Faster Scientific Simulations with New FP64 Algorithms in NVIDIA cuBLAS

Wire brief

According to public reporting via NVIDIA Developer Blog, many scientific simulations, from molecular chemistry to materials science, spend the majority of their execution time computing high-precision dense general...

In related context, 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 Linear Algebra Subprograms (BLAS) on NVIDIA GPUs. Rapid advances in lower-precision hardware have motivated exploration of a new class of algorithms that deliver full double-precision results on the faster integer Tensor Cores. With CUDA Toolkit 13.4.2, cuBLAS no longer has to choose between accuracy and speed. cuBLAS breaks each double-precision number into integer pieces, multiplies them on the GPU’s Tensor Cores, and reassembles the exact result.

Reporting also notes that the answers keep full double-precision accuracy, but the work runs on faster hardware. Two new algorithms make this more efficient: Ozaki-II needs fewer integer multiplications than the earlier Ozaki-I approach, and the 2M method cuts a complex matrix multiplication from four real multiplications to two. This builds on the Automatic Dynamic Precision (ADP) framework introduced in Unlocking Tensor Core Performance with Floating Point Emulation in cuBLAS , which delivers higher double-precision (FP64) matrix-multiplication performance by mapping FP64 arithmetic onto integer Tensor Cores while preserving native FP64 accuracy. cuBLAS delivers automatic acceleration of DGEMM and ZGEMM with no application code changes. Because speed matters only when accuracy is not compromised, we validate both on real-world applications: coupled cluster singles and doubles (CCSD) in quantum chemistry, and BerkeleyGW in materials science.

Points still worth watching: This post explains how the algorithms work, shows benchmark and application results, and covers how to get started. This Silicon News Wire item is a paraphrased brief; full wording remains on the origin page.

Source & notice

This Silicon News Wire brief paraphrases public reporting from NVIDIA Developer Blog for carbon readers and silicon citation. It is not a verbatim full-text reprint.

Read the original: Faster Scientific Simulations with New FP64 Algorithms in NVIDIA cuBLAS