Dynamic Compute Adaptation

Chloros 1.1.0 introduces intelligent hardware detection and automatic processing strategy selection. The processing engine adapts to your hardware — from a Jetson Nano to a multi-GPU workstation — without any manual configuration.

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How It Works

When Chloros starts, it automatically profiles your system:

1. Detects the operating system — Windows or Linux

2. Identifies CPU cores and total RAM

3. Detects GPU presence — NVIDIA CUDA capability, VRAM, model

4. Identifies Jetson model (if applicable) — via /proc/device-tree/model

5. Checks thermal sensors (Jetson) — for temperature-aware processing

6. Selects the optimal compute strategy — based on all detected hardware

7. Configures worker count, pipeline type, and memory allocation automatically

The result is cached so subsequent runs start faster. If hardware changes (e.g., a GPU is added), Chloros re-profiles on the next launch.

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Compute Strategies

Chloros selects one of three compute strategies based on your hardware:

Strategy	GPU Required	Workers	Pipeline	Best For
GPU_PARALLEL	Yes (12GB+ VRAM or 16GB+ shared)	3-4	fused_gpu	Desktop GPUs with 12GB+, Jetson Orin NX 16GB, AGX Orin
GPU_SINGLE	Yes (< 12GB VRAM)	1-3	tiled_gpu	Entry-level GPUs, Jetson Nano, Orin Nano
CPU_PARALLEL	No	cores - 1	cpu_fallback	Systems without NVIDIA GPU

Pipeline Types

• fused_gpu — Full GPU processing path. All debayer, correction, and index operations run on the GPU in a single fused pass. Highest throughput but requires more VRAM.

• tiled_gpu — Memory-efficient GPU path. Processes images in tiles to fit within limited GPU memory. Lower throughput but works on memory-constrained devices.

• cpu_fallback — CPU-only processing using multi-threaded parallelism. Used when no NVIDIA GPU is available.

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Platform-Specific Behavior

Platform	Strategy	Workers	Pipeline	Notes
Jetson Nano 8GB	GPU_SINGLE	1	tiled_gpu (serialized)	Memory-efficient mode, processes one image at a time
Jetson Orin NX 16GB	GPU_PARALLEL	3	fused_gpu (concurrent)	Recommended edge device — real parallel GPU processing
Jetson AGX Orin 64GB	GPU_PARALLEL	4	fused_gpu (concurrent)	Maximum edge performance
Desktop with 8GB GPU	GPU_SINGLE	3	tiled_gpu	Good desktop performance with memory-efficient tiles
Desktop with 12GB+ GPU	GPU_PARALLEL	3-4	fused_gpu	Optimal desktop performance
CPU-only system	CPU_PARALLEL	cores - 1	cpu_fallback	No GPU required, uses ThreadPool

Jetson unified memory: Jetson devices share GPU and CPU memory. A Jetson Orin NX 16GB reports ~15.3GB of VRAM, but this is the same physical RAM used by the OS and CPU processes. Chloros accounts for this when setting memory allocation thresholds.

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Dynamic GPU Memory Allocation

Chloros uses a 4-thread processing pipeline:

• Thread 1 (Detection) — Image loading, EXIF parsing, target detection

• Thread 2 (Calibration) — Reflectance calibration computation

• Thread 3 (Processing) — GPU debayer, vignette correction, index calculation

• Thread 4 (Export) — File writing, metadata embedding

As earlier pipeline threads complete their work (e.g., all images have been detected), their GPU memory allocation is released and redistributed to the remaining active threads. This means Thread 3 (the GPU-intensive stage) gets progressively more memory as the pipeline advances, improving throughput for the most compute-intensive work.

Allocation Stages

Stage	Active Threads	GPU Memory Distribution
Early	1, 2, 3, 4	Split across all threads
Mid-Early	2, 3, 4	Thread 1 memory redistributed
Mid-Late	3, 4	Threads 1+2 memory goes to 3+4
Late	3 or 4	Maximum memory for remaining thread

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Texture Aware Processing

The Texture Aware debayer method (Chloros+ only) uses significantly more GPU memory than the Standard method due to the AI/ML denoising model:

• Systems with < 7GB VRAM are forced into a synchronous processing loop for Texture Aware mode (one image at a time)

• Systems with 7GB+ VRAM can process Texture Aware concurrently, though at reduced worker count compared to Standard

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Thermal Management (Jetson)

Jetson devices have thermal constraints, especially in enclosed or airborne deployments. Chloros monitors GPU and CPU temperatures and automatically adjusts processing:

Temperature	Response
< 70°C	Normal operation — full speed
70°C (Warning)	Reduce batch size
80°C (Critical)	Aggressive throttling — lower concurrency and worker count
90°C (Shutdown)	Stop GPU processing entirely

Temperature monitoring uses tegrastats on Jetson platforms. On desktop systems with adequate cooling, thermal throttling is rarely triggered.

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Memory Pressure Handling

Chloros monitors system memory pressure during processing:

• Memory threshold: 85% utilization triggers conservative behavior

• OOM reduction: If an out-of-memory event occurs, allocation is reduced by 25% (0.75x multiplier)

• Pipeline fallback: Under severe memory pressure, the pipeline falls back from fused_gpu to tiled_gpu automatically

• Swap recommendations: On Jetson, Chloros warns you if swap space is insufficient for your dataset size

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Monitoring Compute Adaptation

CLI Status Output

When processing starts, the CLI displays the detected hardware profile:

Chloros CLI 1.1.0
Platform: Linux aarch64 (Jetson Orin NX 16GB)
Strategy: GPU_PARALLEL | Workers: 3 | Pipeline: fused_gpu
CUDA: Available | GPU Memory: 15.3 GB (shared)

System Diagnostics

Run chloros-cli selftest to see a full hardware profile and verify compute capabilities:

chloros-cli selftest

This checks CUDA availability, GPU memory, denoiser models, and backend connectivity.

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Next Steps

• Processing Pipeline — Understanding the 4-thread pipeline architecture

• NVIDIA Jetson Guide — Jetson-specific deployment and optimization

• CLI : Command Line — Full CLI reference