83 citations · 613 across the 55 of their papers we have counts for
56 papers
CODAG: Characterizing and Optimizing Decompression Algorithms for GPUs
Jeongmin Park, Zaid Qureshi, Vikram Mailthody +10
Data compression and decompression have become vital components of big-data applications to manage the exponential growth in the amount of data collected and stored. Furthermore, b…
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
Jeongmin Brian Park, Vikram Sharma Mailthody, Zaid Qureshi +1
Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Al…
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka +3
Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-sc…
Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures
Kun Wu, Mert Hidayetoğlu, Xiang Song +4
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs. While RGNN…
Parallelizing Maximal Clique Enumeration on GPUs
Mohammad Almasri, Yen-Hsiang Chang, Izzat El Hajj +3
We present a GPU solution for exact maximal clique enumeration (MCE) that performs a search tree traversal following the Bron-Kerbosch algorithm. Prior works on parallelizing MCE o…
Submission-Aware Reviewer Profiling for Reviewer Recommender System
Omer Anjum, Alok Kamatar, Toby Liang +2
Assigning qualified, unbiased and interested reviewers to paper submissions is vital for maintaining the integrity and quality of the academic publishing system and providing valua…
Can Language Models Be Specific? How?
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong +1
"He is a person", "Paris is located on the earth". Both statements are correct but meaningless - due to lack of specificity. In this paper, we propose to measure how specific the l…
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang +2
We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between en…
GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture
Zaid Qureshi, Vikram Sharma Mailthody, Isaac Gelado +10
Graphics Processing Units (GPUs) have traditionally relied on the host CPU to initiate access to the data storage. This approach is well-suited for GPU applications with known data…
A Compiler Framework for Optimizing Dynamic Parallelism on GPUs
Mhd Ghaith Olabi, Juan Gómez Luna, Onur Mutlu +2
Dynamic parallelism on GPUs allows GPU threads to dynamically launch other GPU threads. It is useful in applications with nested parallelism, particularly where the amount of neste…
Graph Neural Network Training with Data Tiering
Seung Won Min, Kun Wu, Mert Hidayetoğlu +3
Graph Neural Networks (GNNs) have shown success in learning from graph-structured data, with applications to fraud detection, recommendation, and knowledge graph reasoning. However…
MLHarness: A Scalable Benchmarking System for MLCommons
Yen-Hsiang Chang, Jianhao Pu, Wen-mei Hwu +1
With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common se…
Understanding Jargon: Combining Extraction and Generation for Definition Modeling
Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang +2
Can machines know what twin prime is? From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly wha…
Open Relation Modeling: Learning to Define Relations between Entities
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong +1
Relations between entities can be represented by different instances, e.g., a sentence containing both entities or a fact in a Knowledge Graph (KG). However, these instances may no…
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong +1
We propose to measure fine-grained domain relevance - the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measuremen…
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection
Jiachen Li, Bowen Cheng, Rogerio Feris +4
Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based…
Parallel K-Clique Counting on GPUs
Mohammad Almasri, Izzat El Hajj, Rakesh Nagi +2
Counting k-cliques in a graph is an important problem in graph analysis with many applications such as community detection and graph partitioning. Counting k-cliques is typically d…
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
Seung Won Min, Kun Wu, Sitao Huang +5
Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale graph-based recommender systems. Training GCN requires the minibatch generator traversing graphs and sam…
PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
Seung Won Min, Kun Wu, Sitao Huang +5
With the increasing adoption of graph neural networks (GNNs) in the machine learning community, GPUs have become an essential tool to accelerate GNN training. However, training GNN…
Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19
Vikram Sharma Mailthody, James Wei, Nicholas Chen +14
COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic a…
TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes
Carl Pearson, Kun Wu, I-Hsin Chung +2
MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications. These datatypes are recursively constructed at runtime from primitive…
Interpretable Visual Reasoning via Induced Symbolic Space
Zhonghao Wang, Kai Wang, Mo Yu +4
We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images; an…
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
Cong Hao, Yao Chen, Xiaofan Zhang +4
High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators. To improve the overall solution quality…
Exploring Semantic Capacity of Terms
Jie Huang, Zilong Wang, Kevin Chen-Chuan Chang +2
We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intellige…