# 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

Copyright (c) 2024 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: BSD-3-Clause

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