arXiv subjects

Eric Hayden Campbell

Publications and source records attributed to Eric Hayden Campbell.

3 recordsLinked to original sources

arXiv2026

Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search

Systems resource management tasks rely primarily on hand-designed heuristics. However, growing hardware heterogeneity and workload diversity require heuristics specialized to particular deployment instances, making manual design expensive and difficult to scale. In this paper, we explore how to synthesize systems heuristics using LLMs. The main challenge is ensuring that generated heuristics execute safely, integrate correctly with the surrounding system, and still achieve strong performance. We propose Vulcan, a framework that identifies LLM-friendly interfaces that isolate core decision logic from the rest of the implementation. With Vulcan, LLM-generated code is restricted to simple stateless decision functions, while trusted runtime abstractions provide rich derived statistics for meaningful policy exploration without system-integration bugs. To ensure execution safety, LLMs synthesize heuristics in a restricted language, Anvil, that guarantees important properties by construction. We evaluate Vulcan across three well-studied domains and demonstrate up to 4.9x higher savings for spot-VM scheduling, up to 2x lower miss ratios for cache eviction, and up to 10% higher application performance for tiered-memory systems, while ensuring execution safety throughout.

cs.OS↗

arXiv2026

Optimal Predicate Pushdown Synthesis

Predicate pushdown is a long-standing performance optimization that filters data as early as possible in a computational workflow. In modern data pipelines, this transformation is especially important because much of the computation occurs inside user-defined functions (UDFs) written in general-purpose languages such as Python and Scala. These UDFs capture rich domain logic and complex aggregations and are among the most expensive operations in a pipeline. Moving filters ahead of such UDFs can yield substantial performance gains, but doing so requires semantic reasoning. This paper introduces a general semantic foundation for predicate pushdown over stateful fold-based computations. We view pushdown as a correspondence between two programs that process different subsets of input data, with correctness witnessed by a bisimulation invariant relating their internal states. Building on this foundation, we develop a sound and relatively complete framework for verification, alongside a synthesis algorithm that automatically constructs optimal pushdown decompositions by finding the strongest admissible pre-filters and weakest residual post-filters. We implement this approach in a tool called Pusharoo and evaluate it on 150 real-world pandas and Spark data-processing pipelines. Our evaluation shows that Pusharoo is significantly more expressive than prior work, producing optimal pushdown transformations with a median synthesis time of 1.6 seconds per benchmark. Furthermore, our experiments demonstrate that the discovered pushdown optimizations speed up end-to-end execution by an average of 2.4$\times$ and up to two orders of magnitude.

cs.PL↗

arXiv2022

Dependently-Typed Data Plane Programming

Programming languages like P4 enable specifying the behavior of network data planes in software. However, with increasingly powerful and complex applications running in the network, the risk of faults also increases. Hence, there is growing recognition of the need for methods and tools to statically verify the correctness of P4 code, especially as the language lacks basic safety guarantees. Type systems are a lightweight and compositional way to establish program properties, but there is a significant gap between the kinds of properties that can be proved using simple type systems (e.g., SafeP4) and those that can be obtained using full-blown verification tools (e.g., p4v). In this paper, we close this gap by developing $Π$4, a dependently-typed version of P4 based on decidable refinements. We motivate the design of $Π$4, prove the soundness of its type system, develop an SMT-based implementation, and present case studies that illustrate its applicability to a variety of data plane programs.

cs.PL↗