Elastic Gang: Per-Token Membership Change for a Hard-Barriered LLM Inference Gang Co-Scheduled with OS Processes
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Abstract (arXiv)
On-device LLM decoding is a hard-barriered CPU-SIMD computation that wants every core for milliseconds per token, while the rest of the OS wants those same cores continuously. A barriered gang cannot simply be dropped into a preemptive scheduler: an unannounced departure deadlocks a barrier, and an unannounced arrival silently corrupts logits. I present the elastic gang of Anima OS, a bare-metal x86-64 Rust kernel in which the inference gang is a first-class schedulable entity whose core membership may change between any two tokens. The core mechanism is an ACK-latched epoch protocol that never waits on a named core: a seqlock-style generation-tagged latch composed with RCU/epoch-style membership consent, so each token's participant set is the intersection of the cores the gang requested and the cores that acked the current epoch. An un-acked core is outside this token and joins at most one token later. Displaced general processes migrate and keep running; cores return to them the moment a generation ends. On a real AMD Zen 5 machine (8C/16T), inference output is bit-exact under verified per-token membership change on both a 135M and a 7B model, the property that makes elasticity safe in a kernel whose safety gate reads logits. Against fair static core partitions, elastic membership Pareto-dominates: at intermediate inference duty cycles it delivers 1.75x (25%), 1.52x (50%), and 1.28x (75%) the general throughput of a static 8-core split at equal or better inference throughput, recovers all eight stranded cores when inference is idle, and converges to the split at saturation. Returning a lent core costs 0.22 us (p50); acquiring a busy, tenant-occupied core costs one scheduling quantum (~16 ms): a running tenant is never preempted mid-slice. Decode throughput saturates at gang width 8, so ceding cores past the knee is nearly free: elasticity auto-sizes the gang online.
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References · 6
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
- 2024Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Servecited by 507
- 2024AIOS: LLM Agent Operating Systemcited by 94
- 2024MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Servingcited by 49
- 2026ProbeLogits: Kernel-Level LLM Inference Primitives for AI-Native Operating Systemscited by 2
- 2026Governed MCP: Kernel-Level Tool Governance for AI Agents via Logit-Based Safety Primitivescited by 1
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