Efficient parallel implementation of a density peaks clustering algorithm on graphics processing unit

2017 ◽  
Vol 18 (7) ◽  
pp. 915-927 ◽  
Author(s):  
Ke-shi Ge ◽  
Hua-you Su ◽  
Dong-sheng Li ◽  
Xi-cheng Lu
Keyword(s):  
Clustering Algorithm ◽  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Processing Unit ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Graphics Processing

AN EVALUATION OF MULTIPLE FEED-FORWARD NETWORKS ON GPUs

2011 ◽  
Vol 21 (01) ◽  
pp. 31-47 ◽  
Author(s):  
NOEL LOPES ◽  
BERNARDETE RIBEIRO
Keyword(s):  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Low Cost ◽  
Back Propagation ◽  
General Purpose ◽  
Training System ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Data Parallel ◽  
Graphics Processing

The Graphics Processing Unit (GPU) originally designed for rendering graphics and which is difficult to program for other tasks, has since evolved into a device suitable for general-purpose computations. As a result graphics hardware has become progressively more attractive yielding unprecedented performance at a relatively low cost. Thus, it is the ideal candidate to accelerate a wide variety of data parallel tasks in many fields such as in Machine Learning (ML). As problems become more and more demanding, parallel implementations of learning algorithms are crucial for a useful application. In particular, the implementation of Neural Networks (NNs) in GPUs can significantly reduce the long training times during the learning process. In this paper we present a GPU parallel implementation of the Back-Propagation (BP) and Multiple Back-Propagation (MBP) algorithms, and describe the GPU kernels needed for this task. The results obtained on well-known benchmarks show faster training times and improved performances as compared to the implementation in traditional hardware, due to maximized floating-point throughput and memory bandwidth. Moreover, a preliminary GPU based Autonomous Training System (ATS) is developed which aims at automatically finding high-quality NNs-based solutions for a given problem.

scholarly journals Parallel Implementation of Density Peaks Clustering Algorithm Based on Spark

2017 ◽  
Vol 107 ◽  
pp. 442-447 ◽  
Author(s):  
Rui Liu ◽  
Xiaoge Li ◽  
Liping Du ◽  
Shuting Zhi ◽  
Mian Wei
Keyword(s):  
Clustering Algorithm ◽  
Parallel Implementation ◽  
Density Peaks ◽  
Density Peaks Clustering

scholarly journals Ray-based modeling and imaging in viscoelastic media using graphics processing units

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. S425-S436
Author(s):  
Martin Sarajaervi ◽  
Henk Keers
Keyword(s):  
Seismic Data ◽  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Processing Unit ◽  
Central Processing ◽  
Imaging Results ◽  
Viscoelastic Modeling ◽  
Graphics Processing ◽  
Complex Valued

In seismic data processing, the amplitude loss caused by attenuation should be taken into account. The basis for this is provided by a 3D attenuation model described by the quality factor [Formula: see text], which is used in viscoelastic modeling and imaging. We have accomplished viscoelastic modeling and imaging using ray theory and the ray-Born approximation. This makes it possible to take [Formula: see text] into account using complex-valued and frequency-dependent traveltimes. We have developed a unified parallel implementation for modeling and imaging in the frequency domain and carried out the numerical integration on a graphics processing unit. A central part of the implementation is an efficient technique for computing large integrals. We applied the integration method to the 3D SEG/EAGE overthrust model to generate synthetic seismograms and imaging results. The attenuation effects are accurately modeled in the seismograms and compensated for in the imaging algorithm. The results indicate a significant improvement in computational efficiency compared to a parallel central processing unit baseline.

HARNESSING THE POWER OF IDLE GPUS FOR ACCELERATION OF BIOLOGICAL SEQUENCE ALIGNMENT

2009 ◽  
Vol 19 (04) ◽  
pp. 513-533 ◽  
Author(s):  
FUMIHIKO INO ◽  
YUKI KOTANI ◽  
YUMA MUNEKAWA ◽  
KENICHI HAGIHARA
Keyword(s):  
Sequence Alignment ◽  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Processing Unit ◽  
Compute Unified Device Architecture ◽  
Grid System ◽  
Biological Sequence ◽  
Device Architecture ◽  
Linear Speedup ◽  
Graphics Processing

This paper presents a parallel system capable of accelerating biological sequence alignment on the graphics processing unit (GPU) grid. The GPU grid in this paper is a desktop grid system that utilizes idle GPUs and CPUs in the office and home. Our parallel implementation employs a master-worker paradigm to accelerate an OpenGL-based algorithm that runs on a single GPU. We integrate this implementation into a screensaver-based grid system that detects idle resources on which the alignment code can run. We also show some experimental results comparing our implementation with three different implementations running on a single GPU, a single CPU, or multiple CPUs. As a result, we find that a single non-dedicated GPU can provide us almost the same throughput as two dedicated CPUs in our laboratory environment, where GPU-equipped machines are ordinarily used to develop GPU applications. In a dedicated environment, the GPU-accelerated code achieves five times higher throughput than the CPU-based code. Furthermore, a linear speedup of 30.7X is observed on a 32-node cluster of dedicated GPUs. We also implement a compute unified device architecture (CUDA) based algorithm to demonstrate further acceleration.

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