FP8 is All You Need (Part 2): Efficient Ozaki-Bailey Style FFT Through Tensor-core Garner Reformulation and Kulisch Escape Route
arXiv:2606.23698v1 Announce Type: cross Abstract: NVIDIA's Blackwell Ultra (B300) cuts FP64 vector throughput to ~1.3 TFLOPS per GPU, roughly 30x below B200 and well below the level at which bandwidth-limited FP64 workloads stay memory-bound. The Ozaki Scheme II framework recovers FP64-equivalent throughput by routing dense matrix multiply through FP8 tensor cores with a mantissa-sliced Chinese-remainder reconstruction. A companion Part (1) paper covers dense GEMM, batched GEMV, stencils, and SpMV; this paper adds the fifth canonical primitive, the 3-D FFT. We present Ozaki-Bailey FFT, an emul
The increasing demand for powerful AI models is pushing compute requirements to their limits, necessitating innovative approaches to hardware and software optimization.
This development allows for significant performance recovery for certain high-demand computational tasks, especially in scientific computing, despite hardware limitations in higher precision processing.
GPU architectures like NVIDIA's Blackwell Ultra can now achieve FP64-equivalent throughput for crucial algorithms like FFTs by cleverly leveraging FP8 tensor cores.
- · NVIDIA
- · High-performance computing (HPC) research
- · AI/ML researchers needing high precision
- · Semiconductor industry
Scientific and AI applications that rely on complex numerical methods will see significant speedups without needing to compromise precision.
This methodology could be extended to other computational primitives, further widening the applicability of lower-precision hardware for high-precision tasks.
It might influence future chip design, encouraging architectures that can flexibly handle diverse precision requirements via algorithmic cleverness rather than brute-force high-precision units.
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Read at arXiv cs.AI