As computation becomes increasingly limited by data movement and energy consumption,
exploiting locality throughout the memory hierarchy becomes critical to continued …

Graph processing is an increasingly important application domain and is typically
communication-bound. In this work, we analyze the performance characteristics of three …

System-on-Chip (SoC) complexity and the increasing costs of silicon motivate the breaking
of an SoC into smaller" chiplets." A chiplet-based SoC design process has the promise to …

Graph analytics is an emerging application which extracts insights by processing large
volumes of highly connected data, namely graphs. The parallel processing of graphs has …

Writing high-performance GPU implementations of graph algorithms can be challenging. In
this paper, we argue that three optimizations called throughput optimizations are key to high …

Algorithms operating on a graph setting are known to be highly irregular and unstructured.
This leads to workload imbalance and data locality challenge when these algorithms are …

We propose overcoming the memory capacity limitation of GPUs with high-capacity Storage-
Class Memory (SCM) and DRAM cache. By significantly increasing the memory capacity …

Large graph processing is now a critical component of many data analytics. Graph
processing is used from social networking Web sites that provide context-aware services …

Unified Memory in heterogeneous systems serves a wide range of applications. However,
limited capacity of the device memory becomes a first order performance bottleneck for data …

Scientists are increasingly exploring and utilizing the massive parallelism of general-
purpose accelerators such as GPUs for scientific breakthroughs. As a result, datacenters …