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exanauts · Mar 11, 2024 · 25ffd7b · 25ffd7b
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diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json
@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-03-07T04:56:51","documenter_version":"1.3.0"}}
+{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-03-11T03:54:17","documenter_version":"1.3.0"}}
diff --git a/dev/generic/index.html b/dev/generic/index.html
@@ -16,12 +16,25 @@
 x_gpu = F \ b_gpu
 
 r_gpu = b_gpu - A_gpu * x_gpu
+norm(r_gpu)
+
+# In-place LU
+d_gpu = rand(T, n) |&gt; CuVector
+B_gpu = A_gpu + Diagonal(d_gpu)
+lu!(F, B_gpu)
+
+c_cpu = rand(T, n)
+c_gpu = CuVector(c_cpu)
+ldiv!(x_gpu, F, c_gpu)
+
+r_gpu = c_gpu - B_gpu * x_gpu
 norm(r_gpu)</code></pre><h2 id="Example-2:-Sparse-symmetric-linear-system-with-multiple-right-hand-sides"><a class="docs-heading-anchor" href="#Example-2:-Sparse-symmetric-linear-system-with-multiple-right-hand-sides">Example 2: Sparse symmetric linear system with multiple right-hand sides</a><a id="Example-2:-Sparse-symmetric-linear-system-with-multiple-right-hand-sides-1"></a><a class="docs-heading-anchor-permalink" href="#Example-2:-Sparse-symmetric-linear-system-with-multiple-right-hand-sides" title="Permalink"></a></h2><pre><code class="language-julia hljs">using CUDA, CUDA.CUSPARSE
 using CUDSS
 using LinearAlgebra
 using SparseArrays
 
 T = Float64
+R = real(T)
 n = 100
 p = 5
 A_cpu = sprand(T, n, n, 0.05) + I
@@ -33,16 +46,29 @@
 X_gpu = similar(B_gpu)
 
 F = ldlt(A_gpu, view=&#39;L&#39;)
-ldiv!(X_gpu, F, B_gpu)
+X_gpu = F \ B_gpu
 
 R_gpu = B_gpu - CuSparseMatrixCSR(A_cpu) * X_gpu
-norm(R_gpu)</code></pre><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>If we only store one triangle of <code>A_gpu</code>, we can also use the wrappers <code>Symmetric</code> and <code>Hermitian</code>. For real matrices, both wrappers are allowed but only <code>Hermitian</code> can be used for complex matrices.</p></div></div><pre><code class="language-julia hljs">S_gpu = Symmetric(A_gpu, :L)
+norm(R_gpu)
+
+# In-place LDLᵀ
+d_gpu = rand(R, n) |&gt; CuVector
+B_gpu = A_gpu + Diagonal(d_gpu)
+ldlt!(F, B_gpu)
+
+C_cpu = rand(T, n, p)
+C_gpu = CuMatrix(C_cpu)
+ldiv!(X_gpu, F, C_gpu)
+
+R_gpu = C_gpu - ( CuSparseMatrixCSR(A_cpu) + Diagonal(d_gpu) ) * X_gpu
+norm(R_gpu)</code></pre><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>If we only store one triangle of <code>A_gpu</code>, we can also use the wrappers <code>Symmetric</code> and <code>Hermitian</code> instead of using the keyword argument <code>view</code> in <code>ldlt</code>. For real matrices, both wrappers are allowed but only <code>Hermitian</code> can be used for complex matrices.</p></div></div><pre><code class="language-julia hljs">S_gpu = Symmetric(A_gpu, :L)
 F = ldlt(S_gpu)</code></pre><h2 id="Example-3:-Sparse-hermitian-positive-definite-linear-system-with-multiple-right-hand-sides"><a class="docs-heading-anchor" href="#Example-3:-Sparse-hermitian-positive-definite-linear-system-with-multiple-right-hand-sides">Example 3: Sparse hermitian positive definite linear system with multiple right-hand sides</a><a id="Example-3:-Sparse-hermitian-positive-definite-linear-system-with-multiple-right-hand-sides-1"></a><a class="docs-heading-anchor-permalink" href="#Example-3:-Sparse-hermitian-positive-definite-linear-system-with-multiple-right-hand-sides" title="Permalink"></a></h2><pre><code class="language-julia hljs">using CUDA, CUDA.CUSPARSE
 using CUDSS
 using LinearAlgebra
 using SparseArrays
 
 T = ComplexF64
+R = real(T)
 n = 100
 p = 5
 A_cpu = sprand(T, n, n, 0.01)
@@ -54,8 +80,20 @@
 X_gpu = similar(B_gpu)
 
 F = cholesky(A_gpu, view=&#39;U&#39;)
-ldiv!(X_gpu, F, B_gpu)
+X_gpu = F \ B_gpu
 
 R_gpu = B_gpu - CuSparseMatrixCSR(A_cpu) * X_gpu
-norm(R_gpu)</code></pre><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>If we only store one triangle of <code>A_gpu</code>, we can also use the wrappers <code>Symmetric</code> and <code>Hermitian</code>. For real matrices, both wrappers are allowed but only <code>Hermitian</code> can be used for complex matrices.</p></div></div><pre><code class="language-julia hljs">H_gpu = Hermitian(A_gpu, :U)
-F = cholesky(H_gpu)</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« Home</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.3.0 on <span class="colophon-date" title="Thursday 7 March 2024 04:56">Thursday 7 March 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+norm(R_gpu)
+
+# In-place LLᴴ
+d_gpu = rand(R, n) |&gt; CuVector
+B_gpu = A_gpu + Diagonal(d_gpu)
+cholesky!(F, B_gpu)
+
+C_cpu = rand(T, n, p)
+C_gpu = CuMatrix(C_cpu)
+ldiv!(X_gpu, F, C_gpu)
+
+R_gpu = C_gpu - ( CuSparseMatrixCSR(A_cpu) + Diagonal(d_gpu) ) * X_gpu
+norm(R_gpu)</code></pre><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>If we only store one triangle of <code>A_gpu</code>, we can also use the wrappers <code>Symmetric</code> and <code>Hermitian</code> instead of using the keyword argument <code>view</code> in <code>cholesky</code>. For real matrices, both wrappers are allowed but only <code>Hermitian</code> can be used for complex matrices.</p></div></div><pre><code class="language-julia hljs">H_gpu = Hermitian(A_gpu, :U)
+F = cholesky(H_gpu)</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« Home</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.3.0 on <span class="colophon-date" title="Monday 11 March 2024 03:54">Monday 11 March 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>