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1. LLSC: A parallel symbolic execution compiler for LLVM IR

   https://doi.org/10.1145/3468264.3473108  

   Wei, Guannan; Tan, Shangyin; Bračevac, Oliver; Rompf, Tiark (August 2021, 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ’21))

   null (Ed.)

   We present LLSC, a prototype compiler for nondeterministic par- allel symbolic execution of the LLVM intermediate representation (IR). Given an LLVM IR program, LLSC generates code preserving the symbolic execution semantics and orchestrating solver invo- cations. The generated code runs efficiently, since the code has eliminated the interpretation overhead and explores multiple paths in parallel. To the best of our knowledge, LLSC is the first compiler for fork-based symbolic execution semantics that can generate parallel execution code. In this demonstration paper, we present the current development and preliminary evaluation of LLSC. The principle behind LLSC is to automatically specialize a symbolic interpreter via the 1st Futamura projection, a fundamental connection between in- terpreters and compilers. The symbolic interpreter is written in an expressive high-level language equipped with a multi-stage programming facility. We demonstrate the run time performance through a set of benchmark programs, showing that LLSC outperforms interpretation-based symbolic execution engines in significant ways. 

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2. Flexible Non-intrusive Dynamic Instrumentation for WebAssembly

   https://doi.org/10.1145/3620666.3651338  

   Titzer, Ben L.; Gilbert, Elizabeth; Teo, Bradley Wei; Anand, Yash; Takayama, Kazuyuki; Miller, Heather (April 2024, Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)

   A key strength of managed runtimes over hardware is the ability to gain detailed insight into the dynamic execution of programs with instrumentation. Analyses such as code coverage, execution frequency, tracing, and debugging, are all made easier in a virtual setting. As a portable, low-level byte-code, WebAssembly offers inexpensive in-process sandboxing with high performance. Yet to date, Wasm engines have not offered much insight into executing programs, supporting at best bytecode-level stepping and basic source maps, but no instrumentation capabilities. In this paper, we show the first non-intrusive dynamic instrumentation system for WebAssembly in the open-source Wizard Research Engine. Our innovative design offers a flexible, complete hierarchy of instrumentation primitives that support building high-level, complex analyses in terms of low-level, programmable probes. In contrast to emulation or machine code instrumentation, injecting probes at the bytecode level increases expressiveness and vastly simplifies the implementation by reusing the engine's JIT compiler, interpreter, and deoptimization mechanism rather than building new ones. Wizard supports both dynamic instrumentation insertion and removal while providing consistency guarantees, which is key to composing multiple analyses without interference. We detail a fully-featured implementation in a high-performance multi-tier Wasm engine, show novel optimizations specifically designed to minimize instrumentation overhead, and evaluate performance characteristics under load from various analyses. This design is well-suited for production engine adoption as probes can be implemented to have no impact on production performance when not in use. 

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3. Mobile-3DCNN: An Acceleration Framework for Ultra-Real-Time Execution of Large 3D CNNs on Mobile Devices

   https://doi.org/10.1145/3747842  

   Niu, Wei; Sun, Mengshu; Li, Zhengang; Chen, Jou-An; Guan, Jiexiong; Shen, Xipeng; Liu, Jun; Zhang, Mei; Wang, Yanzhi; Lin, Xue; et al (July 2025, ACM Transactions on Architecture and Code Optimization)

   It is challenging to deploy 3D Convolutional Neural Networks (3D CNNs) on mobile devices, specifically if both real-time execution and high inference accuracy are in demand, because the increasingly large model size and complex model structure of 3D CNNs usually require tremendous computation and memory resources. Weight pruning is proposed to mitigate this challenge. However, existing pruning is either not compatible with modern parallel architectures, resulting in long inference latency or subject to significant accuracy degradation. This paper proposes an end-to-end 3D CNN acceleration framework based on pruning/compilation co-design called Mobile-3DCNN that consists of two parts: a novel, fine-grained structured pruning enhanced by a prune/Winograd adaptive selection (that is mobile-hardware-friendly and can achieve high pruning accuracy), and a set of compiler optimization and code generation techniques enabled by our pruning (to fully transform the pruning benefit to real performance gains). The evaluation demonstrates that Mobile-3DCNN outperforms state-of-the-art end-to-end DNN acceleration frameworks that support 3D CNN execution on mobile devices, Alibaba Mobile Neural Networks and Pytorch-Mobile with speedup up to 34 × with minor accuracy degradation, proving it is possible to execute high-accuracy large 3D CNNs on mobile devices in real-time (or even ultra-real-time). 

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4. WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models

   https://doi.org/10.1145/3689736  

   Yang, Chenyuan; Deng, Yinlin; Lu, Runyu; Yao, Jiayi; Liu, Jiawei; Jabbarvand, Reyhaneh; Zhang, Lingming (October 2024, Proceedings of the ACM on Programming Languages)

   Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences over the software supply chain. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates test programs without sufficient understanding of internal compiler behaviors. As such, they often fail to construct test programs to exercise intricate optimizations. Meanwhile, traditional white-box techniques, such as symbolic execution, are computationally inapplicable to the giant codebase of compiler systems. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and even have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, guiding LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization, with a spotlight on detecting deep logic bugs in the emerging deep learning (DL) compilers. WhiteFox adopts a multi-agent framework: (i) an LLM-based analysis agent examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) an LLM-based generation agent produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are also used as feedback to further enhance the test generation prompt on the fly. Our evaluation on the three most popular DL compilers (i.e., PyTorch Inductor, TensorFlow-XLA, and TensorFlow Lite) shows that WhiteFox can generate high-quality test programs to exercise deep optimizations requiring intricate conditions, practicing up to 8 times more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 101 bugs for the compilers under test, with 92 confirmed as previously unknown and 70 already fixed. Notably, WhiteFox has been recently acknowledged by the PyTorch team, and is in the process of being incorporated into its development workflow. Finally, beyond DL compilers, WhiteFox can also be adapted for compilers in different domains, such as LLVM, where WhiteFox has already found multiple bugs. 

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5. Whose Baseline Compiler is it Anyway?

   https://doi.org/10.1109/CGO57630.2024.10444855  

   Titzer, Ben L. (March 2024, 2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO))

   Compilers face an intrinsic tradeoff between compilation speed and code quality. The tradeoff is particularly stark in a dynamic setting where JIT compilation time contributes to application runtime. Many systems now employ multiple compilation tiers, where one tier offers fast compile speed while another has much slower compile speed but produces higher quality code. With proper heuristics on when to use each, the overall performance is better than using either compiler in isolation. At the introduction of WebAssembly into the Web platform in 2017, most engines employed optimizing compilers and pre-compiled entire modules before execution. Yet since that time, all Web engines have introduced new “baseline” compiler tiers for Wasm to improve startup time. Further, many new non-web engines have appeared, some of which also employ simple compilers. In this paper, we demystify single-pass compilers for Wasm, explaining their internal algorithms and tradeoffs, as well as providing a detailed empirical study of those employed in production. We show the design of a new single-pass compiler for a research Wasm engine that integrates with an in-place interpreter and host garbage collector using value tags, while also supporting flexible instrumentation. In experiments, we measure the effectiveness of optimizations targeting value tags and find, somewhat surprisingly, that the runtime overhead can be reduced to near zero. We also assess the relative compile speed and execution time of six baseline compilers and place these baseline compilers in a two-dimensional tradeoff space with other execution tiers for Wasm. 

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