In the big data era, much real-world data can be naturally represented as graphs.
Consequently, many application domains can be modeled as graph processing. Graph …

Approximate nearest neighbor (ANN) searching is a fundamental problem in computer
science with numerous applications in (eg,) machine learning and data mining. Recent …

Graph mining algorithms that aim at identifying structural patterns of graphs are typically
more complex than graph computation algorithms such as breadth first search. Researchers …

Abstract Graphical Processing Unit provides massive parallelism due to the presence of
hundreds of cores. Usage of GPUs for general purpose computation (GPGPU) has resulted …

Most GPU-based graph systems cannot handle large-scale graphs that do not fit in the GPU
memory. The ever-increasing graph size demands a scale-up graph system, which can run …

Accelerating SQL queries on stream processing by utilizing heterogeneous coprocessors,
such as GPUs, has shown to be an effective approach. Most works show that heterogeneous …

Today's rapidly growing document volumes pose pressing challenges to modern document
analytics frameworks, in both space usage and processing time. Recently, a promising …

Huge amounts of data have been generated on edge devices every day, which requires
efficient data analytics and management. However, due to the limited computing capacity of …

A challenge that heterogeneous system programmers face is leveraging the performance of
all the devices that integrate the system. This paper presents Sigmoid, a new load balancing …

Heterogeneous CPU-FPGA systems are evolving towards tighter integration between CPUs
and FPGAs for improved performance and energy efficiency. At the same time …