Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.30990

SnakeCharmer: Orchestrating Utility, Timeliness, and Placement in GPU Prefetching

Yuhan Tang·Tiejun Li·Jianmin Zhang·An Hu·Guangyu Sun·Sheng Ma·Yiqun Lang·Hanqing Li·Siqing Fu
View original source ↗Read source document ↗Export metadata ↓

Abstract

GPUs rely on massive thread-level parallelism to tolerate long memory latency, yet they still suffer severe stalls when many warps simultaneously wait for data from the memory system. Address-prediction-based GPU prefetchers can mitigate such stalls, but accurate prediction alone does not guarantee performance improvement: a predicted prefetch becomes beneficial only if it is worth issuing, arrives at the right time, and is retained without harmful interference. Because these requirements interact, optimizing any single dimension in isolation can simply move the bottleneck to another. We capture these requirements with a predicted-prefetch-to-realized-benefit perspective and propose SnakeCharmer, a unified backend prefetch-control framework for address-prediction-based GPU prefetchers. SnakeCharmer separates address prediction from prefetch realization: the frontend predictor generates prefetch candidates, while the backend control path jointly controls utility, timeliness, and placement by determining whether each candidate should be issued, when it should be issued, and where the returned data should be retained. SnakeCharmer realizes this control path with zero-value-aware filtering for utility, hierarchical feedback-driven distance tuning for timeliness, and a register-assisted prefetch buffer for placement. We design and implement SnakeCharmer primarily on top of Snake, and adapt the same backend-control principles to APOGEE to demonstrate that SnakeCharmer's benefits are not limited to a single frontend predictor. On an A100 GPU model, SnakeCharmer improves performance/energy efficiency by 12.8%/20.8% over Snake and by 1.7%/3.2% over APOGEE, with modest hardware overhead.

Explore related subjects

cs.AR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuhan Tang, Tiejun Li, Jianmin Zhang, An Hu, Guangyu Sun, Sheng Ma, Yiqun Lang, Hanqing Li, Siqing Fu. 2026-10-22. SnakeCharmer: Orchestrating Utility, Timeliness, and Placement in GPU Prefetching. https://arxiv.org/abs/2610.30990

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

ARES: Adaptive Reasoning-Effort Steering for PPA- and Cost-Aware RTL Optimization with LLM Agents

Large language model (LLM) agents optimize the power, performance, and area (PPA) of register-transfer-level (RTL) designs by iterating over edits, synthesis, and PPA analysis, paying a dollar cost for every LLM call. Prior agents report the quality reached without its normalized cost, attribute that quality to an engineered cross-design memory, and hold the reasoning effort of every call fixed. We propose Ares with three corresponding innovations. (1) We introduce a normalized dollar cost per LLM call reported alongside the figure of merit (FoM), enabling fair comparison across effort levels and optimizers. (2) Using this accounting, we find the construction of the long-term memory matters little. An engineered memory brings no dependable gain over a plain concatenation of the same experience. (3) We instead adapt the per-call reasoning effort by escalating to deeper reasoning only once progress at a lower effort stalls, via a patience counter fit on 21 training designs, allocating reasoning where it pays rather than uniformly across all iterations. On three test designs unseen during training, the effort policy lowers the FoM by 22-28% with Opus 4.6 and by 18-34% with GPT-5.5, where the best fixed effort reaches 20-24% and 19-29%, respectively, at equal normalized cost. Ares closes on average 91% of the gap from an LLM-drafted multiply-accumulate unit to its highly hand-optimized counterpart. It also reaches a 5% lower FoM than state-of-the-art Dr. RTL at only 35% of its cost.

cs.AR↗
arXiv2026

Hard and Soft Real-Time DNN Inference Scheduling on Heterogeneous Multi-Accelerator Systems via Reinforcement Learning

We present a scheduling framework for heterogeneous multi-accelerator systems executing both hard real-time and soft real-time QoS-aware DNN inference workloads. The framework combines an online reinforcement learning (RL) scheduler with a deterministic runtime safety layer. The RL scheduler dynamically generates candidate schedules by jointly selecting sub-job priorities and accelerator assignments, while runtime certification guarantees that all HRT deadlines remain satisfied independently of the learned policy. To enable runtime certification in heterogeneous precedence-constrained systems, we introduce a schedulability-preserving prefix replacement criterion based on machine-wise cuts. The proposed criterion guarantees that runtime-generated schedule prefixes preserve the existence of a feasible continuation without requiring complete finish-time dominance with respect to a feasible reference schedule. Experimental results on heterogeneous DNN accelerator platforms show that the proposed framework preserves HRT schedulability while improving QoS-aware deadline satisfaction, client-level service quality, and accelerator utilization compared to state-of-the-art scheduling approaches.

cs.AR↗
arXiv2026

HDLouvain: High-Throughput Incremental Clustering for Hyperdimensional Data

Modern shotgun proteomics generates tens of thousands of tandem mass spectra per hour, producing hundreds of gigabytes of data that strain conventional clustering pipelines. DBSCAN and hierarchical agglomerative clustering (HAC) offer flexible similarity search, but DBSCAN is unstable across varying data densities, HAC costs $O(n^2)$, and both yield poor cluster quality. Graph-based methods such as Louvain produce high-quality clusters but scale poorly on streaming data. We observe that Louvain's poor scalability can be resolved without sacrificing its quality, and present HDLouvain, which combines Louvain community detection with the bit-parallel similarity computation of hyperdimensional computing (HDC). HDLouvain partitions a bootstrap fraction of the stream into precursor-mass buckets, applies Louvain to each bucket's kNN similarity graph, and condenses each community into a single binary hypervector by bitwise-majority bundling rather than iterative centroid recomputation. New spectra are assigned by a GPU-accelerated Hamming distance lookup against representatives in their mass bucket, or seed a new cluster when no match meets a similarity threshold $τ$, so the partition grows incrementally without full re-clustering. On five PRIDE datasets (0.99M to 21.13M spectra), sweeping the bootstrap fraction $α$ from 0.2 to 0.9 exposes an explicit, monotonic throughput-fidelity trade-off. On PXD003258, speedup ranges from 4.5x at $α=0.3$ to 1.65x at $α=0.7$. Modularity rises from 0.75-0.76 at $α=0.2$ to 0.95-0.97 at $α=0.9$, never more than 8% below full static recomputation. The Louvain stage achieves 0.86-0.93 normalized mutual information (NMI) on LFR benchmark networks with 30k to 2M nodes, whereas DBSCAN and HAC degrade to 0.57.

cs.AR↗