NVIDIA Hopper SSD (State Space Decomposition) CUDA Example

Overview

This example demonstrates the implementation of State Space Decomposition (SSD) operations on NVIDIA's Hopper GPU architecture. It showcases the use of CUTLASS library components for high-performance tensor computations that efficiently leverage Hopper's advanced hardware capabilities, including TMA (Tensor Memory Accelerator) and warp specialization.

System Requirements

NVIDIA GPU with Hopper Architecture (compute capability 9.0)
CUDA Toolkit 12.0 or newer
C++17 compatible compiler

Build the example

Follow the cutlass example building.

Command Line Options

The example supports the following command line options: --help: Display the usage statement --iterations=<int>: Number of iterations for benchmarking (default: 1) --without_verify: Skip result verification --verbose: Print execution time per kernel --G=<int>: Group size (default: 2) --B=<int>: Batch size (default: 3) --E=<int>: Expanded factor (default: 2) --H=<int>: Number of heads (default: 2)

Limitation

Only support LxDxN = 128x64x128
Uses bfloat16 precision for input/output tensors

Performance

Utilizes TMA (Tensor Memory Accelerator) for efficient memory access
Implements warp-specialized kernels for optimal resource utilization
Limited by the SEGSUM (segment sum) computation part
ALU bound operation

Copyright

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