PrefixWall mitigates APC side channels in multi-tenant LLM systems via selective prefix isolation, delivering up to 70% higher cache reuse and 30% lower latency than full-isolation baselines.
Gonzalez, and Ion Stoica
5 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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verdicts
UNVERDICTED 5representative citing papers
Louver is a new index for LLM KV caches that guarantees zero false negatives for keys above a relevance threshold, runs faster than prior sparse and some dense attention methods, and integrates lightly into existing pipelines.
CSAttention precomputes fixed-size query-centric lookup tables in offline prefill to enable fast table-lookup decoding, delivering near-identical accuracy to full attention and up to 4.6x speedup at 95% sparsity for 32K-128K contexts.
NSA is a hardware-aligned sparse attention mechanism that enables end-to-end trainable long-context modeling by combining coarse token compression with fine-grained selection.
SOCKET replaces hard LSH bucket matches with soft probabilistic collision aggregation to enable efficient, high-quality token selection for sparse attention, matching or exceeding prior methods with up to 1.5x throughput gains.
citing papers explorer
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PrefixWall: Mitigating Prefix Caching Side Channels in Shared LLM Systems
PrefixWall mitigates APC side channels in multi-tenant LLM systems via selective prefix isolation, delivering up to 70% higher cache reuse and 30% lower latency than full-isolation baselines.
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Sparse Attention as a Range Searching Problem: Towards an Inference-Efficient Index for KV Cache
Louver is a new index for LLM KV caches that guarantees zero false negatives for keys above a relevance threshold, runs faster than prior sparse and some dense attention methods, and integrates lightly into existing pipelines.
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CSAttention: Centroid-Scoring Attention for Accelerating LLM Inference
CSAttention precomputes fixed-size query-centric lookup tables in offline prefill to enable fast table-lookup decoding, delivering near-identical accuracy to full attention and up to 4.6x speedup at 95% sparsity for 32K-128K contexts.
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Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention
NSA is a hardware-aligned sparse attention mechanism that enables end-to-end trainable long-context modeling by combining coarse token compression with fine-grained selection.
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SOCKET: SOft Collision Kernel EsTimator for Sparse Attention
SOCKET replaces hard LSH bucket matches with soft probabilistic collision aggregation to enable efficient, high-quality token selection for sparse attention, matching or exceeding prior methods with up to 1.5x throughput gains.