GitLab

* [Refactor] Enhance GEMM warp partitioning logic for improved performance and flexibility

* Updated the warp partitioning logic in `Gemm::ComputeWarpPartition` to better handle various GEMM policies, including FullRow, FullCol, and Square.
* Implemented checks to dynamically adjust warp allocation based on matrix dimensions, ensuring optimal performance.
* Introduced a new `SelectCopy` template to streamline memory access patterns in CUDA templates, enhancing compatibility across different architectures.
* Refactored the Python `GemmWarpPolicy` class to align with the updated C++ logic, improving clarity and maintainability in warp allocation strategies.

* [Refactor] Optimize matrix multiplication parameters and performance in quickstart example

* Updated thread count in the kernel context from 256 to 128 to enhance performance.
* Increased block sizes for matrix dimensions (M, N, block_M, block_N) to 1024 and 128 respectively, improving computational efficiency.
* Adjusted the pipeline stages in the GEMM loop from 0 to 3 for better parallel execution.
* Cleaned up comments for clarity and corrected a typo in the memory copy comment.

* [Refactor] Simplify Copy type selection in OperandTraits for improved clarity

* Replaced the conditional Copy type definition with a new SelectCopy template in OperandTraits, enhancing readability and maintainability of the code.
* This change streamlines the logic for selecting memory copy patterns based on matrix dimensions and warp configurations.