Cross-Configuration Benchmarking

• Cross-configuration benchmarking is a systematic approach that evaluates systems across varied configuration parameters to yield reproducible performance metrics.
• It employs modular frameworks, exhaustive parameterization, and rigorous statistical analysis to ensure fairness and scientific rigor in evaluations.
• Key applications include heterogeneous computing, algorithm configuration, and robust ML evaluation, driving scalable and precise performance assessments.

A cross-configuration benchmark is a systematic approach to evaluating algorithms, software systems, or hardware platforms across a controlled and diverse set of configuration parameters—such as problem instances, device architectures, compiler settings, and hyperparameters—so that the resulting performance, correctness, and robustness metrics reflect realistic, reproducible, and broadly comparable results. Cross-configuration benchmarking is foundational for research on heterogeneous computing, algorithm configuration, optimization, formal systems, and robust ML evaluation, requiring modular frameworks, exhaustive parameterization, precise validation, and careful statistical analysis. This article provides a comprehensive survey of methodological foundations, design patterns, practical workflows, evaluation metrics, and field-specific implementations of cross-configuration benchmarking.

1. Conceptual Foundations and Motivation

The central motivation for cross-configuration benchmarking is two-fold: (i) heterogeneity—increasing divergence in device architectures, system parameters, and algorithmic choices necessitates evaluation across a configuration space, and (ii) fairness and scientific rigor—avoiding misleading results stemming from single-point “arbitrary specificity” or ambiguous, undocumented system factors “arbitrary ambiguity”. In high-performance computing, for example, performance and energy efficiency must be characterized not only for a single device or kernel variant, but across multiple CPUs, GPUs, compilers, kernel parameterizations, and problem scales (Johnston et al., 2018, Wang et al., 2024). In algorithm configuration and machine learning, results are only scientifically meaningful if they aggregate over instance sets, hyperparameter sweeps, and random seeds (Eggensperger et al., 2017, Patterson et al., 2024, Eimer et al., 2021).

The cross-configuration paradigm opposes the practice of “favorite point” benchmarking—selecting one set of flags or a representative instance—and instead treats each evaluable configuration as a valid scenario within a factorial (or sampled) design, enabling quantitative statements about mean behavior, variability, and confidence intervals (Wang et al., 2024).

2. Benchmark Suite Architecture and Parameterization

A robust cross-configuration benchmark suite is structured around a modular, hierarchical design: a portable harness discovers hardware, device, or environment properties at runtime; benchmarks (“kernels,” “dwarfs”) encapsulate configuration descriptors, variant implementations, and problem-size generators; and measurement tools abstract timing, energy, and validation mechanisms (Johnston et al., 2018, Wilfong et al., 16 Sep 2025). Parameter exposure is critical: through command-line flags, config files, or APIs, the user selects problem sizes (L1/L2-sized, global memory, etc.), work-group or thread group sizes, kernel algorithmic variants, data layout (AoS vs. SoA), and additional device- or instance-specific settings.

Sampling the configuration space is managed via:

• Exhaustive sweeps for small parameter spaces.
• Latin-hypercube, random, or factorial design subsetting for combinatorially large spaces (Wang et al., 2024).
• Automated optimal+SPEC-recommended sampling for standardized suites (as in SPEC CPU) (Wang et al., 2024).

Frameworks such as OpenDwarfs for OpenCL deploy a single driver to enumerate platforms/devices, instantiate parameter sets from JSON/XML descriptors, compile with per-vendor flags, and collect results per region/timing (Johnston et al., 2018). In algorithm configuration, the parameter space Θ (including categorical, conditional, and numeric hyperparameters) is fully encoded and all dimensions are either systematically explored or modeled via surrogate regression (Eggensperger et al., 2017).

3. Evaluation Methodology and Metric Design

Performance, correctness, and robustness are the three pillars of cross-configuration benchmarking.

