Memory capacity is a major bottleneck for training deep neural networks (DNN).
Heterogeneous memory (HM) combining fast and slow memories provides a promising …

Sparse tensor contractions appear commonly in many applications. Efficiently computing a
two sparse tensor product is challenging: It not only inherits the challenges from common …

Abstract Non-Volatile Main Memories (NVMMs) have recently emerged as a promising
technology for future memory systems. Generally, NVMMs have many desirable properties …

The memory demand of virtual machines (VMs) is increasing, while the traditional DRAM-
only memory system has limited capacity and high power consumption. The tiered memory …

The tiered-memory system can effectively expand the memory capacity for virtual machines
(VMs). However, virtualization introduces new challenges specifically in enforcing …

Multi-terabyte large memory systems are often characterized by more than two memory tiers
with different latency and bandwidth. Multi-tiered large memory systems call for rethinking of …

Sparse tensor contraction sequence has been widely employed in many fields, such as
chemistry and physics. However, how to efficiently implement the sequence faces multiple …

Particle simulations of plasma are important for understanding plasma dynamics in space
weather and fusion devices. However, production simulations that use billions and even …

QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT)
system to support various applications like smart city, smart grid, smart health, intelligent …

With emerging storage-class memory (SCM) nearing commercialization, there is evidence
that it will deliver the much-anticipated high density and access latencies within only a few …