Analysis of Minuet: A Memory-Efficient Sparse Convolution Engine for Point Cloud Processing
The Minuet engine is a novel approach to processing 3D point clouds using Sparse Convolution (SC) techniques. SC is commonly used for point cloud processing, as it helps preserve the sparsity of input data by only computing operations on specific locations. Minuet aims to improve the efficiency and performance of SC engines, specifically tailored for modern GPUs.
Prior SC engines typically use hash tables to build a kernel map, which stores the necessary General Matrix Multiplication (GEMM) operations to be executed. This approach has been effective, but it has some shortcomings that Minuet addresses. First, Minuet replaces the hash tables with a segmented sorting double-traversed binary search algorithm. This algorithm takes advantage of the on-chip memory hierarchy of GPUs, resulting in more efficient memory utilization.
Another key feature of Minuet is its lightweight scheme for autotuning the tile size in the Gather and Scatter operations of the Gather-GEMM-Scatter process (GMaS step). This feature allows Minuet to adapt the execution to the specific characteristics of each SC layer, dataset, and GPU architecture. By optimizing the tile size, Minuet can achieve better performance and execution efficiency.
In addition, Minuet employs a padding-efficient GEMM grouping approach. This approach aims to reduce both memory padding and kernel launching overheads, further improving the overall efficiency of SC computations. By minimizing unnecessary padding and optimizing the grouping of GEMM operations, Minuet can perform computations more quickly and with less wasted resources.
Evaluations of Minuet against prior SC engines demonstrate significant improvements in performance. On average, Minuet outperforms previous engines by 1.74 times and can achieve up to 2.22 times faster end-to-end point cloud network executions. The novel segmented sorting double-traversed binary search algorithm used in the Map step of Minuet shows remarkable speedups, achieving an average of 15.8 times faster performance compared to previous SC engines, and up to 26.8 times faster in some cases.
The availability of the Minuet source code is a valuable addition, allowing researchers and developers to utilize and build upon the engine’s innovations. This open-source nature promotes collaboration and further advancements in SC techniques for point cloud processing.
In conclusion, the Minuet engine introduces several key improvements to SC processing for point clouds. By addressing the limitations of prior SC engines and utilizing memory-efficient algorithms, adaptive execution schemes, and padding-efficient approaches, Minuet achieves remarkable performance gains. These advancements contribute to the ongoing progress in optimizing point cloud processing on modern GPUs.