Overview
Real-time visibility into energy & carbon
Energy & Carbon Trend
Energy by Pipeline Stage
Insights
Platform Comparison
AMD vs Mac efficiency analysis
AMD Runners (x86)
Mac Runners (ARM)
Mac vs AMD Energy by Stage
Carbon Intensity by Platform
Carbon Impact
Software Carbon Intensity (SCI) tracking
Carbon Intensity Over Time
Carbon per Scenario
SCI Formula (ISO/IEC 21031)
- E: Energy consumed (kWh)
- I: Grid intensity (gCO₂eq/kWh)
- M: Embodied carbon (ignored)
Pipeline Runs
Full energy_metrics table data
Span Attribution
Request-level energy mapping
Energy by Span Type
Verification Proof (Methodology)
Phase 1 (1M items): sort_operation ~1.76 J
Phase 2 (5M items): sort_operation ~14.85 J
O(n log n) scaling proven
Infrastructure Tax remains flat.
Database Explorer
Raw SQLite tabular views
Methodology — Data Collection Explorer
Interactive walkthrough of what we collect and how. Pick a scenario, follow the pipeline gates, inspect the real artefacts.
Pick a scenario
Each CI/CD stage is profiled with the same baseline/workload/cooldown protocol. Five scenarios, two architectures.
Profiling timeline (animated)
10 s baseline → workload → 10 s cooldown. The profiler runs end-to-end at 100 ms sampling. Press play to see how data fills the window.
Baseline subtraction — see it happen
The raw power trace mixes the workload's own energy with the runner's idle background. Phase 3 strips the background out. Step through the four operations the analyst performs on every CSV.
Click 1. Raw trace to begin.
Synthetic trace for illustration — the analyst applies the same four operations to every real timechart.csv. Real runs at ResearchData/cpudata/ use 100 ms sampling and the live runner's idle level.
The 5 pipeline gates — click any gate to expand
Every run passes through these five gates in order. Each gate has a fail-fast safeguard that blocks bad data from entering the research database.
What we keep — click an artefact to see a real sample
Per scenario we persist 4–5 files. Together they let anyone reconstruct, audit, and re-process the run from scratch.
W3C trace context — the seam between power data and span data
Hover any part of the traceparent below to see its role.
Phase 1: Hardware Acquisition
AMD µProf: 100 ms polling, socket0-package-power (W)
Apple powermetrics: 100 ms polling, CPU+GPU+ANE combined
Nyquist constraint limits resolution of spikes < 50 ms — explicitly disclosed.
Phase 3: Signal Deconvolution
E_software = Σ max(0, P_sample − P_idle) × 0.1 s
Phase 4: Temporal Correlation
E_span += P_sample × overlap_ratio × 0.1 s
Conservation of energy: E_Total = E_Spans + E_Infrastructure
Validation Packet
Full methodology audit, validity threats, and reviewer checklist:
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methodology/data_collection_validation.md— full packet -
methodology/data_collection_validation_summary.md— 2–3 page summary -
methodology/bias_remediation_log.md— v3 audit history
Artifacts
Raw pipeline ZIP files available in MinIO
Database Schema & Relations
Loading schema…Relationship Explorer
Pick a pipeline run to see how one energy_metrics row links to its child span_metrics rows (joined on project_name · run_id · scenario).
Table Data (all rows)
Download Full Dataset (CSV)Download = one unified dataset (energy_metrics joined to span_metrics), not separate files.
| Project | Run ID | Scenario | Prefix (bucket area) | Size | Last Modified | Download |
|---|---|---|---|---|---|---|
| Loading... | ||||||
CSV Viewer
Drop any CSV to inspect it. Files are parsed in your browser only — nothing is uploaded.
Drag & drop a CSV file here
or click to browse
Agent POV
Live terminal output of the Analyst Agent
Awaiting connection...
Settings & About
Display Settings
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Timezone:
Database Stats
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Research Team
- Project: A Multi-Agent Framework for Request-Level Energy Attribution
- Member 1: M.M.N.D. Seneviratne (TG/2021/1017)
- Member 2: B.D.D. Devendra (TG/2021/1038)
- Supervisor: Dr. Chandana Pushpakumara