Literature has always witnessed efforts that make use of parallel algorithms/parallel
architecture to improve performance; machine learning space is no exception. In fact, a …

Query co-processing on graphics processors (GPUs) has become an effective means to
improve the performance of main memory databases. However, this co-processing requires …

Thanks to massive parallelism in modern Graphics Processing Units (GPUs), emerging data
processing applications in GPU computing exhibit ten-fold speedups compared to CPU-only …

Heterogeneous systems that contain multiple types of resources, such as CPUs and GPUs,
are becoming increasingly popular thanks to the potential of achieving high performance …

Data mining tools may be computationally demanding, so there is an increasing interest on
parallel computing strategies to improve their performance. The popularization of Graphics …

Recently, we have witnessed that cloud providers start to offer heterogeneous computing
environments. There have been wide interests in both clusters and cloud of adopting …

GPU-accelerated in-memory database systems have gained a lot of popularity over the last
several years. However, GPUs have limited memory capacity, and the data to process might …

In in-memory database systems augmented by hardware accelerators, accelerating the
index searching operations can greatly increase the runtime performance of database …

General purpose computation based on GPU is a hot topic for research in recent years. The
paper presents the parallel implementation of Viterbi algorithm on GPU based on features of …

Programmer-managed GPU memory is a major challenge in writing GPU applications.
Programmers must rewrite and optimize an existing code for a different GPU memory size for …