Computer architecture simulators play an important role in advancing computer architecture
research. With wider research directions and the increased number of simulators that have …

Energy consumption has been widely studied in the computer architecture field for decades.
While the adoption of energy as a metric in machine learning is emerging, the majority of …

Multicore systems are in demand due to their high performance thus making application
mapping an important research area in this field. Breaking an application into multiple …

Graphics Processing Units (GPUs) are rapidly dominating the accelerator space, as
illustrated by their wide-spread adoption in the data analytics and machine learning markets …

Irregular workloads are typically bottlenecked by the memory system. These workloads often
use sparse data representations, eg, compressed sparse row/column (CSR/CSC), to …

Graph processing is an important analysis technique for a wide range of big data
applications. The ability to explicitly represent relationships between entities gives graph …

We present Decoupled Vector Runahead (DVR), an in-core prefetching technique,
executing separately to the main application thread, that exploits massive amounts of …

Resource disaggregation offers a cost effective solution to resource scaling, utilization, and
failure-handling in data centers by physically separating hardware devices in a server …

Graph analytics power a range of applications in areas as diverse as finance, networking
and business logistics. A common property of graphs used in the domain of graph analytics …

Heterogeneous architectures offer divers computing capabilities. Composite Cores
Architecture (CCA) is a class of dynamic heterogeneous architectures that empowers the …