Today's GPU graph processing frameworks face scalability and efficiency issues as the
graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe …

Three-dimensional (3D)-stacking technology and the memory-wall problem have
popularized processing-in-memory (PIM) concepts again, which offers the benefits of …

With the rise of general purpose computing on graphics processing units (GPGPU), the
influence from consumer markets can now be seen across the spectrum of computer …

MapReduce is a programming model from Google that facilitates parallel processing on a
cluster of thousands of commodity computers. The success of MapReduce in cluster …

Abstract Recently Field-Programmable Gate Array (FPGA) vendors, such as Altera and
Xilinx released an Open Computing Language Software Development Kit (OpenCL SDK) …

In this paper, a parallel computing scheme for performing implicit finite element calculations
on acoustic problems running on a graphics processing unit (GPU) is proposed. This …

GPUs have become part of most commodity systems. Nonetheless, they are often
underutilized when not executing graphics-intensive or special-purpose numerical …

This work explores the interaction of three communication-centric design dimensions for
graph workloads on emerging integrated CPU-GPU systems: update propagation with and …

Although there is an extensive literature on GPU implementations of graph algorithms, we
do not yet have a clear understanding of how implementation choices impact performance …

General-purpose GPU applications increasingly use synchronization to enforce ordering
between many threads accessing shared data. Accordingly, recently there has been a push …