High-performance computing (HPC), according to its name, is traditionally oriented toward
performance, especially the execution time and scalability of the computations. However …

Neural personalized recommendation is the cornerstone of a wide collection of cloud
services and products, constituting significant compute demand of cloud infrastructure. Thus …

Moore's Law and Dennard Scaling have guided the semiconductor industry for the past few
decades. Recently, both laws have faced validity challenges as transistor sizes approach …

The rapidly growing popularity and scale of data-parallel workloads demand a
corresponding increase in raw computational power of Graphics Processing Units (GPUs) …

Heterogeneous system architectures are evolving towards tighter integration among
devices, with emerging features such as shared virtual memory, memory coherence, and …

Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning
algorithms to train on non-euclidean data. GNNs are widely used in recommender systems …

Today's GPU graph processing frameworks face scalability and efficiency issues as the
graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe …

As transistor scaling becomes increasingly more difficult to achieve, scaling the core count
on a single GPU chip has also become extremely challenging. As the volume of data to …

Multi-GPU systems have emerged as a desirable platform to deliver high computing
capabilities and large memory capacity to accommodate large dataset sizes. However …

Heterogeneous CPU-FPGA systems are evolving towards tighter integration between CPUs
and FPGAs for improved performance and energy efficiency. At the same time …