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 …

The parallel and distributed simulation field has evolved and grown from its origins in the
1970s and 1980s and remains an active field of research to this day. A brief overview of …

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 …

Today most systems in high-performance computing (HPC) feature a hierarchical hardware
design: Shared memory nodes with several multi-core CPUs are connected via a network …

Skyline computation has many applications including multi-criteria decision making. In this
paper, we study the problem of selecting k skyline points so that the number of points, which …

Emergent heterogeneous systems must be optimized for both power and performance at
exascale. Massive parallelism combined with complex memory hierarchies form a barrier to …

Most recent research in power-aware supercomputing has focused on making individual
nodes more efficient and measuring the results in terms of flops per watt. While this work is …

Power-aware execution of parallel programs is now a primary concern in large-scale HPC
environments. Prior research in this area has explored models and algorithms based on …

Dynamic voltage and frequency scaling (DVFS) can make modern processors more power
and energy efficient if we can accurately predict the effect of frequency scaling on processor …

In current computing systems, many applications require guarantees on their maximum
power consumption to not exceed the available power budget. On the other hand, for some …