arXiv:2606.06510v1 Announce Type: cross Abstract: Conventional HPC dogma holds that native hardware FP64 silicon is the irreducible foundation of scientific computing -- the "holy grail" of double-precision simulation. This paper argues the dogma is wrong: on AI-optimised GPUs of the B300 generation and beyond, abundant FP8 tensor throughput combined with the Chinese Remainder Theorem-based Ozaki Scheme II recovers memory-roof execution at full FP64 accuracy across the canonical HPC kernel spectrum. NVIDIA's Blackwell Ultra (B300) collapses native FP64 to ~1.3 TFLOPS -- a 31x regression from t
The paper leverages next-generation AI-optimized GPUs (B300 generation) which are becoming available now and beyond, enabling a re-evaluation of traditional HPC computing paradigms.
This challenges fundamental assumptions about high-performance computing, suggesting significant efficiency gains and cost reductions are possible by shifting to lower precision formats for AI and scientific workloads.
The perceived necessity of native FP64 hardware for HPC simulation is diminished, opening the door for FP8-centric architectures to achieve FP64 accuracy with far greater throughput and potentially lower energy consumption.
- · NVIDIA
- · GPU manufacturers
- · AI-optimized hardware designers
- · HPC facilities
- · Traditional CPU manufacturers
- · Developers reliant solely on FP64 hardware
- · Legacy HPC architectures
Increased adoption of AI-optimized GPUs for a broader range of scientific computing tasks previously reserved for specialized HPC systems.
Accelerated convergence of AI and traditional HPC hardware architectures, potentially leading to more unified and efficient data centers.
Reduced barriers to entry for advanced scientific simulations due to lower hardware costs and increased accessibility via more common AI accelerators, fostering innovation.
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Read at arXiv cs.AI