#memory-management

#memory-management

PagedAttention optimizes memory usage in language model serving, significantly improving throughput while minimizing KV cache waste.

Anchor-based LLMs reduce memory use by 99% while improving inference speed by up to 3.5 times, enabling practical use on resource-constrained devices.

Memory sharing in PagedAttention enhances efficiency in LLMs, significantly reducing memory usage during sampling and decoding processes.

PagedAttention optimizes memory usage in LLM serving by managing key-value pairs in a non-contiguous manner.

PagedAttention and vLLM improve memory efficiency in LLMs by facilitating multiple output generation through shared prompt state management.

vLLM significantly improves throughput in LLM tasks by utilizing shared prefixes among different input prompts.

vLLM outperforms other models in handling higher request rates while maintaining low latencies through efficient memory management.

vLLM optimizes large language model serving through innovative memory management and GPU techniques.

vLLM optimizes memory management in LLM decoding by reserving only necessary resources, improving efficiency and performance.

The implementation of chatbots using LLMs hinges on effective memory management techniques to accommodate long conversation histories.

vLLM innovatively adapts virtual memory concepts for efficient management of KV caches in large language model services.

vLLM was tested using various Transformer-based large language models to evaluate its performance under load.

Transformer-based language models use autoregressive approaches for token sequence probability modeling.

Batching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.

Large Language Models often exceed single GPU limits, requiring advanced distributed execution techniques for memory management.

vLLM utilizes FCFS scheduling and an all-or-nothing eviction policy to effectively manage resources and prioritize fairness in request handling.

Efficient memory management and tensor contiguity are essential for optimizing performance in PyTorch, especially when handling large-scale datasets.

Current LLM serving systems inefficiently manage memory, resulting in significant waste due to fixed size allocations based on potential maximum sequence lengths.

The final Android 15 beta focuses on stable APIs for developer use and introduces significant behavior changes and new privacy features.

Android developers must prepare for a 16 KB memory page size upgrade to gain performance benefits of 5-10% in apps and games.

Optimizing buffer sizes and investigating memory usage is crucial for performance in Scala services to prevent Out of Memory errors.

Memory issues in a Scala service stemmed from underutilized buffers in Netty and inefficient memory management by the JSON library Jsoniter.

Memory issues in Scala service were traced to buffer mismanagement, leading to high memory usage and frequent GC runs.

Optimized buffer sizes and investigated long-lived object handling to address memory challenges.

Rust's ownership and borrowing principles help manage memory but memory leaks can still occur, necessitating careful management by developers.

Rust ensures memory safety through its unique ownership and borrowing model, mitigating risks present in traditional languages.

JEP 450 optimizes Java heap management by implementing compact object headers, which reduces header size and improves memory efficiency.

Java stack holds local variables and partial results, while Java heap is where memory for class instances and arrays is allocated.

Optimize string concatenation with StringBuilder or StringBuffer.

Use local variables for frequently accessed data.

Optimize loops by moving invariant calculations outside.

Use switch statements for better performance.

Spark's repartition() function is crucial for managing data skewness, optimizing performance, memory utilization, and downstream query efficiency.

Linked lists are efficient for fast addition of data without resizing the entire array, suitable for write-only data, and organizing data for sequential reads.

Data structures are key to efficient data storage and organization, crucial for memory management and optimizing software performance.

Symbols as WeakMap keys allow non-mutating attachment of data, preventing memory leaks.

Memory leaks can occur in various programming languages, from manually managed to automatic memory systems.

Common causes of memory leaks include unintentional object retention, circular references, unclosed resources, event listeners, caching without expiration, and poor memory profiling.