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cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Reducing Solid-State Drive Read Latency by Optimizing Rea...
Jisung Park, Myungsuk Kim, Myoungjun Chun, Lois Orosa, Jihong Ki · 2020-12-23 · via cs.DC updates on arXiv.org

3D NAND flash memory with advanced multi-level cell techniques provides high storage density, but suffers from significant performance degradation due to a large number of read-retry operations. Although the read-retry mechanism is essential to ensuring the reliability of modern NAND flash memory, it can significantly increase the read latency of an SSD by introducing multiple retry steps that read the target page again with adjusted read-reference voltage values. Through a detailed analysis of the read mechanism and rigorous characterization of 160 real 3D NAND flash memory chips, we find new opportunities to reduce the read-retry latency by exploiting two advanced features widely adopted in modern NAND flash-based SSDs: 1) the CACHE READ command and 2) strong ECC engine. First, we can reduce the read-retry latency using the advanced CACHE READ command that allows a NAND flash chip to perform consecutive reads in a pipelined manner. Second, there exists a large ECC-capability margin in the final retry step that can be used for reducing the chip-level read latency. Based on our new findings, we develop two new techniques that effectively reduce the read-retry latency: 1) Pipelined Read-Retry (PR$^2$) and 2) Adaptive Read-Retry (AR$^2$). PR$^2$ reduces the latency of a read-retry operation by pipelining consecutive retry steps using the CACHE READ command. AR$^2$ shortens the latency of each retry step by dynamically reducing the chip-level read latency depending on the current operating conditions that determine the ECC-capability margin. Our evaluation using twelve real-world workloads shows that our proposal improves SSD response time by up to 31.5% (17% on average) over a state-of-the-art baseline with only small changes to the SSD controller.