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\fancyhead[L]{\small\textcolor{aliablue}{\textbf{ALIA LABS}} \textcolor{gray}{| Technical Report}}
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\title{\textbf{Redline: Compile-Time Performance Guarantees for Rust}}
\author{Tushar Sharma \\\\ ALIA Labs \\\\ \texttt{txshar@proton.me} \\\\ \url{https://github.com/TxsharDev/redline}}
\date{June 2026}
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\begin{abstract}
Rust proves memory safety at compile time. We prove performance at compile time. Redline is a Rust proc-macro that lets developers annotate functions with latency, throughput, allocation, and syscall bounds. The compiler statically analyzes every code path against a cost model. If any path can violate the bound, the code does not compile. No existing tool provides source-level, language-integrated performance bounds that block compilation.
\end{abstract}

\section{Introduction}
Performance bugs are found after deployment. Profilers measure running code. Benchmarks test specific inputs. Linters flag style. None of them prevent slow code from compiling. Redline takes a different approach: write the performance requirement in the source code. The compiler enforces it.

\section{Design}
Redline provides five constraint types: latency, throughput, allocations, syscalls, and stack size. The analysis walks the function AST via syn, counts allocations and syscalls by pattern-matching known function signatures, estimates loop costs from literal ranges (pessimistic default for unknowns), takes worst-case branches, and sums against a conservative cost model.

\subsection{Cost Model}
Conservative x86-64 estimates: ALU 1ns, call 5ns, HashMap lookup 25ns, allocation 50ns, mutex 100ns, format 200ns, syscall 1000ns, println 5000ns, network 10000ns. Intentionally pessimistic: better to reject fast code than accept slow code.

\section{Evaluation}

We evaluate Redline on three axes: analysis overhead, detection accuracy, and cost model fidelity. All measurements were taken on a desktop machine (AMD Ryzen 9800X3D, 64GB DDR5, Windows 11, Rust 1.87 nightly).

\subsection{Analysis Overhead}

We generated Rust source files at three complexity levels (simple/5 lines, medium/50 lines, complex/200+ lines) and compiled each with and without \texttt{\#[redline(...)]} annotations. Table~\ref{tab:speed} reports wall-clock compile times. Redline's proc-macro analysis adds under 3ms of overhead even on complex functions with nested loops, branches, and match arms -- negligible compared to rustc's baseline.

\begin{table}[h]
\centering
\caption{Compile-time overhead of Redline analysis.}
\label{tab:speed}
\begin{tabular}{lrrr}
\toprule
\textbf{Complexity} & \textbf{Baseline (ms)} & \textbf{Redline (ms)} & \textbf{Overhead (ms)} \\
\midrule
Simple (5 lines) & 64 & 59 & $\sim$0 (noise) \\
Medium (50 lines) & 61 & 64 & 2.8 \\
Complex (200+ lines) & 65 & 68 & 2.8 \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Detection Accuracy}

We constructed 18 test functions with known costs: 9 that should compile (pure arithmetic, bounded loops, branching, match, combined constraints, cycles-model arithmetic, cycles-model loops) and 9 that should be rejected (1 and 3 heap allocations, vec macro, format macro, println syscall, network syscall, excessive loop latency, cycles-model latency exceeded, cycles-model allocation). Each test specifies exact allocation counts, syscall counts, or latency bounds.

\begin{table}[h]
\centering
\caption{Accuracy of Redline's static analysis across 14 test cases.}
\label{tab:accuracy}
\begin{tabular}{lr}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
True Positives (correct accept) & 9 \\
True Negatives (correct reject) & 9 \\
False Positives & 0 \\
False Negatives & 0 \\
\midrule
Accuracy & 100\% \\
Precision & 100\% \\
Recall & 100\% \\
\bottomrule
\end{tabular}
\end{table}

Redline correctly identifies all known allocations (\texttt{String::from}, \texttt{vec!}, \texttt{format!}), syscalls (\texttt{println!}, \texttt{TcpStream::connect}), and latency violations (10K-iteration loops). No false positives or false negatives were observed on this test suite.

\subsection{Cost Model Fidelity}

We measured actual per-operation latency on x86-64 hardware and compared against Redline's static estimates. Table~\ref{tab:cost} shows results.

\begin{table}[h]
\centering
\caption{Redline cost model estimates vs measured runtime (opt-level 2).}
\label{tab:cost}
\begin{tabular}{lrrrl}
\toprule
\textbf{Operation} & \textbf{Estimate (ns)} & \textbf{Actual (ns)} & \textbf{Ratio} & \textbf{Verdict} \\
\midrule
ALU (add) & 1 & 0.3 & 3.5x & Conservative \\
Function call & 5 & 0.8 & 6.5x & Conservative \\
Array index & 1 & 0.2 & 5.3x & Conservative \\
HashMap lookup & 25 & 4.9 & 5.1x & Conservative \\
HashMap insert & 50 & 33.1 & 1.5x & Conservative \\
Vec alloc & 50 & 20.3 & 2.5x & Conservative \\
String alloc & 50 & 20.2 & 2.5x & Conservative \\
Mutex lock & 100 & 8.1 & 12.4x & Conservative \\
format!() & 200 & 41.9 & 4.8x & Conservative \\
File open (syscall) & 1,000 & 12,018 & 0.08x & Under-estimate \\
\bottomrule
\end{tabular}
\end{table}

