arXiv subjects

Jiehui Li

Publications and source records attributed to Jiehui Li.

5 recordsLinked to original sources

arXiv2026

ByteX: A Unified AI Search Engine at ByteDance

Since 2016, ByteX has been the foundation of ByteDance's search infrastructure, scaling to more than 7,000 clusters and 300 PB of indexed data. Driven by the demands of AI workloads, ByteX has evolved from a text search engine into a unified AI search system supporting vector retrieval, lexical matching, and predicate filtering. Its largest deployment indexes nearly one trillion high-dimensional vectors. This scale exposes two central bottlenecks in AI-era retrieval: memory-intensive graph-index construction under sustained ingestion, and the prohibitive cost of keeping vector indexes entirely in memory. ByteX addresses these bottlenecks with two techniques. First, it introduces a quantization-aware vector kernel based on SymRaBitQ, a new symmetric quantization scheme with tight theoretical guarantees that allows index construction to run directly in the quantized space accurately and efficiently without retaining a copy of full-precision vectors. Second, it provides a hybrid storage engine that supports memory-resident, hybrid, and SSD-resident deployments, with fine-grained record-level caching to trade memory for latency under operational control. On large-scale benchmarks, ByteX improves throughput by up to 3x, reduces indexing memory by 80%, and lowers operating cost by 86% compared with prior systems, while supporting trillion-vector scale, write-heavy or latency-sensitive workloads in production.

cs.DB↗

arXiv2026

Machine learning inference of fission yields from gamma spectroscopy for very low-yield nuclear test verification

Very low-yield nuclear tests pose a major verification challenge for the zero-yield standard of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The zero-yield standard prohibits any explosive experiment that produces a self-sustaining fission chain reaction while allowing subcritical experiments. Previous research shows that on-site gamma spectroscopy of post-test debris provides useful insight into the criticality level, although it remains heavily dependent on knowledge of certain experimental settings. Here, we adopt a new approach whereby machine learning models are trained on simulated gamma spectroscopy data to infer the fission yield of a nuclear very low-yield test. Using high-fidelity 3D Monte Carlo particle transport simulations, we generated gamma spectra measured outside containment vessels after very low-yield tests for 66 million representative scenarios. From these spectra, we extracted 82 fission-product-to-plutonium-239 peak ratios, then trained ML models for two tasks: (1) binary classification of whether a test exceeded a chosen yield threshold, and (2) regression to estimate the actual yield. We find that XGBoost performs best on the classification task across the most policy-relevant yield range. The classifier achieves high accuracy even for yields near the chosen threshold (e.g., >95% for yields +-100 g around a threshold at 1 kg TNT), and the regressor presents a mean absolute relative error of 12.4% for measurements taken a month to a year after the test. These results demonstrate that using machine learning to infer the yield of a past very low-yield nuclear test from gamma spectroscopy data is feasible and accurate. This approach can support efforts to establish a robust verification protocol for the zero-yield standard and could pave the way for a future yield threshold-based verification regime that is both technically feasible and politically viable.

physics.ins-det↗

arXiv2026

Optimizing SSD-Resident Graph Indexing for High-Throughput Vector Search

Graph-based approximate nearest neighbor search (ANNS) methods (e.g., HNSW) have become the de facto state of the art for their high precision and low latency. To scale beyond main memory, recent out-of-memory ANNS systems leverage SSDs to store large vector indexes. However, they still suffer from severe CPU underutilization and read amplification (i.e., storage stalls) caused by limited access locality during graph traversal. We present VeloANN, which mitigates storage stalls through a locality-aware data layout and a coroutine-based asynchronous runtime. VeloANN utilizes hierarchical compression and affinity-based data placement scheme to co-locate related vectors within the same page, effectively reducing fragmentation and over-fetching. We further design a record-level buffer pool, where each record groups the neighbors of a vector; by persistently retaining hot records in memory, it eliminates excessive page swapping under constrained memory budgets. To minimize CPU scheduling overheads during disk I/O interruptions, VeloANN employs a coroutine-based asynchronous runtime for lightweight task scheduling. On top of this, it incorporates asynchronous prefetching and a beam-aware search strategy to prioritize cached data, ultimately improving overall search efficiency. Extensive experiments show that VeloANN outperforms state-of-the-art disk-based ANN systems by up to 5.8x in throughput and 3.25x in latency reduction, while achieving 0.92x the throughput of in-memory systems using only 10% of their memory footprint.

cs.DB↗

arXiv2024

TURNIP: A "Nondeterministic" GPU Runtime with CPU RAM Offload

An obvious way to alleviate memory difficulties in GPU-based AI computing is via CPU offload, where data are moved between GPU and CPU RAM, so inexpensive CPU RAM is used to increase the amount of storage available. While CPU offload is an obvious idea, it can greatly slow down a computation, due to the relatively slow transfer rate between CPU RAM and GPU RAM. Thus, any system for CPU offload needs to ensure that when such a transfer needs to happen, no computation is blocked waiting for the transfer to finish. One of the key challenges when using CPU offload is that memory transfers introduce nondeterminacy into the system: it is not possible to know before runtime when the transfers will finish, and hence what is the best order of operations to run to ensure there is no blocking. In this paper, we describe TURNIP, which is a system for running AI computations using CPU offload. The key innovation in TURNIP is the compilation of the AI computation into a dependency graph that gives the TURNIP runtime freedom to run operations such as GPU kernel calls in many different orders; at runtime, TURNIP chooses the best order in response to real-time events.

cs.DC↗

arXiv2024

EinDecomp: Decomposition of Declaratively-Specified Machine Learning and Numerical Computations for Parallel Execution

We consider the problem of automatically decomposing operations over tensors or arrays so that they can be executed in parallel on multiple devices. We address two, closely-linked questions. First, what programming abstraction should systems for tensor-based computing offer to enable such decompositions? Second, given that abstraction, how should such systems automatically decompose a tensor-based computation? We assert that tensor-based systems should offer a programming abstraction based on an extended Einstein summation notation, which is a fully declarative, mathematical specification for tensor computations. We show that any computation specified in the Einstein summation notation can be re-written into an equivalent tensor-relational computation, and this re-write generalizes existing notations of tensor parallelism such as "data parallel'' and "model parallel.'' We consider the algorithmic problem of optimally computing a tensor-relational decomposition of a graph of operations specified in our extended Einstein summation notation, and we experimentally show the value of the algorithm that we develop.

cs.DC↗