Optimizing Storage and Memory Management for Efficient Language Processing

The field of language processing is moving towards optimizing storage and memory management to improve efficiency and reduce costs. Recent developments focus on eliminating unnecessary writes, minimizing wear, and maintaining parallelism in storage systems. Additionally, there is a growing interest in optimizing key-value cache management for large language models, balancing space, time, accuracy, and positional fidelity. Noteworthy papers include:

  • SilentZNS, which proposes a new zone mapping and management approach to address limitations in current ZNS implementations.
  • BudgetMem, which learns selective memory policies for cost-efficient long-context processing in language models.
  • DynaKV, which enables accurate and efficient long-sequence LLM decoding on smartphones through adaptive KVCache management.
  • FlashMap, which presents a high-performance key-value store optimized for Flash-based solid-state drives.

Sources

Eliminating the Hidden Cost of Zone Management in ZNS SSDs

Stateful KV Cache Management for LLMs: Balancing Space, Time, Accuracy, and Positional Fidelity

BudgetMem: Learning Selective Memory Policies for Cost-Efficient Long-Context Processing in Language Models

DynaKV: Enabling Accurate and Efficient Long-Sequence LLM Decoding on Smartphones

FlashMap: A Flash Optimized Key-Value Store

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