David R. Kaeli (0000-0002-5692-0151)

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Biography

David Kaeli is a COE Distinguished Professor at Northeastern University in Boston, MA, 02115, USA. His research interests include computer architecture, high performance hardware/software, hardware security, reliability and data analytics. He received his PhD in Electrical Engineering from Rutgers University. He is a Fellow of both the IEEE and ACM.

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Northeastern University : Boston, Massachusetts, US

1993-09-01 to present | Professor (Electrical and Computer Engineering)

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David R. Kaeli

https://orcid.org/0000-0002-5692-0151

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Biography

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Northeastern University : Boston, Massachusetts, US

Education and qualifications (1)

Rutgers The State University of New Jersey: New Brunswick, NJ, US

Works (50 of 695)

Scalability Limitations of Processing-in-Memory using Real System Evaluations

Scalability Limitations of Processing-in-Memory using Real System Evaluations

MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training

Scalability Limitations of Processing-in-Memory using Real System Evaluations

Accelerating Finite Field Arithmetic for Homomorphic Encryption on GPUs

Thought Bubbles: A Proxy into Players’ Mental Model Development

VCSR: An Efficient GPU Memory-Aware Sparse Format

Framework for Denoising Monte Carlo Photon Transport Simulations Using Deep Learning

Characterizing and Exploiting Soft Error Vulnerability Phase Behavior in GPU Applications

Spartan: A Sparsity-Adaptive Framework to Accelerate Deep Neural Network Training on GPUs

Daisen: A Framework for Visualizing Detailed GPU Execution

A Novel GPU Overdrive Fault Attack

Effects of trust-based decision making in disrupted supply chains

Griffin: Hardware-Software Support for Efficient Page Migration in Multi-GPU systems

Hardware/Software Obfuscation against Timing Side-channel Attack on a GPU

Introducing Gamettes: A Playful Approach for Capturing Decision-Making for Informing Behavioral Models

Machine learning kernel divergence-based de novo sampling size optimization for environmental applications: A cases study of single-cell Raman spectroscopic phenotyping of EBPR microbial communities

The Path to Multi-GPU Comuting

Vega: A Computer Vision Processing Enhancement Framework with Graph-based Acceleration

A Comprehensive Evaluation of the Effects of Input Data on the Resilience of GPU Applications

Discovering Programmer Intention Behind Written Source Code

Effect of Hydrogeological and Anthropogenic Factors on the Spatial and Temporal Distribution of CVOCs in the Karst System of Northern Puerto Rico

Evaluation of Volta-based DGX-1 System Using DNN Workloads

Exploiting Adaptive Data Compression to Improve Performance and Energy-efficiency of Compute Workloads in Multi-GPU Systems

Exploiting Bank Conflict based Side-channel Timing Leakage of GPUs

GIPSim: Low-level Power Modeling for Resiliency from Side Channel Attacks on GPUs

HAWS: Accelerating GPU Wavefront Execution through Selective Out-of-order Execution

Intra-Cluster Coalescing and Distributed-Block Scheduling to Reduce GPU NoC Pressure

Machine Learning-Based Malware Detection using Recurrent Neural Networks

MGPUSim: Enabling Multi-GPU Performance Modeling and Optimization

PCFI: Program Counter Guided Fault Injection for Accelerating GPU Reliability Assessment

Priority-Based PCIe Scheduling for Multi-Tenant Multi-GPU System

Sample Size Assessment via Kernel Divergence: A Case Study with Single-cell Raman Spectroscopic Datasets

Side-channel Timing Attack of RSA on a GPU

Student Cluster Competition 2018, Team Northeastern University: Reproducing Performance of a Multi-Physics Simulations of the Tsunamigenic 2004 Sumatra Megathrust Earthquake on the AMD EPYC 7551 Architecture

Summarizing CPU and GPU Design Trends with Product Data

Using Machine Learning for Data Imputation to Support Environment Health Studies in Northern Puerto Rico

Student cluster competition 2018, team northeastern university: Reproducing performance of a multi-physics simulations of the Tsunamigenic 2004 Sumatra Megathrust earthquake on the AMD EPYC 7551 architecture

Intra-Cluster Coalescing and Distributed-Block Scheduling to Reduce GPU NoC Pressure

Priority-Based PCIe Scheduling for Multi-Tenant Multi-GPU Systems

Analyzing and Increasing the Reliability of Convolutional Neural Networks on GPUs

11th Workshop on General Purpose Processing using GPUs

A Hybrid Approach to Identifying Key Factors in Environmental Health Studies

A Hybrid Approach to Identifying Key Factors in Environmental Health Studies

A Timing Side Channel Attack on a Mobile GPU

Accelerated Design of Strong, Highly Conductive Carbon Nanotube-based Fibers via Electrical Fusion

Airavat: Improving Energy Efficiency of Heterogeneous Applications

An Efficient Data Management Framework for Puerto Rico Testsite for Exploring Contamination Threats (PROTECT)

An Integrated Simulation Framework for Examining Resiliency in Pharmaceutical Drug Supply Chain Considering Human Behaviors Component

Block Cooperation: Advancing Lifetime of Resistive Memories by Increasing Utilization of Error Correcting Codes