Debugging Performance Issues in WebAssembly Runtimes via Mutation-based Inference
Ruiying Zeng, Shuyao Jiang, Wenxuan Zhao, Yangfan Zhou
TLDR
WarpL is a mutation-based tool that debugs WebAssembly runtime performance issues by identifying suboptimal instruction sequences.
Key contributions
- Introduces WarpL, a novel mutation-based approach for debugging Wasm runtime performance.
- Identifies exact suboptimal instruction sequences by comparing original and mutated Wasm programs.
- Evaluated WarpL on 12 real-world issues across three Wasm runtimes, identifying causes in 10.
- Successfully diagnosed six previously unknown performance issues in the Wasmtime runtime.
Why it matters
Performance issues in WebAssembly runtimes significantly degrade hosted services, and existing debugging methods are inadequate. WarpL offers a novel, effective approach to pinpoint these root causes. This improves Wasm runtime robustness and reliability for critical applications.
Original 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.
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