There has been significant amount of excitement and recent work on GPU-based database
systems. Previous work has claimed that these systems can perform orders of magnitude …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

GPU-accelerated database systems have been studied for more than 10 years, ranging from
prototy** development to industry products serving in multiple domains of data …

Modern solid state drives (SSDs) unnecessarily confine host programs to the conventional
block I/O interface, leading to suboptimal performance and resource under-utilization …

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 …

As the size of data generated every day grows dramatically, the computational bottleneck of
computer systems has been shifted toward the storage devices. Thanks to recent …

Recommender models are commonly used to suggest relevant items to a user for e-
commerce and online advertisement-based applications. These models use massive …

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 …

GPUs are increasingly used for high-performance and interactive data analytics workloads
due to their capability to accelerate computation using massive parallelism. A key constraint …

GPUs offer massive parallelism and high-bandwidth memory access, making them an
attractive option for accelerating data analytics in database systems. However, while modern …