26 citations · 53 across the 11 of their papers we have counts for
11 papers
Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
Hao Feng, Boyuan Zhang, Fanjiang Ye +9
DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitate…
A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization
Daoce Wang, Pascal Grosset, Jesus Pulido +8
Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is…
FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data
Xinyu Chen, Jiannan Tian, Ian Beaver +4
While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between th…
Analyzing Impact of Data Reduction Techniques on Visualization for AMR Applications Using AMReX Framework
Daoce Wang, Jesus Pulido, Pascal Grosset +3
Today's scientific simulations generate exceptionally large volumes of data, challenging the capacities of available I/O bandwidth and storage space. This necessitates a substantia…
AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications
Daoce Wang, Jesus Pulido, Pascal Grosset +11
As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences expo…
HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs
Chengming Zhang, Shaden Smith, Baixi Sun +6
Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation. Among all CF approaches, SimpleX is the state-of-the-art method that ado…