2 citations · 3 across the 4 of their papers we have counts for
5 papers
ZipFlow: a Compiler-based Framework to Unleash Compressed Data Movement for Modern GPUs
Gwangoo Yeo, Zhiyang Shen, Wei Cui +5
In GPU-accelerated data analytics, the overhead of data transfer from CPU to GPU becomes a performance bottleneck when the data scales beyond GPU memory capacity due to the limited…
Nexus: Inferring Join Graphs from Metadata Alone via Iterative Low-Rank Matrix Completion
Tianji Cong, Yuanyuan Tian, Andreas Mueller +5
Automatically inferring join relationships is a critical task for effective data discovery, integration, querying and reuse. However, accurately and efficiently identifying these r…
Zorse: Optimizing LLM Training Efficiency on Heterogeneous GPU Clusters
Runsheng Benson Guo, Utkarsh Anand, Khuzaima Daudjee +1
Large language models (LLMs) require vast amounts of GPU compute to train, but limited availability and high costs of GPUs make homogeneous clusters impractical for many organizati…
GPU Acceleration of SQL Analytics on Compressed Data
Zezhou Huang, Krystian Sakowski, Hans Lehnert +5
GPUs are uniquely suited to accelerate (SQL) analytics workloads thanks to their massive compute parallelism and High Bandwidth Memory (HBM) -- when datasets fit in the GPU HBM, pe…
Terabyte-Scale Analytics in the Blink of an Eye
Bowen Wu, Wei Cui, Carlo Curino +2
For the past two decades, the DB community has devoted substantial research to take advantage of cheap clusters of machines for distributed data analytics -- we believe that we are…