ffi (~> 1.0)
+    rexml (3.2.5)
+    rouge (3.28.0)
+    rubyzip (2.3.2)
+    safe_yaml (1.0.5)
+    sassc (2.4.0)
+      ffi (~> 1.9)
+    terminal-table (2.0.0)
+      unicode-display_width (~> 1.1, >= 1.1.1)
+    unicode-display_width (1.8.0)
+    webrick (1.7.0)
+
+PLATFORMS
+  x86_64-darwin-21
+
+DEPENDENCIES
+  jekyll-feed (~> 0.6)
+  jekyll-paginate
+  jekyll-redirect-from
+  jekyll-remote-theme
+  jekyll-seo-tag
+  jekyll-sitemap
+  webrick (~> 1.7)
+
+BUNDLED WITH
+   2.3.12

Makefile

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+remote_theme: chrisrhymes/bulma-clean-theme
+title: "TACO: The Tensor Algebra Compiler"
+google_analytics: UA-93058524-1

_config.yml

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+- name: Docs 
+  link: /docs
+- name: Publications
+  link: /publications.html
+- name: Related Projects
+  link: /related.html
+- name: Web Tool 
+  link: /codegen.html
+- name: GitHub
+  link: https://github.com/tensor-compiler/taco

_data/navigation.yml

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+- heading: MLIR 
+  link: https://mlir.llvm.org/
+  desc: >
+    MLIR is an open-source project that provides an extensible infrastructure for building compilers for domain-specific programming languages.
+    MLIR provides first-class support for sparse tensor operations through the <code>SparseTensor</code> dialect, which the MLIR compiler can compile to LLVM IR using an implementation of TACO's sparse tensor algebra compiler theory.
+  pubs:
+  - title: Compiler Support for Sparse Tensor Computations in MLIR
+    authors: Aart J.C. Bik, Penporn Koanantakool, Tatiana Shpeisman, Nicolas Vasilache, Bixia Zheng, and Fredrik Kjolstad 
+    venue: arXiv Preprint
+    is_thesis: false
+    paper_link: https://arxiv.org/abs/2202.04305
+    slide_link: https://llvm.org/devmtg/2021-11/slides/2021-CompilerSupportforSparseTensorComputationsinMLIR.pdf
+    youtube: x-nHc3hBxHM
+    abstract: >
+      Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage requirements and computational time. Developing and maintaining sparse software by hand, however, is a complex and error-prone task. Therefore, we propose treating sparsity as a property of tensors, not a tedious implementation task, and letting a sparse compiler generate sparse code automatically from a sparsity-agnostic definition of the computation. This paper discusses integrating this idea into MLIR. 
+    bibtex: >
+      @article{https://doi.org/10.48550/arxiv.2202.04305,
+        doi = {10.48550/ARXIV.2202.04305},
+        url = {https://arxiv.org/abs/2202.04305},
+        author = {Bik, Aart J. C. and Koanantakool, Penporn and Shpeisman, Tatiana and Vasilache, Nicolas and Zheng, Bixia and Kjolstad, Fredrik},
+        keywords = {Programming Languages (cs.PL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+        title = {Compiler Support for Sparse Tensor Computations in MLIR},
+        publisher = {arXiv},
+        year = {2022},
+        copyright = {Creative Commons Attribution 4.0 International}
+      }
+

_data/publications.yml

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+items:
+  - title: Data Analytics
+    img: data-analytics.png
+    call_to_action_name: See an example
+    call_to_action_link: docs/data_analytics.html 
+  - title: Scientific Computing
+    img: scientific-computing.png
+    call_to_action_name: See an example
+    call_to_action_link: docs/scientific_computing.html 
+  - title: Machine Learning 
+    img: machine-learning.png
+    call_to_action_name: See an example
+    call_to_action_link: docs/machine_learning.html 

_data/related.yml

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+items:
+  - title: TACO is versatile!
+    icon: fa-wrench
+    description: >
+      TACO supports a wide range of sparse (and dense) linear/tensor algebra computations, from simpler ones like sparse matrix-vector multiplication to more complex ones like MTTKRP on higher-order sparse tensors. Tensors can be stored in a variety of storage formats, including commonly-used sparse matrix and tensor formats like CSR and COO as well as specialized formats like CSF. 
+  - title: TACO is fast!
+    icon: fa-rocket
+    subtitle: 
+    description: >
+      Under the hood, TACO employs a novel compiler-based technique to generate kernels that are optimized for the computations you want to perform. This lets TACO achieve performance that exceeds the MATLAB Tensor Toolbox by up to several orders of magnitude and that is competitive with other high-performance sparse linear/tensor algebra libraries like Eigen, Intel oneMKL, and SPLATT.
+  - title: TACO is easy to use!
+    icon: fa-coffee
+    description: >
+      TACO lets you perform complex sparse linear and tensor algebra computations by writing just a few lines of code. TACO provides C++ and Python APIs that can be used in similar ways as other linear algebra libraries like Eigen and SciPy. The command-line tool and web tool also make it easy to build your own library of custom linear/tensor algebra kernels that you can link to your applications.
+    #call_to_action_name: Call to action 3
+    #call_to_action_link: https://www.csrhymes.com/bulma-clean-theme/page-3/