Minuet

Minuet is a library that efficiently implements sparse convolutions for point clouds on GPUs.

Documentation | Research Paper | Artifact Evaluation

Introduction

Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point cloud by only allowing outputs to specific locations. To efficiently compute SC, prior SC engines first use hash tables to build a kernel map that stores the necessary General Matrix Multiplication (GEMM) operations to be executed (Map step), and then use a Gather-GEMM-Scatter process to execute these GEMM operations (GMaS step).

In this work, we analyze the shortcomings of prior state-of-the-art SC engines, and propose Minuet, a novel memory-efficient SC engine tailored for modern GPUs, where we

• Replace the hash tables used in the Map step with a novel segmented sorting double-traversed binary search algorithm that highly utilizes the on-chip memory hierarchy of GPUs;
• Use a lightweight scheme to autotune the tile size in the Gather and Scatter operations of the GMaS step, such that to adapt the execution to the particular characteristics of each SC layer, dataset, and GPU architecture;
• Employ a padding-efficient GEMM grouping approach that reduces both memory padding and kernel launching overheads.

Benchmarks

Our evaluations show that Minuet significantly outperforms prior SC engines by on average $1.74\times$ (up to $2.22\times$) for end-to-end point cloud network executions. Our novel segmented sorting double-traversed binary search algorithm achieves superior speedups by $15.8\times$ on average (up to $26.8\times$) over prior SC engines in the Map step.

End-To-End Performance

Map Step Performance

Installation

pip3 install "torch~=2.1" "packaging~=23.2"
CMAKE_BUILD_PARALLEL_LEVEL=$(nproc) pip3 install .

License

Please refer to the LICENSE file.