MAYAN_ALFA
RESEARCH
RESEARCH
Independent Computational Observation Framework focused on ARM64 benchmarking, reproducible validation and long-term archive discipline.
VIEW RESEARCH
ABOUT THE PROJECT
Independent research framework for computational observation.
MAYAN_ALFA is built as an observation-first research environment for benchmark discipline, deterministic validation, reproducible datasets and long-term archive integrity.
01
Observation before interpretation
The project records measured computational behavior first. Interpretation, comparison and
publication come only after validation and QA.
CORE PILLARS
A disciplined system for measured computational work.
01
Benchmark Validation
Cross-checking computational outputs across independent methods, summary files and QA reports.
02
ARM64 Observation
Focused measurement of runtime behavior, throughput, scaling and platform-specific execution patterns.
03
Dataset Archive
Public releases up to 10B, with extended 100B+ validated archives prepared as premium datasets.
VALIDATED PERFORMANCE
Benchmark datasets validated across independent systems.
PUBLIC
10B
Open validated release
Public benchmark datasets available for independent verification and reproducible computational testing.
PREMIUM
100B
Extended validation archive
Large-scale structured datasets prepared for research environments, benchmark comparison and archive storage.
SCALING
1000B+
Experimental scaling framework
Long-term computational observation model designed for future large-scale validation experiments.
VALIDATION RESULTS
Public benchmark comparison overview.
Public release covers validated benchmark outputs up to 10B. Extended 100B+ validation archives are reserved for premium datasets.
| LIMIT | MAYAN_ALFA | MR ONLY | PRIMESIEVE | π(N) | STATUS |
|---|---|---|---|---|---|
| 10M | 1.684354 s | 3.604905 s | 1.000000 s | 664579 | OK |
| 100M | 0.360000 s | 23.874431 s | 1.000000 s | 5761455 | OK |
| 1B | 1.793206 s | 244.145627 s | 9.000000 s | 50847534 | OK |
| 10B | 18.806417 s | NOT RUN | 94.000000 s | 455052511 | OK |
Measured values represent internal benchmark execution time under specific ARM64 testing conditions and should not be interpreted as universal performance metrics.
Results depend on hardware configuration, compiler optimization, execution methodology and dataset structure.
All benchmark outputs were cross-validated against independent computational methods and archived within the MAYAN_ALFA validation framework.
RELEASE STRUCTURE
Public research layer and extended archive system.
PUBLIC
Open validated releases
Public repository releases contain validated benchmark datasets, QA summaries, comparison reports and reproducible execution outputs up to 10B.
Designed for independent verification, long-term archival consistency and transparent computational observation.
PREMIUM
Extended validation archives
Large-scale 100B+ structured datasets, extended validation layers, archive-grade benchmark outputs and premium computational observation packages prepared for research and institutional environments.
OBSERVATION PRINCIPLES
A computational framework built on measured observation and reproducible validation.
01
Observation First
The framework records measured computational behavior before interpretation.
All conclusions are derived from validated execution outputs and structured comparison layers.
02
Validation Before Publication
Public releases are published only after cross-validation against independent computational systems, QA procedures and archive verification workflows.
03
Long-Term Archive Discipline
The project maintains reproducible datasets, benchmark summaries and structured archival layers designed for long-term computational research continuity.
DATASET FLOW
Structured validation pipeline for public and premium computational archives.
STEP 01
Computation
Execution outputs are generated under controlled ARM64 benchmark conditions with deterministic validation workflows and structured logging layers.
→
STEP 02
Cross Validation
Outputs are compared against independent computational systems including MR and primesieve validation frameworks.
→
STEP 03
Public Release
Validated datasets up to 10B are published as transparent public research layers with QA summaries and archive manifests.
→
STEP 04
Premium Archive
Extended 100B+ validation archives are maintained separately as premium computational research datasets and long-term archival packages.
VALIDATION SOURCES
Public repositories and archival validation layers.
MAYAN_ALFA maintains transparent public validation layers together with structured long-term archive systems prepared for reproducible computational observation and benchmark verification.
SOURCE 01
GitHub Repository
Public benchmark releases, validation summaries, source packages and computational observation layers published through the official GitHub research repository.
VIEW GITHUB
SOURCE 02
Zenodo Archive
Structured DOI-based archival releases synchronized with long-term preservation workflows for validated benchmark and research datasets.
VIEW ZENODO
SOURCE 03
Validation QA
Cross-validation reports, dataset integrity summaries, independent comparison outputs and benchmark QA documentation.
VIEW QA
RESEARCH TIMELINE
Long-term development and validation evolution of the MAYAN_ALFA framework.
PHASE 01
Core computational observation layer
Initial deterministic computational observation framework focused on structured execution behavior, validation logging and benchmark reproducibility under ARM64 environments.
PHASE 02
Independent cross-validation architecture
Integration of MR comparison layers, primesieve verification workflows, QA summaries and archive-oriented computational validation procedures.
PHASE 03
Public benchmark release framework
Structured public releases up to 10B including reproducible benchmark datasets, validation summaries and transparent publication methodology.
PHASE 04
Extended archival scaling system
Development of long-term premium validation archives, large-scale computational observation layers and institutional-grade archival preparation workflows.
VALIDATION MATRIX
Independent computational verification layers built for transparent benchmark discipline.
The MAYAN_ALFA framework combines structured execution, independent cross-validation systems and archival reproducibility layers to ensure measurable computational transparency.
LAYER 01
Execution Validation
Deterministic execution environments designed for stable runtime observation and reproducible computational measurements.
ARM64 execution monitoring
Controlled benchmark pipelines
Structured runtime summaries
LAYER 02
Cross-System Comparison
Outputs are independently compared across verification systems including MR frameworks and primesieve validation references.
MR validation workflows
primesieve comparison layers
QA mismatch control
LAYER 03
Public Dataset Publishing
Validated datasets are structured into reproducible public releases with transparent archival methodology and benchmark summaries.
10B public validation releases
CSV and QA archive structure
Long-term reproducibility
LAYER 04
Premium Research Archives
Extended computational observation archives prepared for large-scale validation environments and institutional-grade archival workflows.
100B+ structured archives
Extended benchmark storage
Research-grade dataset preparation
RESEARCH ACCESS
Structured computational observation for transparent benchmark research.
MAYAN_ALFA combines deterministic computational observation, independent validation systems and long-term archival discipline into a transparent benchmark research framework prepared for reproducible public and premium dataset environments.
