Conventional HPC holds that native hardware FP64 is the irreducible foundation of scientific computing. On AI-optimized GPUs of the NVIDIA B300 generation and beyond, native FP64 throughput has collapsed to ~1.3 TFLOPS even as FP8 tensor throughput has grown to multiple PFLOPS. We argue something stronger than that this is survivable: the FP8 tensor-core matrix-multiply is the sole computational primitive on which double-precision scientific computing needs to be built. Every canonical kernel -- dense and sparse linear algebra, spectral transforms, stencils -- and every application composing them reduces, via the Chinese Remainder Theorem-based Ozaki Scheme II, to sequences of FP8 matrix operations; the only non-FP8 arithmetic is a bounded, fixed-width integer accumulation at reconstruction. Native FP64 is thereby demoted from a hardware requirement to a derived accuracy guarantee obtained by composition over the FP8 primitive. We organize the claim as a five-layer hierarchy -- the FP8 op, Ozaki II, the basic kernels or Berkeley "dwarfs", composite solvers, and full applications -- and, because the dwarf taxonomy already spans scientific computing, establish it by exhibiting the reduction for every dwarf rather than a sample. The claim is falsifiable, and we build the instrument that tests it: a Tensor-Memory Equilibrium (TME) model extending the Roofline with emulation parameters (alpha, beta, gamma). We identify register-level fusion as the mechanism that keeps emulation memory-bound, project recovered FP64 performance across B300 and Rubin against an H100 baseline, and close the kernel coverage with a companion FFT analysis and compensated reductions. The model could have returned a negative verdict; instead it passes across the dwarfs and their compositions. This is the analytical half of a two-part program, with a follow-on implementation to validate the thesis on real silicon.

翻译：传统HPC认为原生硬件FP64是科学计算不可约简的基础。在NVIDIA B300代及以后的AI优化GPU上，原生FP64吞吐量已降至约1.3 TFLOPS，而FP8张量吞吐量却增长至多个PFLOPS。我们提出一个比"可存活"更强的论点：FP8张量核心矩阵乘法是构建双精度科学计算的唯一计算原语。每个经典内核——稠密和稀疏线性代数、谱变换、模板计算——以及组合它们的每个应用，通过基于中国剩余定理的Ozaki方案II，均简化为FP8矩阵运算序列；唯一的非FP8运算是重建时有限、固定宽度的整数累加。因此，原生FP64从硬件要求降级为通过FP8原语组合得到的导出精度保证。我们将这一主张组织为五层层次结构——FP8运算、Ozaki II、基本内核或Berkeley“矮人”、组合求解器和完整应用——并且由于矮人分类已覆盖科学计算，我们通过展示每个矮人（而非样本）的简化来确立它。该主张可证伪，我们构建了测试它的工具：一种张量内存均衡（TME）模型，通过仿真参数（alpha、beta、gamma）扩展了Roofline模型。我们确定寄存器级融合为保持仿真内存受限的机制，预测B300和Rubin上恢复的FP64性能相对于H100基线，并通过配套FFT分析和补偿归约补全内核覆盖。该模型本可能得出否定结论；但它在矮人及其组合上通过了测试。这是两部分计划中的分析部分，后续实现将在真实芯片上验证该论点。