AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work presents Purlin, a scale-up communication framework that separates semantics, orchestration, and the datapath for distributed inference from existing collective implementations, and introduces a shared orchestration protocol, Stage, Notify, And Consume (SNAC), which derives coordination from these specificati...
Ave, an agentic framework that uses data-flow invariants as compile-time guardrails for kernel optimization and evaluates Ave on AMD MI300X across GEMM, flash attention, and MoE, which together account for up to 90% of GPU time in LLM inference.
The key insight in BOOST is to use kernel access patterns to make page allocation and runtime data management wave-aware, and applies modulo-based page placement that eliminates access-ratio variance.
PerfReasoning is introduced, a benchmark that evaluates LLMs both as direct performance reasoners and as generators of analytical performance-model code, exposing the gap between plausible architectural reasoning and reliable performance-model construction.
HiSparse is merged into upstream SGLang and evaluated across three sparse-attention families (DSA, NSA, and Quest) on H200, B200, and GH200 platforms: it improves peak generation throughput by up to 4.7x on long-context workloads while preserving comparable per-token latency and reducing time-to-first-token at high loa...
Valk, a performance analysis tool that combines data from multiple profilers, shows that when memory bandwidth is increased, kernels become compute bound, and makes three recommendations to fully utilize the GPUs' potential for relational workloads when the memory wall is removed.
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