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We present Chipmunk, a new framework to test persistent-memory (PM) file systems for crash-consistency bugs. Using Chipmunk, we discovered 23 new bugs across five PM file systems; most bugs have been confirmed and fixed by developers. The discovered bugs have serious consequences, including making the file system un-mountable or breaking rename atomicity. We present a detailed study of the bugs found using Chipmunk and discuss important lessons learned for designing and testing PM file systems. 

Applications targeting digital signal processors (DSPs) benefit from fast implementations of small linear algebra kernels. While existing auto-vectorizing compilers are effective at extracting performance from large kernels, they struggle to invent the complex data movements necessary to optimize small kernels. To get the best performance, DSP engineers must hand-write and tune specialized small kernels for a wide spectrum of applications and architectures. We present Diospyros, a search-based compiler that automatically finds efficient vectorizations and data layouts for small linear algebra kernels. Diospyros combines symbolic evaluation and equality saturation to vectorize computations with irregular structure. We show that a collection of Diospyros-compiled kernels outperform implementations from existing DSP libraries by 3.1× on average, that Diospyros can generate kernels that are competitive with expert-tuned code, and that optimizing these small kernels offers end-to-end speedup for a DSP application. 

Effective symbolic evaluation is key to building scalable ver- ification and synthesis tools based on SMT solving. These tools use sym- bolic evaluators to reduce the semantics of all paths through a finite program to logical constraints, discharged with an SMT solver. Using an evaluator effectively requires tool developers to be able to identify and re- pair performance bottlenecks in code under all-path evaluation, a difficult task, even for experts. This paper presents a new method for repairing such bottlenecks automatically. The key idea is to formulate the symbolic performance repair problem as combinatorial search through a space of semantics-preserving transformations, or repairs, to find an equivalent program with minimal cost under symbolic evaluation. The key to real- izing this idea is (1) defining a small set of generic repairs that can be combined to fix common bottlenecks, and (2) searching for combinations of these repairs to find good solutions quickly and best ones eventually. Our technique, SymFix, contributes repairs based on deforestation and symbolic reflection, and an efficient algorithm that uses symbolic profil- ing to guide the search for fixes. To evaluate SymFix, we implement it for the Rosette solver-aided language and symbolic evaluator. Applying SymFix to 18 published verification and synthesis tools built in Rosette, we find that it automatically improves the performance of 12 tools by a factor of 1.1×–91.7×, and 4 of these fixes match or outperform expert- written repairs. SymFix also finds 5 fixes that were missed by experts. 

This paper presents Serval, a framework for developing au- tomated verifiers for systems software. Serval provides an extensible infrastructure for creating verifiers by lifting interpreters under symbolic evaluation, and a systematic approach to identifying and repairing verification performance bottlenecks using symbolic profiling and optimizations. Using Serval, we build automated verifiers for the RISC-V, x86-32, LLVM, and BPF instruction sets. We report our experience of retrofitting CertiKOS and Komodo, two systems previously verified using Coq and Dafny, respectively, for automated verification using Serval, and discuss trade-offs of different verification methodologies. In addition, we apply Serval to the Keystone security monitor and the BPF compil- ers in the Linux kernel, and uncover 18 new bugs through verification, all confirmed and fixed by developers.