In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

MapReduce, originally developed by Google for search applications, has recently become a
popular programming framework for parallel and distributed environments. This paper …

We introduce SparkCL, an open source unified programming framework based on Java,
OpenCL and the Apache Spark framework. The motivation behind this work is to bring …

A K-means clustering algorithm involves partitioning of data iteratively into k clusters. It is
one of the most popular data-mining algorithms [Wu et al. 2007], and is widely used in other …

In recent years, heterogeneous computing has emerged as the vital way to increase
computers? performance and energy efficiency by combining diverse hardware devices …

The paper is concerned with the issue of how software systems actually use Heterogeneous
Parallel Architectures (HPAs), with the goal of optimizing power consumption on these …

Large-scale workloads often show parallelism of different levels. which offers acceleration
potential for clusters and parallel processors. Although processors such as GPGPUs and …

Combining several types of devices and architectures is at the heart of heterogeneous
computing's power efficiency advantage, but the strength of heterogeneous systems is also …

Since the early conception of managed runtime systems with tiered JIT compilation, several
research attempts have been made to accelerate the bytecode execution. In this paper, we …

FPGAs have shown great promise for accelerating computationally intensive algorithms.
However, FPGA-based accelerator design is tedious and time consuming if we rely on …