It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …

In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …

Many software systems today are configurable, offering customization of functionality by
feature selection. Understanding how performance varies in terms of feature selection is key …

Many parallel applications suffer from latent performance limitations that may prevent them
from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only …

Many applied scientific domains are increasingly relying on large-scale parallel
computation. Consequently, many large clusters now have thousands of processors …

Power has become a primary concern for HPC systems. Dynamic voltage and frequency
scaling (DVFS) and dynamic concurrency throttling (DCT) are two software tools (or knobs) …

Performance modeling is an important and active area of research in high-performance
computing (HPC). It helps in better job scheduling and also improves overall performance of …

For designers of large-scale parallel computers, it is greatly desired that performance of
parallel applications can be predicted at the design phase. However, this is difficult because …

Application-specific multiprocessor systems-on-chip (MPSoCs) are usually designed by
using a platform-based approach, where a wide range of customizable parameters can be …