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Miaoqi Chu

dblp:421/2341 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0002-6465-1529ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 56% GPUs and heterogeneous computing · 44%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems
data compression
0.912025
lsCOMP: Efficient Light Source Compression · SC 2025
GPUs and heterogeneous computing › GPU computing › GPU algorithms
GPU compression
0.912025
lsCOMP: Efficient Light Source Compression · SC 2025

Methods — techniques the papers use, named apart from their topics

single-kernel design · 0.9fixed-length encoding · 0.9
YearPublicationVenuePosition
2025 lsCOMP: Efficient Light Source Compression
abstract
Light source facilities, which generate X-rays for probing microstructures and dynamic processes, produce intense data streams, reaching up to 250 GB/s and projected to exceed 1 TB/s by the end of this decade. Managing such massive data poses critical challenges due to limited local processing capacity and bandwidth constraints when offloading data to HPC systems. To address these challenges, we propose lsCOMP, a GPU compressor that operates within a single kernel. lsCOMP supports both lossless and configurable lossy compression, ensuring high compression ratios and preserved data quality across diverse light source applications. On a single NVIDIA A100 GPU, lsCOMP achieves compression throughputs of 380.89 to 509.21 GB/s in lossless mode, delivering up to 20 times higher performance than industry-leading GPU compressors while achieving superior compression ratios. In lossy modes, lsCOMP further improves throughput and ratios significantly. Additionally, lsCOMP demonstrates versatile performance across various integer datasets and supports TB/s-level random access throughput.
Yafan Huang, Sheng Di, Robert Underwood, Peco Myint, Miaoqi Chu, Guanpeng Li, Nicholas Schwarz, Franck Cappello
SC5