General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from
researchers in graph analyzing, scientific computing, and deep learning. Many optimization …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete
systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial …

Dedicated accelerator hardware has become essential for processing AI-based workloads,
leading to the rise of novel accelerator architectures. Furthermore, fundamental differences …

This paper proposes the Tensor Marshaling Unit (TMU), a near-core programmable dataflow
engine for multicore architectures that accelerates tensor traversals and merging, the most …

De novo genome assembly, ie, rebuilding the sequence of an unknown genome from
redundant and erroneous short sequences, is a key but computationally intensive step in …

Domains such as bio-informatics, social network analysis, and computer vision, describe
relations between entities and cannot be interpreted as vectors or fixed grids, instead, they …

Sparse GEneral Matrix-matrix Multiplication (SpGEMM) is a critical operation in many
applications. Current multithreaded implementations are based on Gustavson's algorithm …

In generating large quantities of DNA data, high-throughput sequencing technologies
require advanced bioinformatics infrastructures for efficient data analysis. k-mer counting …

This Exascale Computing Project (ECP) milestone report summarizes the status of all 30
ECP Applications Development (AD) subprojects at the end of FY20. In October and …