Disaggregated memory can address resource provisioning inefficiencies in current
datacenters. Multiple software runtimes for disaggregated memory have been proposed in …

Recent research efforts propose remote memory systems that pool memory from multiple
hosts. These systems rely on the virtual memory subsystem to track application memory …

Disaggregated memory provides many cost savings and resource provisioning benefits for
current datacenters, but software systems enabling disaggregated memory access result in …

K-means clustering is the most well-known unsupervised learning method that partitions the
input dataset into K clusters based on the similarity between the data samples. In this paper …

Advances in hardware are a challenge but also a new opportunity. In particular, devices like
FPGAs and GPUs are a chance to extend and customize relational engines with new …

Low-latency inference for machine learning models is increasingly becoming a necessary
requirement, as these models are used in mission-critical applications such as autonomous …

K-Means is a popular clustering algorithm widely used and extensively studied in the
literature. In this paper we explore the challenges and opportunities in using low precision …