Skill UI

This guide details the advanced methodology for structuring the 'Related Work' section for top-tier academic conferences (e.g., ASPLOS). It emphasizes positioning research across multiple parallel domains—including architecture, OS/systems, compilers, and accelerators—and defining the precise technical boundary where your work advances the state of the art.

This guide provides authors with comprehensive best practices for submitting research artifacts (e.g., compilers, benchmarks, proofs) for post-acceptance evaluation at PLDI. It details how to structure the submission, ensuring reproducibility, containerization, and clear documentation to achieve Functional, Reusable, or Available badges.

This guide provides best practices for designing and auditing academic evaluations of programming language features and compilers, following standards used in venues like PLDI. It covers selecting defensible benchmark suites, establishing strong baselines, performing targeted ablations, and measuring multiple performance metrics (runtime, compile time, memory) to ensure research claims are reproducible and scientifically sound.

This guide addresses the stringent requirements for empirical evaluation in compiler and runtime systems, aligning with SIGPLAN's guidelines. It covers critical aspects such as proper warmup discipline, reporting variance and confidence intervals, selecting principled benchmarks, pinning toolchains, and ensuring cross-platform validity. It provides a protocol for generating verifiable measurement logs for academic publications.

A comprehensive guide for authors submitting technical papers to top compiler and systems conferences (like PLDI). It teaches how to structure an argument as a rigorous engineering case, focusing on stating the 'hard case' first, describing mechanisms rather than using adjectives, and ensuring every claim is directly backed by evidence throughout the paper.

This guide helps researchers determine the optimal venue (like POPL, PLDI, OOPSLA) for their academic papers on programming languages. It advises authors on whether their core contribution is theoretical (proofs, semantics), implementation-focused (compilers, benchmarks), or empirical (user studies), ensuring their work reaches the most appropriate specialized reviewer community.

This guide outlines the necessary steps to ensure that parallel computing performance papers are scientifically reproducible. Since parallel results depend heavily on machine architecture, topology, and runtime conditions, proper documentation must cover everything from CPU/GPU specifications and interconnects to thread pinning policies, random seeds, and compiler flags. The focus is on proving the scaling trend, not just the absolute numbers, allowing results to hold true across different hardware platforms.

This guide helps researchers determine the correct academic venue for their work in parallel and concurrent computing. It clarifies the scope of PPoPP versus related conferences (CGO, PLDI, ASPLOS, HPCA, POPL, etc.) by asking: 'Is the parallelism the point, or is it the setting?' Use this framework to distinguish contributions focused on parallel algorithms, compiler optimization, concurrency theory, or hardware microarchitecture.

This specialized skill provides a deep workflow for reverse engineering stripped Go and Rust binaries. It guides the recovery of lost runtime metadata, including symbols, pclntab data, and panic strings. The process addresses language-specific challenges, such as Go's interface structures and Rust's complex async state machines, serving as a crucial complement to general decompilers like IDA or Ghidra.