The exponential growth of the dataset size demanded by modern big data applications
requires innovative server architecture design for data centers. A server workload comprise …

Existing near-data processing (NDP) techniques have demonstrated their strength for some
specific data-intensive applications. However, they might be inadequate for a data center …

Over the last decades, the processing power of computers has increased at a higher rate
than their memory bandwidth [1]. Even though the historical trend of the doubling of …

The cost of data-movement is one of the fundamental issues with modern compute systems
processing Big Data workloads. One approach to move the computation closer to data is to …

The ever increasing amount of data being handled in data centers causes an intrinsic
inefficiency: moving data around is expensive in terms of bandwidth, latency, and power …

Over the past decades, the memory hierarchy has increasingly become the bottleneck in
general-purpose processors due to a widening gap between the growing demand for large …

The capacity of memory and storage devices is expected to increase drastically with
adoption of the forthcoming memory and integration technologies. This is a welcome …

Near-Data Processing (NDP) has been a promising architectural paradigm to address the
memory wall challenge for data-intensive applications. Typical NDP systems based on 3D …

Existing near-data processing (NDP)-powered architectures have demonstrated their
strength for some data-intensive applications. Data center servers, however, have to serve …

Data centers are currently constructed with fixed blocks (blades); the hard boundaries of this
approach lead to suboptimal utilization of resources and increased energy requirements …