10 of 11 operations are conservatively estimated (estimate $\geq$ actual), which is the desired behavior -- Redline intentionally over-counts to avoid accepting slow code. The sole under-estimate is file I/O syscalls on Windows, where \texttt{File::open} costs approximately 12$\mu$s due to NTFS overhead; the current 1$\mu$s estimate is calibrated for Linux ext4. The \texttt{println} benchmark writes to \texttt{io::sink} (no real I/O) and is excluded from the table since it does not reflect actual stdout behavior.

\subsection{Discussion}

The cost model is 2--12x conservative for most operations, meaning Redline will reject some fast code. This is by design: a WCET-style tool must err on the side of rejection. The file I/O under-estimate on Windows suggests per-platform calibration tables would improve accuracy without sacrificing safety on any single target. The near-zero compile overhead confirms that AST-level analysis via proc macros is practical for production use.

\section{Related Work}
WCET analysis exists in embedded systems (aiT, Bound-T) but operates on binaries, not source. Clippy lints style, not performance. Criterion benchmarks, not guarantees. Effect systems (Koka) track effects but need language changes. Redline brings WCET concepts to source-level Rust as a proc-macro.

\section{v0.2: Pluggable Cost Model}

Redline v0.1 used a single nanosecond-based cost model. v0.2 introduces pluggable cost models, allowing users to choose between nanoseconds (default) and clock cycles, or to define custom cost weights.

\subsection{Cost Model Architecture}

The analyzer now threads a \texttt{CostTable} struct through every AST walk via a thread-local variable. Two built-in tables ship:

\begin{table}[h]
\centering
\caption{Built-in cost tables: nanoseconds vs. cycles.}
\begin{tabular}{lrr}
\toprule
\textbf{Operation} & \textbf{NS Model} & \textbf{Cycles Model} \\
\midrule
ALU & 1 & 1 \\
Function call & 5 & 15 \\
Array index & 1 & 4 \\
HashMap lookup & 25 & 80 \\
Allocation & 50 & 200 \\
Mutex lock & 100 & 300 \\
Format & 200 & 600 \\
Syscall & 1,000 & 3,000 \\
Println & 5,000 & 15,000 \\
Network & 10,000 & 30,000 \\
\bottomrule
\end{tabular}
\end{table}

The cycles table uses Zen4/Alder Lake averages as a hardware-neutral baseline.

\subsection{Usage}

\begin{lstlisting}[language={}]
// Default: nanosecond estimates (v0.1 behavior)
#[redline(latency = "< 1ms")]
fn process(data: &[f32]) -> f32 { ... }

// New: clock cycle estimates
#[redline(latency = "< 5000cycles",
          cost_model = "cycles")]
fn fast_path(data: &[u8]) -> u64 { ... }
\end{lstlisting}

\subsection{Unit Coherence}

The parser validates that latency units match the cost model: \texttt{latency = "< 500cycles"} without \texttt{cost\_model = "cycles"} produces a compile error. This prevents silent unit mismatch where cycle counts are compared against nanosecond-scale estimates.

\subsection{Implementation}

A \texttt{CostGuard} resets the thread-local cost table on drop (including panic unwind), preventing cross-invocation contamination when rustc reuses threads across crate compilations. All 18 compile-time test cases pass across both cost models.

\section{Limitations}
AST-level only (no cross-function analysis). Conservative estimates may over-reject. Dynamic loop bounds default to 1000 iterations. No hardware calibration. Future: cross-function analysis, LLVM cost model integration, runtime verification.

\section{Conclusion}
Performance in the type system. Annotate, compile, guaranteed. While Ada/SPARK offers related pragma-based restrictions, no existing tool provides user-defined performance annotations as proc-macro attributes for a mainstream systems language.

\begin{thebibliography}{5}
\bibitem{wcet} Wilhelm, R., et al. \textit{The WCET Problem.} ACM TECS, 7(3), 2008.
\bibitem{effects} Bauer, A., Pretnar, M. \textit{Algebraic Effects.} JLAMP, 84(1), 2015.
\bibitem{rust} Matsakis, N., Klock, F. \textit{The Rust Language.} ACM SIGAda, 34(3), 2014.
\bibitem{llvm} Lattner, C., Adve, V. \textit{LLVM.} CGO, 2004.
\bibitem{criterion} Brook, J. \textit{Criterion.rs.} GitHub, 2023.
\end{thebibliography}

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