Minuet: Accelerating 3D Sparse Convolutions on GPUs
Jiacheng Yang Christina Giannoula Jun Wu Mostafa Elhoushi James Gleeson Gennady Pekhimenko
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. Minuet proposes to
(i) 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, (ii) 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, and (iii) employ a padding-efficient GEMM grouping approach
that reduces both memory padding and kernel launching overheads. 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. The source code of Minuet
is publicly available at https://github.com/UofT-EcoSystem/Minuet.