Search performance benchmarking

search-performance-benchmarking

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Summary

Benchmark and compare the search performance of spectral library query tools by executing identical query sets on baseline and improved systems, measuring wall-clock time, resource consumption, and speedup ratios. This skill validates whether algorithmic or infrastructure improvements achieve claimed performance gains at scale.

When to use

When you have two or more implementations of a spectral search tool (e.g., MASST vs. MASST+) and need to quantify whether claimed performance improvements (e.g., '100-fold speedup') are reproducible. Use this skill to establish empirical evidence of speedup under controlled conditions with identical database and parameter settings.

When NOT to use

• Query sets are not identical or not executed in the same order on both systems — uncontrolled variance will obscure true speedup.
• Database indices, parameters (mass tolerance, scoring), or data splits differ between baseline and candidate — results will reflect configuration differences, not algorithmic improvement.
• Only a single or very small query set is used — variance and statistical confidence will be too low to claim reproducible speedup.

Inputs

• query mass spectrum set (MGF or USI format)
• baseline spectral database (indexed or reference)
• candidate spectral database (identically configured)
• mass tolerance parameter (ppm or Da)
• spectral similarity scoring method (e.g., cosine, dot product)

Outputs

• wall-clock search time for baseline system (seconds or minutes)
• wall-clock search time for candidate system (seconds or minutes)
• speedup ratio (scalar, unitless)
• per-query timing statistics (mean, std dev, confidence interval)
• aggregate search completion status and result count validation
• resource consumption log (CPU, memory utilization)

How to apply

Obtain or prepare a representative query set (e.g., metabolite spectra from public data or a curated benchmark set). Configure both the baseline system (e.g., MASST) and the candidate system (e.g., MASST+) with identical database indices, mass tolerance, and scoring parameters. Execute the entire query set on the baseline system using a single continuous session, recording total wall-clock elapsed time and per-query timing. Repeat identically on the candidate system. Calculate the speedup ratio as (baseline_time ÷ candidate_time) and report with per-query and aggregate statistics (mean, variance, confidence intervals). Document resource consumption (CPU, memory) during both runs to contextualize the speedup claim.

Related tools

• MASST (baseline spectral search system for performance comparison) — https://masst.ucsd.edu/
• MASST+ (candidate improved spectral search system to benchmark against MASST) — https://github.com/mohimanilab/MASSTplus
• GNPS (public web service and database hosting for MASST+ and spectral library data) — https://gnps.ucsd.edu/

Evaluation signals

• Speedup ratio equals or exceeds the claimed threshold (e.g., ~100-fold reduction asserted in abstract); report actual ratio with confidence interval.
• Per-query timing distributions are consistent (low variance or monotonic trend) on both systems, indicating stable and repeatable performance.
• Result count and rank order of spectral similarity hits are identical between baseline and candidate, confirming correctness is preserved during optimization.
• Resource consumption (CPU, memory) during candidate search is proportional to the speedup — e.g., 100-fold time reduction should correlate with equivalent or lower resource usage.
• Benchmark completes on billion-scale database without timeout or resource exhaustion, validating scalability claim stated in finding.

Limitations

• Speedup measurements are sensitive to hardware (CPU model, memory bandwidth, I/O subsystem) and system load; benchmark should be repeated on identical or similar hardware to ensure reproducibility.
• Wall-clock time includes database load and initialization overhead; if baseline system loads database slower, speedup ratio may overstate algorithmic improvement.
• Query set must be representative of real-world usage; a carefully curated benchmark set may not reflect performance on diverse or pathological queries in production.
• Comparisons are valid only when both systems query the same database state (version, indexing, clustering); schema changes or data drift between runs invalidate the benchmark.

Evidence

• [other] Execute the query set on the baseline MASST system and record total wall-clock search time: "Execute the query set on the baseline MASST system and record total wall-clock search time."
• [other] Execute the same query set on MASST+ and record total wall-clock search time: "Execute the same query set on MASST+ and record total wall-clock search time."
• [other] Calculate the speedup ratio and verify it equals approximately 100-fold: "Calculate the speedup ratio (MASST time ÷ MASST+ time) and verify it equals approximately 100-fold."
• [intro] reducing the search time by two orders of magnitude: "reducing the search time by two orders of magnitude"
• [readme] querying against databases of billions of mass spectra, which was not feasible with MASST: "It is capable of querying against databases of billions of mass spectra, which was not feasible with MASST"