Teola: Towards End-to-End Optimization of LLM-based Applications

• URL: http://arxiv.org/abs/2407.00326v1
• Date: Sat, 29 Jun 2024 05:59:53 GMT
• Title: Teola: Towards End-to-End Optimization of LLM-based Applications
• Authors: Xin Tan, Yimin Jiang, Yitao Yang, Hong Xu,
• Abstract summary: Large language model (LLM)-based applications contribute to the end-to-end latency. Existing frameworks employ coarse-grained orchestration with task modules, which confines optimizations to within each module. We propose fine-grained end-to-end orchestration, which utilizes task primitives as the basic units and represents each query's workflow as a primitive-level dataflow graph.
• Score: 13.478509565946354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM)-based applications consist of both LLM and non-LLM components, each contributing to the end-to-end latency. Despite great efforts to optimize LLM inference, end-to-end workflow optimization has been overlooked. Existing frameworks employ coarse-grained orchestration with task modules, which confines optimizations to within each module and yields suboptimal scheduling decisions. We propose fine-grained end-to-end orchestration, which utilizes task primitives as the basic units and represents each query's workflow as a primitive-level dataflow graph. This explicitly exposes a much larger design space, enables optimizations in parallelization and pipelining across primitives of different modules, and enhances scheduling to improve application-level performance. We build Teola, a novel orchestration framework for LLM-based applications that implements this scheme. Comprehensive experiments show that Teola can achieve up to 2.09x speedup over existing systems across various popular LLM applications.

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