Sparse Matrix-Sparse Vector Multiplication (SpMSpV) is a core primitive in graph traversal, sparse linear algebra, and sparse model inference. Its input vector is often dynamically sparse, so the best GPU execution path depends on both global sparsity and the local vector-block distribution. Existing GPU SpMSpV methods often bind storage layouts, push/pull traversal, and kernels together, making fine-grained adaptation difficult without extra storage or scheduling overhead. This paper presents DB-SpMSpV, a dual-view blocked SpMSpV framework for dynamic GPU workloads. DB-SpMSpV partitions the matrix into fixed-size 2D blocks, maintains block-level CSR/CSC views at the high level, and reuses a single low-level block payload to support both row-driven pull and column-driven push. At runtime, it selects the global traversal path based on input block sparsity, chooses block microkernels from the local matrix/vector block structure, and uses load balancing, asynchronous prefetching, and hierarchical writeback to reduce irregular memory accesses, writeback conflicts, and load imbalance. We further integrate the framework into DB-BFS and DB-Decoding. We evaluate DB-SpMSpV on NVIDIA A100 and RTX 4090 using SuiteSparse matrices, symmetric graphs, and three open-source LLMs. Across input sparsities, DB-SpMSpV achieves average speedups of 5.48$\times$--64.34$\times$ over cuSPARSE and 2.36$\times$--14.01$\times$ over TileSpMSpV on A100, with similar gains on RTX 4090. DB-BFS further improves end-to-end graph traversal by 2.66$\times$ over TileBFS on A100 and 3.60$\times$ on RTX 4090 on average, while DB-Decoding accelerates single-token linear layers by up to 4.50$\times$.
Xing Cong, Chenhao Xie, Rui Wang et al.· 0 citations
Large language models (LLMs) are increasingly used to interpret operational evidence and assist incident response in cloud-native microservice systems. However, recovery-oriented use cases require more than identifying a root cause. After observing symptoms and diagnosing a fault, an operator or agent must translate the diagnosis into a concrete recovery action, apply it to an admissible target, and verify that service health has been restored. Existing RCA and log-analysis evaluations are well-suited to diagnosis, but they do not characterize this subsequent action decision. This paper presents R2Act, a recovery-action evaluation framework for post-diagnosis incident response. R2Act defines an incident schema, quality gate, action-space representation, recovery-validity metrics, offline evaluator, and live-replay protocol. We instantiate the framework as a benchmark dataset of 302 quality-audited Kubernetes incidents from \system. Each incident provides synchronized multi-modal observations, root-cause labels, an incident-specific action space, and annotated valid and invalid recovery plans. We evaluate heuristic, supervised, RCA-oriented, deep log, and LLM-based methods. The strongest RAG-based LLMs reach 91.4\%--99.7\% root-cause service accuracy, yet their recovery validity remains only 36.8\%--60.3\%. Even when both the root-cause service and fault type are correct, recovery-oriented methods still choose invalid actions for 39.5\%--62.0\% of correctly diagnosed incidents. Overall, this work reveals that many recovery failures arise not from missing diagnostic knowledge, but from the difficulty of translating diagnostic evidence into valid recovery actions and admissible targets. This work provides a reproducible, simplified starting point for research and evaluation.
Jiaxing Qi, Zhongzhi Luan, Hongyu Zhang et al.· 0 citations