Why multi-GPU saturates at lower rates than single-GPU:

• Multi-GPU jobs require contiguous GPU allocations (e.g., 4 GPUs on same node)
• This creates fragmentation - even with free GPUs, jobs may not fit
• Single-GPU jobs can use any available GPU, packing more efficiently
• Expect the saturation spike around 3.5-4.0 jph for multi-GPU vs 5-6 jph for single-GPU

Your telemetry data is well-structured for visualization:

• Telemetry files: Per-round snapshots with GPU utilization (V100/P100/K80), job counts, and timing
• Completions files: Job lifecycle events with duration/JCT
• The 36:36:36 config means 108 total GPUs across 3 types, not a 36x36 grid

The simulation logs contain rich allocation data I can use:

• Job arrivals: job_type, scale_factor (GPU count), total_steps
• Micro-task scheduling: Which job → which worker_type → which worker_id(s)
• Worker IDs 72-107 are V100s, meaning IDs encode GPU type (0-35=K80, 36-71=P100, 72-107=V100)

Saturation comparison:

• Single-GPU (Fig 9): Saturates at ~5 jph, spike from 5-8 jph
• Multi-GPU Max-Min (Fig 10): Saturates at ~3 jph, spike from 3-4 jph
• Multi-GPU Finish-Time (Fig 11): Saturates at ~3 jph, spike from 3-4.4 jph

Why multi-GPU saturates earlier: Multi-GPU jobs need contiguous GPU allocations (e.g., 4 GPUs on same node). Even with free GPUs, jobs may not fit due to fragmentation - this is exactly what FGD’s fragmentation-aware scheduling addresses.

These tools were created during the ECOS solver failure investigation to visualize what was happening inside simulations.

1. extract_telemetry.py - Parses simulation logs to extract:

   • Per-round telemetry (utilization, active jobs, completion rate)
   • Job completion events with timestamps
   • Outputs JSON files for visualization

2. telemetry_viewer.html - Interactive browser-based dashboard:

   • Plots time series of any telemetry metric
   • Visualizes JCT over time, utilization curves
   • Compares multiple experiments side-by-side
   • Shows simulation phases (warm-up, measurement, saturation)

3. telemetry_data/ - Extracted data from sample experiments used during debugging

Key architecture improvements from Codex review:

• Fixed-size records are critical for efficient range requests - variable data must be in separate sections
• Web Workers for parsing prevent UI jank during data loading
• Modular architecture (DataSource/Decoder/Model/Renderer/Controller) makes the code testable and maintainable

Task 1.1 complete - Created foundation binary format module with:

• Magic number GPUVIZ01 for file format identification
• align_to_8() uses bitwise (x + 7) & ~7 - efficient 8-byte alignment without division
• This pattern ensures binary sections can be read directly into memory on aligned boundaries

Tasks 1.2b, 1.3, 1.4 complete - Binary format now supports:

• Job metadata: 16-byte records with uint32 job_id (handles >65K jobs)
• Round data: Fixed-size records with gpu_used[] (uint16) and allocations[] (uint32)
• The fixed-size formula 28 + 2*num_gpu_types + 4*total_gpus enables direct offset calculation for streaming

Binary format complete - All 6 binary format tasks done with 24 passing tests:

• Header: 256 bytes with section offsets for streaming
• Jobs: 16-byte fixed records (uint32 IDs handle >65K jobs)
• Rounds: Fixed-size with formula-based offset calculation
• Queue: Variable-length with index for random access
• File writer creates complete .viz.bin files

Phase 1 Complete - Python preprocessing pipeline:

• binary_format.py: Complete binary file format with 24 tests
• log_parser.py: Parses job arrivals, allocations, telemetry from simulation logs
• preprocess_viz.py: Converts simulation.log → .viz.bin for web visualization
• Total: 33 tests passing

Phase 2 Complete - All 7 JS modules implemented:

• DataSource: LRU cache + AbortController for range requests
• Decoder: Binary parsing matching Python format exactly (little-endian, 8-byte aligned)
• Worker: Offloads decoding to background thread
• Model: Observer pattern for state management
• Renderer: Canvas with dirty-region tracking (only repaints changed cells)
• Controller: Full playback, keyboard shortcuts, dual-simulation support
• Key design: Local files loaded via file.arrayBuffer() (range requests only for HTTP)

Architecture highlights:

• Binary format: Fixed-size round records enable O(1) offset calculation for any round
• Dirty-region rendering: Only repaints GPU cells that changed between frames
• Local file loading: Uses file.arrayBuffer() (not range requests) since file:// doesn’t support Range headers
• Queue index: Separate section with uint64 offsets allows random access to variable-length queue data
• Cross-language compatibility: Python struct format <IHBxIxxxx matches JS DataView byte-by-byte

Cross-language verification complete - Key validations:

• Header fields match exactly between Python struct.pack and JS DataView
• Round size formula Math.ceil((28 + 2*types + 4*gpus) / 8) * 8 matches Python’s (x + 7) & ~7
• uint32 values >65535 decode correctly in both languages (job_id=70000)
• Little-endian byte order consistent via struct.pack("<...") and DataView.getXxx(offset, true)

Architecture Summary - The visualizer uses a clean pipeline design:

1. Python pipeline: simulation.log → log_parser.py → preprocess_viz.py → .viz.bin
2. JS pipeline: .viz.bin → DataSource (fetch) → Decoder (parse) → Model (state) → Renderer (canvas)
3. Binary format: Fixed-size records enable O(1) round access via offset = base + round * size
4. Dirty tracking: Only changed GPU cells repaint, critical for 108-cell grids at 10fps

Both files contain 20,003 rounds and 1,268 jobs across 108 GPUs (36 K80 + 36 P100 + 36 V100). Each round record is 28 + 2*3 + 4*108 = 466 bytes, aligned to 472 bytes. The rounds section alone is 472 * 20003 ≈ 9MB. The queue section takes up most of the file (~50MB) since each round stores a variable-length list of queued job IDs, and with 1,268 jobs, many are queued at any given time.

