Optimizing WebAssembly Bytecode for IoT Devices Using Deep Reinforcement Learning

WebAssembly has shown promising potential on various IoT devices to achieve the desired features such as multi-language support and seamless device-cloud integration. The execution performance of WebAssembly bytecode is directly influenced by compilation sequences. While existing research has explored the optimization of compilation sequences for native code, these approaches are not suitable to WebAssembly bytecode due to its unique instruction format and control flow graph structure. In this work, we propose WasmRL, a novel efficient deep reinforcement learning (DRL)-based compiler optimization framework tailored for WebAssembly bytecode. We conduct a fine-grained analysis of the characteristics of WebAssembly instructions and associated compilation flags. We observe that the same compilation sequence may yield contrasting performance outcomes in WebAssembly and native code. Motivated by our observation, we introduce a WebAssembly-specific DRL state representation that simultaneously captures the impact of various compilation sequences on the WebAssembly bytecode and its runtime performance. To enhance the training efficiency of the DRL model, we propose a tree-based action space refinement method. Furthermore, we develop a pluggable cross-platform training strategy to optimize WebAssembly bytecode across different IoT devices. We evaluate the performance of WasmRL extensively on PolybenchC, MiBench, Shootout public datasets and real-world IoT applications. Experimental results show: (1) The DRL model trained on a specific device achieves 1.4x/1.1x speedups over -O3 for seen/unseen programs; (2) The DRL model trained on different devices simultaneously achieves 1.21x/1.06x improvements respectively. The code has been available at https://github.com/CarrollAdmin/WasmRL .