Debugging Performance Issues in WebAssembly Runtimes via Mutation-based Inference

TL;DR

WarpL is a mutation-based tool that accurately identifies suboptimal instruction sequences causing performance issues in WebAssembly runtimes, improving debugging precision.

Contribution

The paper introduces WarpL, a novel mutation-based method for pinpointing root causes of performance issues in WebAssembly runtimes, addressing limitations of existing debugging techniques.

Findings

WarpL identified the exact causes in 10 out of 12 issues.

WarpL successfully diagnosed six previously unknown issues in Wasmtime.

WarpL is implemented as an open-source tool and evaluated on real-world problems.

Abstract

Performance debugging in WebAssembly (Wasm) runtimes is essential for ensuring the robustness of Wasm, especially since performance issues have frequently occurred in Wasm runtimes, which can significantly degrade the capabilities of hosted services. Many performance issues in Wasm runtimes result from suboptimal compilation of input Wasm programs, for which existing performance debugging methods primarily designed for application-level inefficiencies are not well-suited. In this paper, we present WarpL, a novel mutation-based approach that aims to identify the exact suboptimal instruction sequences responsible for the performance issues in Wasm runtimes, thereby narrowing down the root causes. Specifically, WarpL obtains a functionally similar mutant in which the performance issue does not manifest, and isolates the exact suboptimal instructions by comparing the machine code of the original and mutated programs. We implement WarpL as an open-source tool and evaluate it on 12 real-world performance issues across three widely used Wasm runtimes. WarpL identified the exact causes in 10 out of 12 issues. Notably, we have used WarpL to successfully diagnose six previously unknown performance issues in Wasmtime.