Constructing energy-efficient database systems to reduce economic costs and
environmental impact has been studied for 10 years. With the emergence of the big data …

Query processing on GPU-style coprocessors is severely limited by the movement of data.
With teraflops of compute throughput in one device, even high-bandwidth memory cannot …

There has been a growing interest in using GPU to accelerate data analytics due to its
massive parallelism and high memory bandwidth. The main constraint of using GPU for data …

Processor manufacturers build increasingly specialized processors to mitigate the effects of
the power wall in order to deliver improved performance. Currently, database engines have …

The emergence of novel hardware accelerators has powered the tremendous growth of
machine learning in recent years. These accelerators deliver incomparable performance …

The GPU-accelerated DataFrame library cuDF has become increasingly popular for data
analytics applications due to its superior performance against CPU-based DataFrame …

Query compilation is an effective approach to improve the performance of repeated
database queries. GPU-based approaches have significant promise, but face difficulties in …

In-memory key-value stores play a critical role in many data-intensive applications to provide
high-throughput and low latency data accesses. In-memory key-value stores have several …

With the advancement of processor technology, numerous hardware accelerators beyond
CPUs and GPUs are emerging to meet the rapid growth in computation demands …

The penetration of wireless communication systems in industrial and private environments is
constantly increasing due to their flexible and mobile application possibilities. Wearables …