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

The fields of Metagenomics and Metatranscriptomics involve the examination of complete
nucleotide sequences, gene identification, and analysis of potential biological functions …

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

Background Bioinformatic workflows frequently make use of automated genome assembly
and protein clustering tools. At the core of most of these tools, a significant portion of …

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 …

Metagenomics has enabled accessing the genetic repertoire of natural microbial
communities. Metagenome shotgun sequencing has become the method of choice for …

Similarity search is one of the most fundamental computations that are regularly performed
on ever-increasing protein datasets. Scalability is of paramount importance for uncovering …

Combinatorial algorithms in general and graph algorithms in particular play a critical
enabling role in numerous scientific applications. However, the irregular memory access …

We present a distributed-memory algorithm for sparse matrix-matrix multiplication
(SpGEMM) of extremely large matrices where the generated output is larger than the …