Graph processing plays a pivotal role in numerous large-scale applications, including social and transportation networks. One of the primary challenges in handling large-scale graph data is its tendency to surpass DRAM capacities. Conventional methods ...

Rapid growth in data size poses significant computational and memory challenges to data processing. FPGA accelerators and near-storage processing have emerged as compelling solutions for tackling the growing computational and memory requirements. Many ...

The rapid increase in data volume has introduced a range of problems for data centers, notably increasing their operational demands and pushing their capabilities to efficiently manage, store, and process information. Further, such large volumes of data ...

Graph Neural Network (GNN) training algorithms commonly perform neighborhood sampling to construct fixed-size mini-batches for weight aggregation on GPUs. State-of-the-art disk-based GNN frameworks compute sampling on the CPU, transferring edge ...

Large-scale machine learning (ML) models rely on extremely large datasets to learn their exponentially growing number of parameters. While these models achieve unprecedented success, the increase in training time and hardware resources required is ...

Rapid growth in data size poses increasing computational and memory challenges to data processing. FPGA accelerators and near-storage processing are promising candidates for tackling computational and memory requirements, and many near-storage FPGA ...