Deep Learning (DL) has been widely applied in both academia and industry. System innovations can continue to squeeze more efficiency out of modern hardware. Existing syst[...]

Sparse linear algebra operations are very important across many areas of science. Therefore, efficiently finding ways to perform these matrix computations is essential. T[...]

The increased size of data sets is driving the need for increased memory storage andspeed. Persistent memory, PMEM, has emerged as a new solution to the problem. It place[...]

In the realm of high-performance computing (HPC), the efficient scheduling of jobs is essential for improving the performance of the system and making the best use of its[...]

The growing complexity of high-performance computing (HPC) systems has led to the development of parallel programming models, such as OpenMP and OpenACC, to make it easie[...]

Wireless multi-hop networks have been widely exploited for deploying cost-efficient network backbones including wireless community mesh networks, high-speed urban network[...]

Recent advancements in the domain of deep learning models have engendered remarkable progress across numerous computer vision tasks. Notably, there has been a burgeoning [...]

The development of in-memory technologies has fueled the emerging of in-memory computing systems. Simultaneously, with novel memory technologies such as high-bandwidth me[...]

With the increasing prevalence of Parallel File Systems (PFSes) in the context of vast and complex server networks, the importance of accurate anomaly detection on runtim[...]

This thesis presents a comprehensive study on the application of reinforcement learn- ing (RL) to optimize batch job scheduling in high-performance computing (HPC). The r[...]