• Performance: Host and kernel timing (sub-microsecond via LibSciBench), speedup ($S = T_{ref} / T_{target}$), throughput (GFLOP/s, GB/s), and “grindtime” ($T_w = W / N_g$—wall-time per gridpoint) serve as primary metrics in heterogeneous computing (Johnston et al., 2018, Wilfong et al., 16 Sep 2025).
• Statistical Aggregation: Results are not summarized by single best or worst values. Instead, sample means, standard deviations, and confidence intervals (using Student’s t or normal quantiles) are reported, treating each configuration equally (Wang et al., 2024). For software variant comparison, Kendall’s $\tau$ and Spearman’s $\rho$ quantify ranking agreement between aggregated performance vectors (Matricon et al., 8 Sep 2025).
• Energy and Power: Integration with vendor power APIs (RAPL, NVML, ROCm) enables quantification of energy-to-solution per configuration (Johnston et al., 2018).
• Correctness: Output is cross-validated against reference implementations with elementwise absolute or relative tolerances ($\epsilon_{abs}$, $\epsilon_{rel}$), accompanied by multi-precision and hash-based checks for large outputs (Johnston et al., 2018). In numerical computing, interval libraries are assessed for both containment (correctness) and width inflation (tightness) against exact-arithmetic baselines (Tang et al., 2021).
• Coverage Metrics: Fraction of configuration space sampled, variance within/between configurations, and effect-size estimates (Wilcoxon rank-biserial, etc.) are reported to judge comprehensiveness and statistical power (Matricon et al., 8 Sep 2025, Wang et al., 2024).

4. Surrogate Modeling and Benchmark Reduction for Scalability

Full factorial benchmarking across large configuration-product spaces is often intractable. Two principal strategies address this:

• Surrogate Regression Models: Empirical performance models (EPMs), typically random forests or quantile regression forests, are fit to available configuration-instance-performance triples (θ, π, y), enabling orders-of-magnitude faster evaluation by replacing expensive simulations with cheap, predictive calls (Eggensperger et al., 2017). Surrogates must be validated both off-line (RMSE, Spearman ρ) and in situ (agreement of trajectory/ranking statistics). Imputation strategies for censored data (adaptive capping) use EM-like truncated normal estimation (Eggensperger et al., 2017).
• Test Suite Minimization: BISection Sampling (BISS) reduces the set of benchmark tasks while exactly preserving the ranking of software variants, through recursive variance-reduction, bisection, and divide-and-conquer merging, subject to invariance of Kendall's $\tau$ (Matricon et al., 8 Sep 2025). Experiments show mean benchmark cost reductions of 44% with BISS, outperforming random, greedy, PCA, and integer-programming baselines (Matricon et al., 8 Sep 2025).

A plausible implication is that as configuration spaces and variant sets continue to grow with advances in heterogeneous computing and AI, scalable surrogate- or reduction-based methodologies will be essential to maintain tractable, informative, and reproducible comparative studies.

5. Validation, Reproducibility, and Reporting

Cross-configuration benchmarking mandates rigorous validation at multiple levels:

• Ground-truth Reference Implementations: Output of each configuration is checked against a “golden” host or exact-arithmetic reference, with failures logged for manual inspection (Johnston et al., 2018, Tang et al., 2021, Wilfong et al., 16 Sep 2025).
• Reproducibility Logging: Full metadata capture (device/driver/provenance, kernel build logs, seeds, environmental variables) is required; experiment serialization enables others to reload and replicate identical benchmark conditions (Eimer et al., 2021, Wilfong et al., 16 Sep 2025).
• Automated Testing Suites: Regression tests cover the full allowable range of parameter settings, configurations, and variants, using tight-tolerance checks for floating-point or integer output (Wilfong et al., 16 Sep 2025).
• Standardized APIs: Gym-style (RL), CMake-based (OpenCL/HPC), or Python decorator-based (GUI agents) interfaces enforce uniformity for runner and logger logic (Johnston et al., 2018, Xu et al., 2024, Eimer et al., 2021, Patterson et al., 2024).
• Aggregated and Disaggregated Metrics: All summary statistics must be reported both per-configuration and as aggregate, with error bars, boxplots, and—where relevant—scaling or trade-off plots (Wang et al., 2024, Matricon et al., 8 Sep 2025).
• Best Practice Protocols: Always fix and record all state/action/reward definitions (for RL), random seeds, and instance splits in versioned configuration files (Eimer et al., 2021).

