GNN-based anomaly detection has recently attracted considerable attention. Existing
attempts have thus far focused on jointly learning the node representations and the classifier …

This work reveals that memory-intensive computation is a rising performance-critical factor in
recent machine learning models. Due to a unique set of new challenges, existing ML …

Parallel technology boosts data processing in recent years, and parallel direct data
processing on hierarchically compressed documents exhibits great promise. The high …

Due to decelerating gains in single-core CPU performance, computationally expensive
simulations are increasingly executed on highly parallel hardware platforms. Agent-based …

Abstract Graphical Processing Unit provides massive parallelism due to the presence of
hundreds of cores. Usage of GPUs for general purpose computation (GPGPU) has resulted …

In modern data management systems, directly performing operations on compressed data
has been proven to be a big success facing big data problems. These systems have …

We study the problem of supervised contrastive (SupCon) learning on graphs. The SupCon
loss has been recently proposed for classification tasks by pulling data points in the same …

Deep neural networks (DNNs) have achieved remarkable success in many fields. However,
large-scale DNNs also bring storage costs when storing snapshots for preventing clusters' …

Sparse matrix-vector multiplication (SpMV) is a fundamental routine in computational
science and engineering. Its optimization methods on various homogeneous parallel …

With the development of computer architecture, even for embedded systems, GPU devices
can be integrated, providing outstanding performance and energy efficiency to meet the …