Validated computational research
Benchmark Results & Computational Validation.
Independent ARM64 benchmark observation framework focused on reproducible runtime validation, cross-system verification and structured computational archive discipline.
Benchmark board
P0.7 Canonical Binary Benchmark
Public range and controlled archive range are kept separate on purpose. The table below is the public interpretation window for the current benchmark series.
Public window
10M - 10B
Open presentation layer and the current public validation core.
Controlled archive
100B - 500B
Separate interpretation layer preserved with stricter publication boundaries.
Wall-clock state
Long
The current workflow keeps correctness and traceability ahead of aggressive optimization.
Binary timing
Indicative
Instrumentation is still pending the later benchmark phase.
Structured Measurement Pipeline for Public Validation and Controlled Archive Release.
Benchmark outputs move through separated measurement modes, independent verification, QA release gates, and long-term archival preparation.
Measurement Capture
Execution outputs are generated under controlled ARM64 benchmark conditions with deterministic logging, range metadata, and explicit execution mode labels.
Cross Validation
Outputs are compared against independent computational systems, including MR-only execution and primesieve reference layers, before any public interpretation is made.
QA Release Gate
Validated datasets pass through comparison review, manifest checks, and range classification before they enter the public 10B window.
Archive Tiering
Approved outputs are split between the open validation layer and the controlled 100B+ archive layer for long-term premium research continuity.
Wall-Clock Runtime and Clean-Binary Timing Stay Separated.
The pipeline does not collapse end-to-end runtime, clean-binary timing, and later instrumentation into one speed claim. Each observation keeps its own mode, purpose, and publication context.
- Wall-clock workflow observation
- Clean-binary directional timing
- Execution mode attached to every result
- Range and archive tier kept visible
Only Bounded, Reproducible Observations Move into Public or Premium 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 100B+ remains in the controlled archive lane.
Each Pipeline Pass Produces a Structured Benchmark Evidence Set.
Open Validation Window up to 10B
Transparent benchmark table, public charts, repository artifacts, and readable QA summaries prepared for open review.
Controlled 100B+ Continuity Layer
Extended benchmark evidence, larger archive memory, and premium comparison packages retained outside the public presentation window.
Public Benchmark Layers and Premium Validation Archives.
Public data and premium archive layers are separated so the public window stays readable while the 100B+ layer stays controlled.
Open Benchmark Layer
Public datasets provide transparent benchmark visibility and independent reproducibility for core runtime observations.
- Validated datasets up to 10B
- Cross-system QA summaries
- Public benchmark manifests
- GitHub + Zenodo release layers
100B+ Validation Layer
Premium computational archives extend benchmark validation into large-scale structured dataset environments.
- Extended 100B+ archives
- Long-term reproducible layers
- Premium QA and comparison reports
- Research-oriented packaging
Observation-First Methodology for Structured Computational Research.
MAYAN ALFA is designed as a deterministic computational observation framework focused on validation, reproducibility, and archive integrity.
Measured Observation
All outputs are treated as measured computational observations generated under controlled execution conditions.
Cross-System Validation
Benchmark datasets are validated against independent systems and archived with QA summaries.
Long-Term Discipline
Structured datasets, manifests, and validation reports are preserved as reproducible research layers.
Access Validated Benchmark Releases and Extended Premium Archives.
Public MAYAN ALFA releases provide transparent benchmark validation up to 10B. Extended 100B+ archives, premium QA layers, and long-term computational datasets are prepared as premium research packages.
Runtime Comparison Across Validated Benchmark Layers.
Relative runtime scaling comparison between MAYAN ALFA, MR-only execution, and primesieve reference validation across the public validated core.
Relative Runtime Scaling
The public window remains bounded to 10M–10B, with 100B+ retained as controlled archive evidence.
Validated Log-Scale Runtime Curve
Public core runtime progression across MAYAN ALFA, MR-only execution, and primesieve validation layers.
Benchmark Visualization and Runtime Scaling Analytics.
Graph outputs complement the public benchmark board with visual runtime interpretation across validated computational layers.
Log-Scale Runtime Comparison.
Relative execution progression across the validated 10M–10B public benchmark range.
Observed Throughput Behavior.
Observed throughput scaling behavior across MAYAN ALFA validation layers and structured dataset ranges.
Speedup Against MR-Only Execution.
Relative performance gain of MAYAN ALFA versus MR-only execution across the public validated core.
Relative Runtime vs. Primesieve.
Relative runtime comparison between MAYAN ALFA and primesieve validation layers under public benchmark conditions.
Validation Gates for Reproducible Benchmark Integrity.
Public benchmark outputs are checked through a layered QA process so every published observation remains bounded, reproducible, and archive-safe.
Open validation window for the current public benchmark layer.
Public release datasets are expected to clear the comparison gate before publication.
MAYAN ALFA, MR-only, and primesieve form the comparison framework.
Extended archives remain controlled 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 observation package.
- Cross-system comparison against independent computational layers.
- Range classification between public and controlled archive tiers.
- Manifest review before publication packaging starts.
- Interpretive discipline attached to every reported runtime.
The QA Layer Protects Both Clarity and Continuity.
QA is used not only for correctness, but also for publication boundaries, reproducibility, and long-term archive integrity.
- Public material stays readable and methodologically bounded.
- 100B+ evidence remains outside the open presentation layer.
- Release notes and summaries stay aligned with benchmark structure.
- Archive continuity is preserved for later premium research workflows.
QA Summary
Validation in MAYAN ALFA is a publication gate, not just 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 Benchmark Observation Through a Layered Computational Method.
MAYAN ALFA uses a controlled runtime methodology where observation, validation, archival packaging, and publication boundaries remain explicitly separated.
Primary Runtime Observation
MAYAN ALFA executes structured runtime observation across ARM64 benchmark ranges while preserving deterministic execution methodology.
Independent Comparison Layer
Benchmark outputs are validated against MR-only execution and primesieve reference layers to verify runtime consistency.
Controlled Archive Separation
Public benchmark layers stay bounded to the open window, while 100B+ evidence is retained as a controlled archive tier.
Regenerated Release Evidence
Benchmark reports, CSV outputs, runtime summaries, and validation archives are regenerated after each clean benchmark cycle.
Method Summary
The MAYAN ALFA ecosystem focuses on benchmark observation, validation continuity, and structured archive preservation rather than theoretical claims or unsupported computational narratives.
Public Distribution Through Structured Research Release Channels.
MAYAN ALFA publishes benchmark outputs through layered public channels so runtime summaries, datasets, and archive-ready materials remain transparent and reproducible.
Open Repository Release Layer
Source releases, benchmark manifests, public runtime reports, and validation summaries are published through the GitHub repository layer.
- Release manifests and benchmark package structure.
- CSV outputs and runtime summaries.
- Public-facing validation notes and documentation.
- Repository continuity for transparent release history.
Archive-Ready DOI Publication Layer
Stable release artifacts are prepared for archival publication through long-term DOI-oriented research packaging.
- Curated release packages for archival preservation.
- Structured metadata for research citation workflows.
- Long-term public access to fixed release snapshots.
- Continuity between public benchmark layers and formal archives.
Publication Summary
MAYAN ALFA separates runtime observation, validation, archive control, and public distribution into a disciplined publication workflow. The result is a reproducible benchmark ecosystem that supports both open review and long-term research continuity.
