Scalable persistent memory (PM) has opened up new opportunities for building indexes that
operate and persist data directly on the memory bus, potentially enabling instant recovery …

Persistent memory (PM) based indexing techniques have been proposed to build fast yet
persistent indexes that sit on the memory bus. Over the past decade, numerous techniques …

Data pipelines (ie, converting raw data to features) are critical for machine learning (ML)
models, yet their development and management is time-consuming. Feature stores have …

Multi-terabyte large memory systems are often characterized by more than two memory tiers
with different latency and bandwidth. Multi-tiered large memory systems call for rethinking of …

Skip list (skiplist) is a competitive index structure that offers superior concurrency and
excellent performance but with high memory overhead and low access locality. Emerging …

Federated learning (FL), which enables collaborative learning without revealing raw data, is
an emerging topic in privacy-preserving machine learning. Based on our experiences in …

In recent years, at ByteDance, we have started seeing more and more business scenarios
that require performing real-time data serving besides complex Ad Hoc analysis over large …

As the use of online AI inference services rapidly expands in various applications (eg, fraud
detection in banking, product recommendation in e-commerce), real-time feature extraction …

Skiplist as an in-memory index performs pretty well on rapid insertions because there are no
rotations or reallocations for rebalancing. The emerging non-volatile memory (NVM) …

Crash consistency overhead is a long-standing barrier to the adoption of byte-addressable
persistent memory in practice. Despite continuous progress, persistent transactions for crash …