dorsal/arxiv
View SchemaMitigating GIL Bottlenecks in Edge AI Systems
| Authors | Mridankan Mandal, Smit Sanjay Shende |
|---|---|
| Categories | |
| ArXiv ID | 2601.10582vv1 |
| URL | https://arxiv.org/abs/2601.10582 |
| License | http://creativecommons.org/licenses/by-sa/4.0/ |
Abstract
Deploying Python based AI agents on resource-constrained edge devices presents a runtime optimization challenge: high thread counts are needed to mask I/O latency, yet Python's Global Interpreter Lock (GIL) serializes execution. We demonstrate that naive thread-pool scaling causes a "saturation cliff": >= 20% throughput degradation at overprovisioned thread counts (N >= 512) on edge-representative configurations. We present a lightweight profiling tool and adaptive runtime system using a Blocking Ratio metric (beta) that distinguishes genuine I/O wait from GIL contention. Our library-based solution achieves 96.5% of optimal performance without manual tuning, outperforming multiprocessing (limited by ~8x memory overhead on devices with 512 MB-2 GB RAM) and asyncio (blocked by CPU-bound phases). Evaluation across seven edge AI workload profiles, including real ML inference with ONNX Runtime MobileNetV2, demonstrates 93.9% average efficiency. Comparative experiments with Python 3.13t (free threading) show that while GIL elimination enables ~4x throughput on multi-core edge devices, the saturation cliff persists on single-core devices, validating our beta metric for both GIL and no-GIL environments. This provides practical optimization for edge AI systems.
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"abstract": "Deploying Python based AI agents on resource-constrained edge devices presents a runtime optimization challenge: high thread counts are needed to mask I/O latency, yet Python\u0027s Global Interpreter Lock (GIL) serializes execution. We demonstrate that naive thread-pool scaling causes a \"saturation cliff\": \u003e= 20% throughput degradation at overprovisioned thread counts (N \u003e= 512) on edge-representative configurations. We present a lightweight profiling tool and adaptive runtime system using a Blocking Ratio metric (beta) that distinguishes genuine I/O wait from GIL contention. Our library-based solution achieves 96.5% of optimal performance without manual tuning, outperforming multiprocessing (limited by ~8x memory overhead on devices with 512 MB-2 GB RAM) and asyncio (blocked by CPU-bound phases). Evaluation across seven edge AI workload profiles, including real ML inference with ONNX Runtime MobileNetV2, demonstrates 93.9% average efficiency. Comparative experiments with Python 3.13t (free threading) show that while GIL elimination enables ~4x throughput on multi-core edge devices, the saturation cliff persists on single-core devices, validating our beta metric for both GIL and no-GIL environments. This provides practical optimization for edge AI systems.",
"arxiv_id": "2601.10582",
"authors": [
"Mridankan Mandal",
"Smit Sanjay Shende"
],
"categories": [
"cs.DC",
"cs.OS",
"cs.PF"
],
"license": "http://creativecommons.org/licenses/by-sa/4.0/",
"title": "Mitigating GIL Bottlenecks in Edge AI Systems",
"url": "https://arxiv.org/abs/2601.10582",
"version": "v1"
},
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