FPGA accelerators are being applied in various types of systems ranging from embedded
systems to cloud computing for their high performance and energy efficiency. Given the …

We present GPMR, our stand-alone MapReduce library that leverages the power of GPU
clusters for large-scale computing. To better utilize the GPU, we modify MapReduce by …

This paper describes a heterogeneous computer cluster called Axel. Axel contains a
collection of nodes; each node can include multiple types of accelerators such as FPGAs …

Machine learning and data mining are gaining increasing attentions of the computing
society. FPGA provides a highly parallel, low power, and flexible hardware platform for this …

As association rules widely used, it needs to study many problems, one of which is the
generally larger and multi-dimensional datasets, and the rapid growth of the mount of data …

With the end of CPU core scaling due to dark silicon limitations, customized accelerators on
FPGAs have gained increased attention in modern datacenters due to their lower power …

We design and implement Mars, a MapReduce runtime system accelerated with graphics
processing units (GPUs). MapReduce is a simple and flexible parallel programming …

Today's heterogeneous architectures bring together multiple general-purpose CPUs and
multiple domain-specific GPUs and FPGAs to provide dramatic speedup for many …

Modern datacenters are reinforcing the computational power and energy efficiency by
assimilating field programmable gate arrays (FPGAs). The sustainability of this large-scale …

MapReduce, originally developed by Google for search applications, has recently become a
popular programming framework for parallel and distributed environments. This paper …