NVIDIA Releases New cuBLAS Algorithms to Accelerate Double-Precision Scientific Computing
NVIDIA's CUDA Toolkit 13.4.2 introduces Ozaki-II and 2M algorithms, leveraging integer Tensor Cores to accelerate FP64 matrix operations with significant gains in quantum chemistry and materials science.
NVIDIA has introduced two new algorithms, Ozaki-II and 2M, in the latest release of its CUDA Toolkit 13.4.2 for the cuBLAS math library. These technologies aim to resolve a long-standing contradiction in high-performance computing: how to leverage the faster integer Tensor Cores—designed primarily for low-precision tasks—while maintaining the full accuracy of double-precision floating-point (FP64) calculations. This update allows scientific simulations dependent on high-precision dense general matrix multiplications (GEMM), such as those in molecular chemistry and materials science, to automatically gain speed without requiring any changes to application code.
Traditionally, GPU FP64 units offer high precision but limited throughput, while Tensor Cores provide extreme speed for lower-precision data. The new cuBLAS methods employ "floating-point emulation" techniques, decomposing each double-precision number into multiple integer segments, performing exact integer multiplications on the Tensor Cores, and reassembling the result to meet strict FP64 standards. Specifically, the Ozaki-II algorithm utilizes the Chinese Remainder Theorem (CRT) to significantly reduce the number of required integer multiplications compared to its predecessor, Ozaki-I. Meanwhile, the 2M algorithm optimizes complex matrix multiplication by reducing the operation from four real multiplications to two, thereby lowering computational overhead.
According to NVIDIA's official technical blog, these algorithms demonstrate substantial performance improvements in real-world applications. In quantum chemistry, DGEMM (real double-precision matrix multiplication) using Ozaki-II runs 5.5x faster than native FP64 on NVIDIA VR NVL72 systems and 3.9x faster on GB200. For Coupled Cluster Singles and Doubles (CCSD) calculations—considered the "gold standard" in quantum chemistry—the end-to-end speedup reaches 2x on VR NVL72 and 1.4x on GB200. When employing mixed-precision modes, these gains extend to 5x and 3.5x respectively.