Measurement overview
Measurements & Computational Validation
Independent ARM64 measurement layer focused on reproducible runtime validation, cross-system verification, structured archive continuity, and correctness-first computational interpretation.
Benchmark board
P0.7 Canonical Binary Benchmark
Public measurement evidence and controlled archive evidence are kept separate on purpose. This board defines the public interpretation window for the current P0.7 benchmark series.
Public window
10M - 10B
Open measurement layer and validated public evidence range.
Controlled archive
BRONZE 10M–100B • SILVER 10M–200B • GOLD 10M–500B
Extended evidence layer preserved under a stricter publication boundary.
Full workflow runtime
Active
Correctness-first execution path preserving reproducibility, traceability, and audit value.
Directional timing
Indicative
Clean-binary timing remains a throughput signal until deeper instrumentation becomes fully auditable.
Controlled Runtime Pipeline
MAYAN ALFA uses a bounded execution and publication path where observation, comparison, archive tiering, and release interpretation remain explicitly separated.
Run the benchmark
Execute canonical measurement runs under controlled runtime conditions while preserving mode, scale, and reference context.
Compare outputs
Check MAYAN ALFA against MR-only and primesieve reference layers before any publication-facing interpretation is written.
Classify the range
Assign each result to the correct publication boundary: public 10M–10B or controlled archive tiers beyond the open release window.
Archive tiering
Approved outputs are split between the public validation layer and the controlled archive model for long-term research continuity.
Full Workflow Runtime and Clean-Binary Timing Stay Separated.
The pipeline does not collapse end-to-end runtime, clean-binary timing, and later instrumentation into one performance claim. Each observation keeps its own mode, purpose, and publication context.
- Full workflow runtime
- Clean-binary directional timing
- Execution mode attached to every result
- Range and archive tier kept visible
Only Bounded, Reproducible Observations Move into Public or Controlled Layers.
Every dataset is checked as a measurement package rather than as an isolated number. That keeps the public layer readable and the larger archive layer methodologically controlled.
- Limit, step, mode, and archive tier stay attached to the reported value.
- Independent comparison layers must agree before release packaging starts.
- Public releases stop at 10B while the 10M–500B controlled archive model remains in the archive lane.
Measurement Outputs Become Structured Archive Memory.
Reports, graphs, compare tables, QA notes, catalogs, and release manifests are all treated as parts of one evidence package rather than as disconnected files.
Controlled Continuity Layers for BRONZE / SILVER / GOLD
Extended comparison evidence, larger archive memory, and controlled comparison packages retained outside the public presentation layer.
BRONZE 10M–100B · SILVER 10M–200B · GOLD 10M–500B
Public Evidence Layers and Controlled Validation Archives.
Public datasets and controlled archive layers are separated so the public window stays readable while larger evidence layers remain bounded, traceable, and release-safe.
Open Measurement Evidence Layer
Public datasets provide transparent measurement visibility and reproducible reference material for the validated public range.
- Validated datasets up to 10B
- Cross-system validation summaries
- Public benchmark manifests
- GitHub and Zenodo release layers
10M–500B Controlled Evidence Layer
Controlled computational archives extend validation across larger research layers through structured comparison, archive continuity, and stricter publication boundaries.
- 100B controlled evidence
- 200B controlled evidence
- 500B controlled reference layer
- Long-term reproducible archive continuity
- Validation and comparison reports
- Research-oriented evidence packaging
Observation-First Methodology for Structured Computational Validation.
MAYAN ALFA is structured as a deterministic computational observation framework focused on reproducibility, validation discipline, and long-term archive continuity.
Measured Observation
Computational outputs are treated as measured observations generated under controlled execution conditions with explicit runtime context.
Cross-System Validation
Benchmark datasets are compared against independent computational systems and preserved together with validation summaries and QA review layers.
Archive Continuity
Structured manifests, validation reports, and evidence packages are preserved as reproducible long-term computational archive layers.
Access Public Benchmark Releases and Controlled Archive Layers.
Public MAYAN ALFA releases provide transparent benchmark validation up to the 10B layer. Extended BRONZE 10M–100B, SILVER 10M–200B and GOLD 10M–500B archive continuity, controlled validation layers and structured computational evidence packages are preserved as research archive releases.
Validation Gates for Reproducible Benchmark Integrity.
Public benchmark outputs are reviewed through a layered validation process so every published observation remains bounded, reproducible, and archive-safe.
Open validation boundary for the current public measurement layer.
Public release datasets must clear the comparison gate before publication.
MAYAN ALFA, MR-only, and primesieve form the comparison framework.
