A survey of CPU-GPU heterogeneous computing techniques
As both CPUs and GPUs become employed in a wide range of applications, it has been
acknowledged that both of these Processing Units (PUs) have their unique features and …
acknowledged that both of these Processing Units (PUs) have their unique features and …
A comprehensive view of Hadoop research—A systematic literature review
Context: In recent years, the valuable knowledge that can be retrieved from petabyte scale
datasets–known as Big Data–led to the development of solutions to process information …
datasets–known as Big Data–led to the development of solutions to process information …
Enabling transparent acceleration of big data frameworks using heterogeneous hardware
The ever-increasing demand for high performance Big Data analytics and data processing,
has paved the way for heterogeneous hardware accelerators, such as Graphics Processing …
has paved the way for heterogeneous hardware accelerators, such as Graphics Processing …
GPU-accelerated high-throughput online stream data processing
The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs)
makes them perfect for parallel processing of big data. In this paper, we present the design …
makes them perfect for parallel processing of big data. In this paper, we present the design …
G-Storm: GPU-enabled high-throughput online data processing in Storm
The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs)
makes them perfect for parallel processing of big data. In this paper, we present the design …
makes them perfect for parallel processing of big data. In this paper, we present the design …
A new GPU bundle adjustment method for large-scale data
M Zheng, S Zhou, X Xiong, J Zhu - … Engineering & Remote …, 2017 - ingentaconnect.com
We developed a fast and effective bundle adjustment method for large-scale datasets. The
preconditioned conjugate gradient (PCG) algorithm and GPU parallel computing technology …
preconditioned conjugate gradient (PCG) algorithm and GPU parallel computing technology …
Dynamic slot allocation technique for MapReduce clusters
MapReduce is a popular parallel computing paradigm for large-scale data processing in
clusters and data centers. However, the slot utilization can be low, especially when Hadoop …
clusters and data centers. However, the slot utilization can be low, especially when Hadoop …
Mrorder: Flexible job ordering optimization for online mapreduce workloads
MapReduce has become a widely used computing model for large-scale data processing in
clusters and data centers. A MapReduce workload generally contains multiple jobs. Due to …
clusters and data centers. A MapReduce workload generally contains multiple jobs. Due to …
Speedup for multi-level parallel computing
This paper studies the speedup for multi-level parallel computing. Two models of parallel
speedup are considered, namely, fixed-size speedup and fixed-time speedup. Based on …
speedup are considered, namely, fixed-size speedup and fixed-time speedup. Based on …
Gpu-accelerated cloud computing for data-intensive applications
Recently, many large-scale data-intensive applications have emerged from the Internet and
science domains. They pose significant challenges on the performance, scalability and …
science domains. They pose significant challenges on the performance, scalability and …