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Tensor Cores

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EVENT TIMELINE

2

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA cuTile and Jane Street optimize Mamba‑2 forward pass with three Tensor Core kernels

Jane Street implemented the Mamba‑2 state‑space sequence model using NVIDIA's CUDA Tile (cuTile) Python DSL and worked with NVIDIA to optimize three custom forward‑pass kernels—chunk_cumsum, fwd_pass_state, and compute_out—targeting Tensor Cores. Optimizations include register‑resident tiling, TF32 casts, DRAM latency hints, operand routing to Tensor Memory, TMA‑friendly memory layouts, and an autotuner for block size and occupancy per input shape.

6.0

RESEARCH · 1 SOURCE · Google Research

Google Research outlines software optimizations for mixed-input matmul on NVIDIA Ampere (CUTLASS)

A Google Research blog post by Manish Gupta presents software techniques to implement mixed-input matrix multiplication (e.g., F16 inputs × U8 weights) on NVIDIA Ampere Tensor Cores by handling data-type conversion and layout conformance; the methods are released in the open-source NVIDIA/CUTLASS repository and are reported to add minimal software overhead while approaching hardware peak performance. The work targets memory- and compute-heavy LLM workloads by enabling weight-only quantization patterns that reduce model memory footprint.

7.0