Extended archive evidence remains outside the public presentation range.
Every Public Result Passes a Structured Review Layer.
MAYAN ALFA does not publish isolated numbers. Every release candidate is treated as a bounded measurement evidence package.
- Cross-system comparison against independent computational reference layers.
- Range classification between public and controlled archive tiers.
- Manifest review before publication packaging starts.
- Interpretive discipline attached to every reported runtime.
The Validation Layer Protects Both Clarity and Continuity.
Validation is used not only for correctness, but also for publication boundaries, reproducibility, and long-term archive integrity.
- Public material stays readable and methodologically bounded.
- The controlled 10M–500B archive tiers remain outside the open presentation layer.
- Release notes and summaries stay aligned with benchmark structure.
- Archive continuity is preserved for later controlled research workflows.
Validation Summary
Validation in MAYAN ALFA is a publication gate, not only a technical checklist. The goal is to preserve reliable runtime observation, transparent public release discipline, and protected long-term archive continuity across every benchmark layer.
Structured Measurement Methodology for Controlled Computational Observation.
MAYAN ALFA uses a controlled runtime methodology where observation, validation, archive packaging, and publication boundaries remain explicitly separated.
Runtime Observation
MAYAN ALFA records structured runtime observations across ARM64 measurement ranges while preserving deterministic execution context.
Independent Comparison Layer
Measurement outputs are compared against MR-only execution and primesieve reference layers to verify consistency before interpretation.
Controlled Archive Separation
Public measurement layers remain bounded to the open window, while the controlled 10M–500B archive tiers are retained under controlled archive discipline.
Regenerated Evidence
Benchmark reports, CSV outputs, runtime summaries, and validation artifacts are regenerated after each clean measurement cycle.
Methodology Summary
The MAYAN ALFA framework focuses on measured observation, validation continuity, and structured archive preservation rather than unsupported performance claims or unbounded computational narratives.
Visual Runtime Scaling Across Validated Measurement Layers.
The visualization layer separates normalized comparison views from absolute runtime curves so baseline-relative scaling and raw execution-time progression remain methodologically distinct.
Relative Runtime Scaling
This panel shows normalized relative runtime scaling across the 10M-10B public window. Primesieve is held as the fixed baseline reference at 1.000 for each range, while MAYAN ALFA and MR-only are shown as relative observations against that baseline.
Log-Scale Runtime Curve
This panel shows absolute runtime progression on a logarithmic scale. Unlike the normalized view, the line curve represents raw execution-time behavior in seconds across MAYAN ALFA, MR-only execution, and primesieve reference layers.
Benchmark Visualizations and Runtime Scaling Evidence.
Graph outputs support the public benchmark board with separated visual interpretation of absolute runtime, throughput behavior, speedup structure, and relative comparison layers across the validated public measurement range.
Log-Scale Runtime Comparison
Absolute runtime progression across the validated 10M-10B public measurement range, shown on a logarithmic scale to preserve readability across large execution-time differences.
Observed Throughput Behavior
Observed throughput behavior across the validated public measurement range, showing how computational throughput changes as structured dataset limits increase.
Speedup Against MR-Only Execution
Relative speedup of MAYAN ALFA versus MR-only execution across the public validated range, presented as a comparison layer rather than as an absolute runtime graph.
Relative Runtime vs. Primesieve
Relative runtime comparison between MAYAN ALFA and primesieve under public benchmark conditions, where primesieve functions as a reference layer rather than a standalone performance claim.
Structured Research Distribution Through Public Release Channels.
MAYAN ALFA distributes benchmark outputs through layered public publication channels so runtime summaries, datasets, and archive-ready materials remain transparent, reproducible, and structurally preserved.
Open Repository Release Layer
Source releases, benchmark manifests, public runtime summaries, and validation documentation are distributed through the GitHub repository layer.
- Benchmark manifests and structured release packaging.
- CSV outputs and reproducible runtime summaries.
- Public validation notes and methodological documentation.
- Transparent repository continuity across release generations.
DOI-Oriented Archive Publication
Stable benchmark releases are prepared for long-term archival publication through DOI-oriented research packaging and reproducible preservation workflows.
- Curated release packages for archival preservation.
- Structured metadata for citation-oriented workflows.
- Long-term public access to fixed benchmark snapshots.
- Continuity between public validation layers and formal archives.
Publication Summary
MAYAN ALFA separates runtime observation, validation, archive control, and public distribution into a structured publication workflow. The result is a reproducible benchmark ecosystem designed for transparent review, controlled archive continuity, and long-term research preservation.
