The enormous growth in the amount of data generated by the life sciences is continuously
shifting the field from model-driven science towards data-driven science. The need for …

Disaggregated memory is a promising approach that addresses the limitations of traditional
memory architectures by enabling memory to be decoupled from compute nodes and …

Existing GPU lossy compressors suffer from expensive data movement overheads,
inefficient memory access patterns, and high synchronization latency, resulting in limited …

Computational Pangenomics is an emerging field that studies genetic variation using a
graph structure encompassing multiple genomes. Visualizing pangenome graphs is vital for …

Dynamic contingency analysis (DCA) for modern power systems is fundamental to help
researchers and operators look ahead of potential issues, arrange operational plans, and …

Tightly-coupled HPC systems have rigid memory allocation and can result in expensive
memory resource underutilization. As novel memory and network technologies mature …

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

Bioinformatics workloads differ significantly from traditional scientific computing and AI
workloads because they consist primarily of integer-only operations and string comparisons …

String alignment algorithms are an essential tool for understanding DNA and protein
sequences. They demand substantial computation in real-world applications, and are thus a …

The advancement of Silicon CMOS technology has led to information technology innovation
for decades. Transistor density, computation frequency, and storage capacity have been …