Computer Engineering Commons^™

Toward Reliable And Efficient Message Passing Software For Hpc Systems: Fault Tolerance And Vector Extension, Dong Zhong

Doctoral Dissertations

As the scale of High-performance Computing (HPC) systems continues to grow, researchers are devoted themselves to achieve the best performance of running long computing jobs on these systems. My research focus on reliability and efficiency study for HPC software.

First, as systems become larger, mean-time-to-failure (MTTF) of these HPC systems is negatively impacted and tends to decrease. Handling system failures becomes a prime challenge. My research aims to present a general design and implementation of an efficient runtime-level failure detection and propagation strategy targeting large-scale, dynamic systems that is able to detect both node and process failures. Using multiple overlapping …

A Magnetic Actuated Fully Insertable Robotic Camera System For Single Incision Laparoscopic Surgery, Xiaolong Liu

Doctoral Dissertations

Minimally Invasive Surgery (MIS) is a common surgical procedure which makes tiny incisions in the patients anatomy, inserting surgical instruments and using laparoscopic cameras to guide the procedure. Compared with traditional open surgery, MIS allows surgeons to perform complex surgeries with reduced trauma to the muscles and soft tissues, less intraoperative hemorrhaging and postoperative pain, and faster recovery time. Surgeons rely heavily on laparoscopic cameras for hand-eye coordination and control during a procedure. However, the use of a standard laparoscopic camera, achieved by pushing long sticks into a dedicated small opening, involves multiple incisions for the surgical instruments. Recently, single …

Hardware-Efficient Scalable Reinforcement Learning Systems, Zhenzhen Liu

Doctoral Dissertations

Reinforcement Learning (RL) is a machine learning discipline in which an agent learns by interacting with its environment. In this paradigm, the agent is required to perceive its state and take actions accordingly. Upon taking each action, a numerical reward is provided by the environment. The goal of the agent is thus to maximize the aggregate rewards it receives over time. Over the past two decades, a large variety of algorithms have been proposed to select actions in order to explore the environment and gradually construct an e¤ective strategy that maximizes the rewards. These RL techniques have been successfully applied …