The data looks correct: Round 0 has 58 jobs queued with GPUs already allocated (the allocations snapshot captures state between telemetry entries). By round 1, 57 jobs are running. By round 5, utilization is 93.5% with 100/108 GPUs in use. At round 100, utilization drops to 64.5% as jobs complete. The allocation count (90-100 GPUs) vs the total (108) shows the cluster isn’t fully packed, which is expected since some jobs need specific GPU types.

• Vertex AI vs Gemini API: Vertex AI is Google Cloud’s enterprise offering (what MoltBot uses for chat). The Gemini API (ai.google.dev) is the consumer API with generous free tier - 60 requests/minute for Gemini 1.5, includes image generation.
• nano-banana-pro: Uses Gemini 3 Pro Image model for AI image generation/editing with natural language prompts.

• Simpler setup: One API key (Gemini API) now powers both the chat LLM and image generation, instead of separate Vertex AI credentials.
• Cost: Gemini API has a generous free tier (60 RPM for most models). Image generation with Gemini 3 Pro Image is also included.
• nano-banana-pro: You can now ask MoltBot to “generate an image of…” or “edit this image to…” via WhatsApp!

• Gemini 3 Flash is optimized for speed while maintaining strong reasoning. It’s ideal for quick Q&A, everyday tasks, and when low latency matters.
• Cost difference: Flash models typically cost 5-10x less than Pro models per token.
• When to use Pro: Deep reasoning, complex coding, nuanced analysis. Flash handles most daily tasks well.

• Apple’s ecosystem is intentionally locked to macOS/iOS. These CLIs work by accessing local Apple databases and APIs that only exist on macOS.
• If Apple integration is important, a Mac mini makes a great always-on MoltBot server (~$600, low power, quiet).

• summarize uses yt-dlp under the hood for YouTube and can handle podcasts, articles, and local files
• nano-pdf uses Gemini for understanding PDF content and making edits - needs your GEMINI_API_KEY (already configured)
• video-frames leverages ffmpeg for precise frame extraction at specific timestamps

• macOS removed the built-in Screen Sharing support for RDP years ago
• Microsoft Remote Desktop is free and the most reliable option for Mac-to-Windows/Linux RDP
• The open rdp:// URL scheme only works if an app registers to handle it

• Microsoft rebranded “Remote Desktop” to “Windows App” in 2024
• App Store installs don’t require sudo, making them easier for automated workflows
• Homebrew casks for GUI apps often need elevated privileges for /Applications

• Snap apps store configs in ~/snap/<app>/<version>/.config/ instead of ~/.config/
• obsidian-cli expects the standard location, so the symlink bridges the gap
• This is a common pattern when mixing snap-installed apps with CLI tools

• Vertex AI vs Gemini API: Vertex AI is Google Cloud’s enterprise offering (what MoltBot uses for chat). The Gemini API (ai.google.dev) is the consumer API with generous free tier - 60 requests/minute for Gemini 1.5, includes image generation.
• nano-banana-pro: Uses Gemini 3 Pro Image model for AI image generation/editing with natural language prompts.

• Simpler setup: One API key (Gemini API) now powers both the chat LLM and image generation, instead of separate Vertex AI credentials.
• Cost: Gemini API has a generous free tier (60 RPM for most models). Image generation with Gemini 3 Pro Image is also included.
• nano-banana-pro: You can now ask MoltBot to “generate an image of…” or “edit this image to…” via WhatsApp!

• Gemini 3 Flash is optimized for speed while maintaining strong reasoning. It’s ideal for quick Q&A, everyday tasks, and when low latency matters.
• Cost difference: Flash models typically cost 5-10x less than Pro models per token.
• When to use Pro: Deep reasoning, complex coding, nuanced analysis. Flash handles most daily tasks well.

• Apple’s ecosystem is intentionally locked to macOS/iOS. These CLIs work by accessing local Apple databases and APIs that only exist on macOS.
• If Apple integration is important, a Mac mini makes a great always-on MoltBot server (~$600, low power, quiet).

• summarize uses yt-dlp under the hood for YouTube and can handle podcasts, articles, and local files
• nano-pdf uses Gemini for understanding PDF content and making edits - needs your GEMINI_API_KEY (already configured)
• video-frames leverages ffmpeg for precise frame extraction at specific timestamps

• macOS removed the built-in Screen Sharing support for RDP years ago
• Microsoft Remote Desktop is free and the most reliable option for Mac-to-Windows/Linux RDP
• The open rdp:// URL scheme only works if an app registers to handle it

• Microsoft rebranded “Remote Desktop” to “Windows App” in 2024
• App Store installs don’t require sudo, making them easier for automated workflows
• Homebrew casks for GUI apps often need elevated privileges for /Applications

• Snap apps store configs in ~/snap/<app>/<version>/.config/ instead of ~/.config/
• obsidian-cli expects the standard location, so the symlink bridges the gap
• This is a common pattern when mixing snap-installed apps with CLI tools