The paper presents a design for executing a full‑FP64 1024³ 3‑D FFT on NVIDIA’s Blackwell Ultra (B300) GPU using FP8 tensor cores. It replaces traditional FP64 arithmetic with a sequence of FP8‑tensor DFT GEMMs, Karatsuba‑based residue combination, and exact CRT reconstruction, leaving only a final conversion for rounding. The main bottleneck identified is a per‑output integer epilogue that limits performance to 63–87 ms, far above the theoretical 12.9 ms roof, and the authors propose modest hardware changes—such as an INT8 tensor core and cross‑column accumulation—to reduce this gap.
arXiv:2606. 23698v1 Announce Type: cross Abstract: NVIDIA's Blackwell Ultra (B300) cuts FP64 vector throughput to ~1.
The article argues that on AI‑optimised NVIDIA B300 GPUs and newer, the FP8 tensor‑core matrix operation—implemented via the CRT‑based Ozaki Scheme II—can become the primary substrate for matrix‑heavy FP64 kernels while maintaining FP64‑grade accuracy. It introduces the Tensor‑Memory Equilibrium (TME) model, a Roofline extension with four parameters, to show that FP8 can match native FP64 performance under certain intensity thresholds and tile‑fusion conditions. The study identifies two notable exceptions—large dense‑square DGEMM and 3‑D FFT—where additional hardware or software adjustments are required to reach the memory roof. whyItMatters":"The paper demonstrates that FP8, with appropriate reconstruction and deconstruction strategies, can replace native FP64 for high‑performance computing workloads on modern GPUs, potentially reducing hardware complexity and energy consumption while preserving accuracy."
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.
arXiv:2606. 06510v2 Announce Type: replace-cross Abstract: Conventional HPC holds that native hardware FP64 is the irreducible foundation of scientific computing.
arXiv:2606. 14598v1 Announce Type: new Abstract: Post-training INT8 (W8A8) quantization of diffusion transformers is widely deployed as a speed optimization, yet on consumer Ampere GPUs it is frequently slower than the FP8 and NF4 alternatives it is meant to beat.
arXiv:2607. 14568v1 Announce Type: cross Abstract: A companion study ran a 35B mixture-of-experts model on a 2011 NVIDIA Tesla C2075 (Fermi, sm_20, 6GB) as a GPU-prefill/CPU-decode hybrid, because the 4-bit model did not fit in device memory (arXiv:2606.
arXiv:2608. 10103v1 Announce Type: cross Abstract: High-performance Tensor Core kernels rely on a low-level PTX pipeline built from asynchronous data movement with cp.
arXiv:2606. 12280v1 Announce Type: new Abstract: Post-training quantization lets large text-to-image diffusion transformers run on consumer GPUs, yet the hardware-specific trade-offs are seldom measured directly.
arXiv:2606. 09377v1 Announce Type: cross Abstract: Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $\alpha$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator.
arXiv:2608. 13756v1 Announce Type: new Abstract: Two GPU kernels implementing the same scaled INT8 GEMM interface are usually treated as interchangeable.
Formal neural network verification -- proving that a network satisfies safety properties for \emph{all} inputs in a specified domain -- is bounded in practice by GPU memory: standard implementations of bound-propagation algorithms (IBP, CROWN, $α$-CROWN) require weight and relaxation-coefficient matrices to reside entirely on one accelerator. We adapt two parallelism techniques originally developed for large-scale model training to the \texttt{auto\_LiRPA}\,/\,$α,β$-CROWN verification framework.
arXiv:2606. 06521v1 Announce Type: cross Abstract: FP8 (E4M3) acceleration for attention computation offers significant throughput gains, but the 3-bit mantissa introduces precision challenges when the softmax probability matrix P is cast to FP8 before the P*V matrix multiplication.