89 citations · 119 across the 14 of their papers we have counts for
20 papers
GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient Parallel Processing of Massive Datasets
Junhyeok Jang, Miryeong Kwon, Donghyun Gouk +2
We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use pr…
Asynchronous Persistence with ASAP
Ahmed Abulila, Izzat El Hajj, Myoungsoo Jung +1
Supporting atomic durability of updates for persistent memories is typically achieved with Write-Ahead Logging (WAL). WAL flushes log entries to persistent memory before making the…
Failure Tolerant Training with Persistent Memory Disaggregation over CXL
Miryeong Kwon, Junhyeok Jang, Hanjin Choi +2
This paper proposes TRAININGCXL that can efficiently process large-scale recommendation datasets in the pool of disaggregated memory while making training fault tolerant with low o…
Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs
Miryeong Kwon, Donghyun Gouk, Sangwon Lee +1
Graph neural networks (GNNs) process large-scale graphs consisting of a hundred billion edges. In contrast to traditional deep learning, unique behaviors of the emerging GNNs are e…
Ohm-GPU: Integrating New Optical Network and Heterogeneous Memory into GPU Multi-Processors
Jie Zhang, Myoungsoo Jung
Traditional graphics processing units (GPUs) suffer from the low memory capacity and demand for high memory bandwidth. To address these challenges, we propose Ohm-GPU, a new optica…
Revamping Storage Class Memory With Hardware Automated Memory-Over-Storage Solution
Jie Zhang, Miryeong Kwon, Donghyun Gouk +4
Large persistent memories such as NVDIMM have been perceived as a disruptive memory technology, because they can maintain the state of a system even after a power failure and allow…