Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …

With the increasing size of DNN models and the growing discrepancy between compute
performance and memory bandwidth, fusing multiple layers together to reduce off-chip …

Over the past few years, the explosion in sparse tensor algebra workloads has led to a
corresponding rise in domain-specific accelerators to service them. Due to the irregularity …

The widespread use of Sparse Matrix Dense Matrix Multiplication (SpMM) and Sampled
Dense Matrix Dense Matrix Multiplication (SDDMM) kernels makes them candidates for …

Running multiple deep neural networks (DNNs) in parallel has become an emerging
workload in both edge devices, such as mobile phones where multiple tasks serve a single …

The design space exploration of scaled-out manycores for communication-intensive
applications (eg, graph analytics and sparse linear algebra) is hampered due to either lack …

Solving sparse systems of linear equations is a crucial component in many science and
engineering problems, like simulating physical systems. Sparse matrix factorization …

Quantum circuit simulator (QCS) is essential for designing quantum algorithms because it
assists researchers in understanding how quantum operations work without access to …

In this work, we propose a unified abstraction for graph algorithms: the Extended General
Einsums language, or EDGE. The EDGE language expresses graph algorithms in the …

Performance in scientific and engineering applications such as computational physics,
algebraic graph problems or Convolutional Neural Networks (CNN), is dominated by the …