Faster Scientific Simulations with New FP64 Algorithms in NVIDIA cuBLAS
<img width="768" height="431" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-768x431.png" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 5px; clear:both;max-width: 100%;" link_thumbnail="" decoding="async" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-768x431.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-179x100.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-300x168.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-625x351.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-645x362.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-660x370.png 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-500x281.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-160x90.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-362x203.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-196x110.png 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-1024x575.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-960x540.png 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7.webp 1536w" sizes="(max-width: 768px) 100vw, 768px" title="image5" />Many scientific simulations, from molecular chemistry to materials science, spend the majority of their execution time computing high-precision dense general...<img width="768" height="431" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-768x431.png" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 5px; clear:both;max-width: 100%;" link_thumbnail="" decoding="async" loading="lazy" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-768x431.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-179x100.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-300x168.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-625x351.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-645x362.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-660x370.png 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-500x281.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-160x90.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-362x203.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-196x110.png 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-1024x575.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7-960x540.png 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image5-7.webp 1536w" sizes="auto, (max-width: 768px) 100vw, 768px" title="image5" /><p>Many scientific simulations, from molecular chemistry to materials science, spend the majority of their execution time computing high-precision dense general matrix multiplications (GEMM). These operations are called DGEMM for real 64-bit floating-point numbers and ZGEMM for complex 64-bit floating-point numbers. cuBLAS is widely used by HPC and AI applications to accelerate GEMM and other Basic…</p>
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