Deep Learning (DL) models have achieved superior performance in many application
domains, including vision, language, medical, commercial ads, entertainment, etc. With the …

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 networks represent contractions over multiple sparse tensors. Tensor
contractions are higher-order analogs of matrix multiplication. Tensor networks arise …

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

Sparse tensor algebra computations have become important in many real-world
applications like machine learning, scientific simulations, and data mining. Hence …

The emergence of heterogeneous memory (HM) provides a cost-effective and high-
performance solution to memory-consuming HPC applications. Deciding the placement of …

Tiered memory, combining multiple memory components with different performance and
capacity, provides a cost-effective solution to increase memory capacity and improve …

The many-body correlation function is a fundamental computation kernel in modern physics
computing applications, eg, Hadron Contractions in Lattice quantum chromodynamics …

Because of cost, non-volatile memory NVDIMMs such as Intel Optane are attractive in single-
node big-memory systems. We evaluate performance and cost trade-offs when using …

Many fields of scientific simulation, such as chemistry and condensed matter physics, are
increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this …