Many proposals to scale quantum technology rely on modular or distributed designs
wherein individual quantum processors, called nodes, are linked together to form one large …

Torus networks are an attractive topology in supercomputing, balancing the tradeoff
between network diameter and hardware costs. The nodes in a torus network are connected …

Diagnosing the cluster-based performance of large-scale deep neural network (DNN)
models during training is essential for improving training efficiency and reducing resource …

The era of extremely heterogeneous supercomputing brings with itself the devil of increased
performance variation and reduced reproducibility. There is a lack of understanding in the …

The overall efficiency of an extreme-scale supercomputer largely relies on the performance
of its network interconnects. Several of the state of the art supercomputers use networks …

Mathematical optimization is the process of determining the set of globally or locally optimal
parameters in a finite or infinite search space. It has been extensively employed in the …

Understanding and tuning the performance of extreme-scale parallel computing systems
demands a streaming approach due to the computational cost of applying offline algorithms …

Abstract Graphics Processing Units (GPUs) have been widely used to accelerate artificial
intelligence, physics simulation, medical imaging, and information visualization applications …

To optimize the performance and efficiency of HPC applications, programmers and analysts
often need to collect various performance metrics for each computer at different time points …

Visualization design studies bring together visualization researchers and domain experts to
address yet unsolved data analysis challenges stemming from the needs of the domain …