Optimal Kernel Orchestration for Tensor Programs with Korch

TL;DR

Korch is a tensor program optimizer that decomposes tensor operators into primitives and uses linear programming to find optimal kernel orchestration, significantly improving GPU execution efficiency for deep neural networks.

Contribution

Korch introduces a novel approach combining operator fission and constrained optimization to discover optimal kernel orchestration strategies for tensor programs.

Findings

Outperforms existing optimizers by up to 1.7x on V100 GPUs

Achieves up to 1.6x speedup on A100 GPUs

Effective across a variety of DNN models

Abstract

Kernel orchestration is the task of mapping the computation defined in different operators of a deep neural network (DNN) to the execution of GPU kernels on modern hardware platforms. Prior approaches optimize kernel orchestration by greedily applying operator fusion, which fuses the computation of multiple operators into a single kernel, and miss a variety of optimization opportunities in kernel orchestration. This paper presents Korch, a tensor program optimizer that discovers optimal kernel orchestration strategies for tensor programs. Instead of directly fusing operators, Korch first applies operator fission to decompose tensor operators into a small set of basic tensor algebra primitives. This decomposition enables a diversity of fine-grained, inter-operator optimizations. Next, Korch optimizes kernel orchestration by formalizing it as a constrained optimization problem, leveraging an off-the-shelf binary linear programming solver to discover an optimal orchestration strategy, and generating an executable that can be directly deployed on modern GPU platforms. Evaluation on a variety of DNNs shows that Korch outperforms existing tensor program optimizers by up to 1.7x on V100 GPUs and up to 1.6x on A100 GPUs. Korch is publicly available at https://github.com/humuyan/Korch.