LoCoMAP：长上下文记忆访问预测让图分析预取更稳
Topology Survives Shuffling: Robust Long-Context Memory Access Prediction for Graph Analytics
搞系统或图计算的朋友可以看看，这篇把预取器的记忆访问预测历史拉到 128、跨度拉到 512，节点打乱都不用重训，CC 图上还留 95.4% 的 F1。
arXiv 论文提出 LoCoMAP，一个用于图分析内存访问预测的长上下文表示学习框架，观察历史与预测跨度可独立控制。在 Connected Components、PageRank、SSSP 三种工作负载和六类合成图上，预测跨度从 8 扩到 512 使 F1 提升 88.3%，历史从 8 扩到 128 提升 7.8%。节点打乱实验中，长上下文的 CC 模型不重训仍保留 95.4% 的原始 F1，在 SBM 图上 F1 提升达 2.06 倍。PageRank 在完全打乱下保留 57.6% F1，平均比短上下文高 11.0%。结果显示长上下文预测器无需重训即可跨图结构泛化，可支撑通用图预取器。
Topology Survives Shuffling: Robust Long-Context Memory Access Prediction for Graph Analytics
Graph analytics generates irregular, topology-dependent memory-access streams via indirect traversals, limiting data prefetching. Accurate prediction enables timely prefetches that hide memory latency. However, ML predictors couple short histories with fixed horizons, fitting the training trace's node layout rather than learning transferable graph patterns. We propose LoCoMAP, a long-context representation-learning framework for memory-access prediction with independently controlled observation history and prediction horizon. We evaluate Connected Components (CC), PageRank (PR), and Single-Source Shortest Paths (SSSP) on six synthetic graph families, with Erdos-Renyi (ER) and Grid as random and deterministic controls. We test two interventions: same-family regeneration changes connectivity, while node shuffling disrupts layout but preserves connectivity. Across 18 graph-workload pairs, extending history from 8 to 128 accesses improves F1 by 7.8%, while extending prediction horizon from 8 to 512 improves F1 by 88.3%. For CC, regeneration is less disruptive than shuffling: without retraining, long context retains 95.4% of original F1 and outperforms short context in six families. Under full shuffle, long context improves CC F1 by 33.6% across ER, Barabasi-Albert, Copying Model, and Stochastic Block Model (SBM), with a 2.06x gain on SBM, but not on Watts-Strogatz or Grid, where near-zero delta mass shifts into heavy tails beyond the predictor's range. Long context outperforms short context on six real-world CC graphs. At full shuffle, long-context PR retains 57.6% of original F1 and averages 11.0% above short context. Both SSSP models retain about 30% of original F1 under full shuffle as distance updates access vertex data in a new order. Within these bounds, long-context predictors are robust across related graphs and layouts without retraining, enabling a generalizable graph prefetcher.