Feather uses reinforcement learning and a Chunked Hash Tree to balance batch size against prefix homogeneity in LLM inference, delivering 2-10x higher throughput than existing schedulers.
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InProceedings of the ACM SIGOPS 31st Symposium on Operating Systems Principles (SOSP ’25)
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Four attribution methods applied to over one million Polygon blocks show that most atomic arbitrage MEV opportunities trace to single source transactions from a small set of protocols.
Proxics introduces lightweight virtual processors and low-latency communication channels as portable OS abstractions for programming near-data processing accelerators, demonstrated on real hardware for memory-intensive workloads.
Strait cuts high-priority deadline violations in ML inference serving by 1-11 percentage points through contention modeling and priority scheduling under high GPU load.
BloomBee is a distributed LLM inference system that achieves up to 1.76x higher throughput and 43.2% lower latency than prior decentralized systems by optimizing communication across multiple dimensions in low-bandwidth internet settings.
citing papers explorer
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Requests of a Feather Must Flock Together: Batch Size vs. Prefix Homogeneity in LLM Inference
Feather uses reinforcement learning and a Chunked Hash Tree to balance batch size against prefix homogeneity in LLM inference, delivering 2-10x higher throughput than existing schedulers.
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The Origins of MEV: Systematic Attribution of Arbitrage Opportunity Creation at Scale
Four attribution methods applied to over one million Polygon blocks show that most atomic arbitrage MEV opportunities trace to single source transactions from a small set of protocols.
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Proxics: an efficient programming model for far memory accelerators
Proxics introduces lightweight virtual processors and low-latency communication channels as portable OS abstractions for programming near-data processing accelerators, demonstrated on real hardware for memory-intensive workloads.
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Strait: Perceiving Priority and Interference in ML Inference Serving
Strait cuts high-priority deadline violations in ML inference serving by 1-11 percentage points through contention modeling and priority scheduling under high GPU load.
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Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization
BloomBee is a distributed LLM inference system that achieves up to 1.76x higher throughput and 43.2% lower latency than prior decentralized systems by optimizing communication across multiple dimensions in low-bandwidth internet settings.