Deterministic Rematerialization: Convergent Evolution in Cloud Kernels and Edge Swarms

Abstract

As AI scales in both parameter count (Large Language Models) and distribution (IoT Swarms), data movement—not arithmetic—has become the dominant cost. This paper identifies a structural isomorphism between two families of solutions that emerged independently: fused, IO-aware GPU kernels for cloud training (e.g., FlashAttention) and silent consensus for decentralized edge learning (QRES).

We argue that both converge on the same strategy—Deterministic Rematerialization—in which intermediate state is intentionally discarded and later recomputed on demand, trading surplus compute for orders-of-magnitude reductions in IO. We validate this thesis with a 10,000-node simulation demonstrating constant memory growth ($O(1)$) with respect to the number of peers, supporting the claim that determinism is not merely a constraint but an enabling technology for state-elision at scale.

Key Verified Results (v18.0):

• Scalability: 10,000 concurrent nodes simulated on a single 2-vCPU Azure instance.

• Memory: < 0.70 KB RAM overhead per node (amortized) at scale.

• Reliability: 100% consensus success rate using "Silent Consensus" protocol.

• Compression: 31.8x compression ratio on telemetry data vs Zstd (2.1x).