6. Domain-specific Applications and Case Studies

Cross-configuration benchmarking has been deployed in:

• Heterogeneous Computing: Modular OpenCL benchmark suites (e.g., OpenDwarfs) systematically assess CPUs, GPUs, accelerators, supporting variant kernels and device-specific tuning (Johnston et al., 2018). Portable toolchains like MFC abstract batch scheduling, compilation, and test/benchmark orchestration for supercomputers and emerging GPU/CPU architectures (Wilfong et al., 16 Sep 2025).
• Algorithm Configuration and Hyperparameter Optimization: Surrogate benchmarks emulate realistic AC scenarios, yielding 1000× speedups while preserving outcome fidelity (Eggensperger et al., 2017). Cross-environment hyperparameter benchmarks (CHS) in RL enforce global optimum identification and robust policy ranking under CDF-normalized aggregation (Patterson et al., 2024).
• Dynamic Algorithm Control: DACBench frames dynamic tuning as a contextual MDP, capturing both static and online hyperparameter scheduling policies in a unified evaluation protocol and enabling logging, visualization, and challenge dimension characterization (Eimer et al., 2021).
• Software Variant/Leaderboard Evaluation: BISS reduces instance costs for large LLM, SAT, and performance-modeling leaderboards while maintaining exact ranking, enabling scalable evaluation across evolving variant sets (Matricon et al., 8 Sep 2025).
• Numerical Library Certification: Cross-platform interval arithmetic benchmarks enumerate compiler, OS, and library variants, validating correctness (containment rate), tightness (interval width), and speed, with explicit recommendations for deployment robustness (Tang et al., 2021).
• Formal Reasoning Benchmarks: Cross-system formal mathematics datasets (miniF2F) drive comparative neural theorem proving, requiring uniform oracle-problem formalizations, standardized metrics (Pass$@$N), and reproducibility across Lean, Metamath, and others (Zheng et al., 2021).

7. Limitations, Pitfalls, and Evolving Practices

Despite its robustness, cross-configuration benchmarking faces key challenges and caveats:

• Curse of Dimensionality: The exponential growth of configuration-product spaces places severe strain on time and compute budgets, necessitating judicious parameter selection, sampling, or surrogate modeling (Wang et al., 2024, Eggensperger et al., 2017).
• Randomization and Statistical Variance: Methods such as BISS are randomized and may occasionally yield different minimal sets or masking of ties, necessitating careful multi-run aggregation and sanity-check baselines (Matricon et al., 8 Sep 2025).
• Reproducibility-on-New Hardware: As new compilers, devices, or OS versions become available, golden references may require extension or refinement, raising maintenance overhead (Wilfong et al., 16 Sep 2025, Tang et al., 2021).
• Corner-case Sensitivity: Certain configuration regions—e.g., specific dataset sizes or kernel parameters—can exhibit pathologies (cache collisions, compiler bugs, performance cliffs) invisible to default-point benchmarking (Wang et al., 2024, Wilfong et al., 16 Sep 2025).
• Coverage vs. Cost Trade-offs: Enhancing cross-configuration coverage improves generality but imposes computation and reporting burdens; optimal subsetting remains an open, context-dependent question (Matricon et al., 8 Sep 2025).
• Evolving Metrics and Protocols: As new fields and modalities (multimodal agents, online RL, quantum/AI hardware) emerge, configuration axes, metrics, and validation strategies must evolve accordingly (Xu et al., 2024, Eimer et al., 2021, Patterson et al., 2024).

In sum, cross-configuration benchmarking provides the only rigorous pathway to robust, reproducible, and fair algorithm and system evaluation in modern, heterogeneous, and rapidly evolving computing environments. Its implementation demands modular design, exhaustive parameter exposure, meticulous validation, statistical discipline, and continual adaptation to new scientific and engineering domains.

References:

(Johnston et al., 2018, Wang et al., 2024, Eggensperger et al., 2017, Matricon et al., 8 Sep 2025, Eimer et al., 2021, Tang et al., 2021, Wilfong et al., 16 Sep 2025, Zheng et al., 2021, Patterson et al., 2024, Xu et al., 2024).