Extending openDLX: Custom Operators and Optimization Tips

Benchmarking openDLX: Performance Gains on Edge and Cloud Hardware

Summary

A benchmarking study for openDLX should measure inference throughput, latency, resource use, and efficiency across representative edge and cloud hardware, compare openDLX to baseline runtimes, and report reproducible results (commands, model versions, data, and metrics).

Recommended benchmark design

1. Goals
   • Measure throughput (FPS or queries/sec), p95/p99 latency, CPU/GPU/NPU utilization, memory, and energy (watts).
   • Compare openDLX vs. two baselines (e.g., vendor runtime and TensorRT/ONNX Runtime).
2. Workloads
   • Vision: object detection (YOLOv5/YOLOv8), classification (ResNet50), segmentation (DeepLabV3).
   • NLP: one transformer encoder (BERT-base) and one small LLM (7B) for generation latency.
   • Batch sizes: 1, 4, 16 (edge: 1 and 4).
3. Hardware targets
   • Edge: Raspberry Pi 4 (CPU), Google Coral / Edge TPU, Nvidia Jetson Xavier NX/TX2, Intel NPU (if available).
   • Cloud: NVIDIA A10/A100 GPU, CPU instance (Xeon), inference accelerator (AWS Inferentia/GPU equivalent).
4. Metrics & measurement
   • Throughput (warm and steady-state), latency distribution (p50/p90/p95/p99), 99% tail, model accuracy/quality preserved, CPU/GPU/NPU utilization, memory, power (W), efficiency (throughput/W).
   • Report raw logs, CSVs, and configuration files.
5. Methodology
   • Use identical model files and input batches across runtimes; convert once (ONNX/TensorRT) and verify numerically.
   • Warm-up period (e.g., 30s) then measure for a fixed interval (e.g., 120s) with multiple runs (3+) and report median.
   • Pin cores / set power profiles and report thermal behavior.
   • Isolate network effects for cloud (local filesystem or S3 with time-stamped latencies).
6. Bench scripts & reproducibility
   • Provide scripts to run experiments, convert models, and generate reports (CSV + HTML dashboard).
   • Seed RNGs and freeze non-deterministic ops where possible.
7. Comparisons & analysis
   • Show relative gains: % increase in throughput, % latency reduction, and fps/W improvements.
   • Break down where gains come from: operator fusion, quantization, batching, kernel optimizations, memory reuse.
8. Reporting
   • Per-model tables (throughput, p95, energy), per-hardware graphs (throughput vs. batch size), and efficiency plots (fps/W).
   • Include command lines, environment (OS, drivers, runtime versions), and model conversion steps.

Example concise results summary (format to publish)

• openDLX vs ONNX Runtime (Jetson Xavier NX, ResNet50, batch=1): +42% throughput, p95 latency −28%, power −10% (fps/W +58%).
• openDLX vs vendor runtime (Coral, MobileNetV2, batch=1): similar accuracy, throughput +15%, lower tail latency.
• Cloud (A10, BERT-base, batch=8): openDLX achieves 1.2–1.4× throughput vs optimized TensorRT pipeline depending on token length.

If you want, I can:

• draft runnable benchmark scripts for chosen models/hardware, or
• create a publication-ready benchmark report template with plots and tables.