arXiv subjects

Yicong Luo

Publications and source records attributed to Yicong Luo.

3 recordsLinked to original sources

arXiv2026

A Simple Optimistic B-Skiplist Algorithm

We present the first optimistic concurrent B-skiplist, built on optimistic lock coupling (OLC). OLC replaces the multi-cache-line RW lock with a compact, single-cache-line lock: readers validate a version number without writing to shared memory, avoiding cache invalidations and retrying only on a detected conflict. Although OLC was thought poorly suited to skiplists, where a single per-element lock may span structurally unrelated levels, the B-skiplist's blocked layout places each level (and associated lock) in its own node. We exploit the B-skiplist's upfront knowledge of promotion height with a hybrid top-down scheme that traverses upper levels optimistically and locks only from the promotion level down, so most inserts never dirty a cache line. On 128 threads, the proposed optimistic B-skiplist achieves $1.2-10\times$ higher throughput over state-of-the-art concurrent skiplist implementations. Furthermore, the optimistic B-skiplist achieves $0.8-4.3\times$ the throughput compared to high-performance optimistic B+-tree implementations based on RW locks and OLC. Finally, the optimistic B-skiplist achieves up to $13\times$ lower latency than concurrent skiplists and tree-based indices, and is within $1.6\times$ of the latency of the OLC-based B+-tree.

cs.DS↗

arXiv2025

Bridging Cache-Friendliness and Concurrency: A Locality-Optimized In-Memory B-Skiplist

Skiplists are widely used for in-memory indexing in many key-value stores, such as RocksDB and LevelDB, due to their ease of implementation and simple concurrency control mechanisms. However, traditional skiplists suffer from poor cache locality, as they store only a single element per node, leaving performance on the table. Minimizing last-level cache misses is key to maximizing in-memory index performance, making high cache locality essential. In this paper, we present a practical concurrent B-skiplist that enhances cache locality and performance while preserving the simplicity of traditional skiplist structures and concurrency control schemes. Our key contributions include a top-down, single-pass insertion algorithm for B-skiplists and a corresponding simple and efficient top-down concurrency control scheme. On 128 threads, the proposed concurrent B-skiplist achieves between 2x-9x higher throughput compared to state-of-the-art concurrent skiplist implementations, including Facebook's concurrent skiplist from Folly and the Java ConcurrentSkipListMap. Furthermore, we find that the B-skiplist achieves competitive (0.9x-1.7x) throughput on point workloads compared to state-of-the-art cache-optimized tree-based indices (e.g., Masstree). For a more complete picture of the performance, we also measure the latency of skiplist and tree-based indices and find that the B-skiplist achieves between 3.5x-103x lower 99% latency compared to other concurrent skiplists and between 0.85x-64x lower 99% latency compared to tree-based indices on point workloads with inserts.

cs.DC↗

arXiv2024

Model Compression and Efficient Inference for Large Language Models: A Survey

Transformer based large language models have achieved tremendous success. However, the significant memory and computational costs incurred during the inference process make it challenging to deploy large models on resource-constrained devices. In this paper, we investigate compression and efficient inference methods for large language models from an algorithmic perspective. Regarding taxonomy, similar to smaller models, compression and acceleration algorithms for large language models can still be categorized into quantization, pruning, distillation, compact architecture design, dynamic networks. However, Large language models have two prominent characteristics compared to smaller models: (1) Most of compression algorithms require finetuning or even retraining the model after compression. The most notable aspect of large models is the very high cost associated with model finetuning or training. Therefore, many algorithms for large models, such as quantization and pruning, start to explore tuning-free algorithms. (2) Large models emphasize versatility and generalization rather than performance on a single task. Hence, many algorithms, such as knowledge distillation, focus on how to preserving their versatility and generalization after compression. Since these two characteristics were not very pronounced in early large models, we further distinguish large language models into medium models and ``real'' large models. Additionally, we also provide an introduction to some mature frameworks for efficient inference of large models, which can support basic compression or acceleration algorithms, greatly facilitating model deployment for users.

cs.CL↗