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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. 

Modern operating systems allow user-space applications to submit code for kernel execution through the use of in-kernel domain specific languages (DSLs). Applications use these DSLs to customize system policies and add new functionality. For performance, the kernel executes them via just-in-time (JIT) compilation. The correctness of these JITs is crucial for the security of the kernel: bugs in in-kernel JITs have led to numerous critical issues and patches. This paper presents JitSynth, the first tool for synthesizing verified JITs for in-kernel DSLs. JitSynth takes as input interpreters for the source DSL and the target instruction set architecture. Given these interpreters, and a mapping from source to target states, JitSynth synthesizes a verified JIT compiler from the source to the target. Our key idea is to formulate this synthesis problem as one of synthesizing a per-instruction compiler for abstract register machines. Our core technical contribution is a new compiler metasketch that enables JitSynth to efficiently explore the resulting synthesis search space. To evaluate JitSynth, we use it to synthesize a JIT from eBPF to RISC-V and compare to a recently developed Linux JIT. The synthesized JIT avoids all known bugs in the Linux JIT, with an average slowdown of 1.82x in the performance of the generated code. We also use JitSynth to synthesize JITs for two additional source-target pairs. The results show that JitSynth offers a promising new way to develop verified JITs for in-kernel DSLs. 

Utilizing memory and register bandwidth in modern architectures may require swizzles — non-trivial mappings of data and computations onto hardware resources — such as shuffles. We develop Swizzle Inventor to help programmers implement swizzle programs, by writing program sketches that omit swizzles and delegating their creation to an automatic synthesizer. Our synthesis algorithm scales to real-world programs, allowing us to invent new GPU kernels for stencil computations, matrix transposition, and a finite field multiplication algorithm (used in cryptographic applications). The synthesized 2D convolution and finite field multiplication kernels are on average 1.5–3.2x and 1.1–1.7x faster, respectively, than expert-optimized CUDA kernels.