scholarly journals CUDA-ACCELERATED FEATURE SELECTION

2020 ◽  
Author(s):  
Sterling Ramroach ◽  
Jonathan Herbert ◽  
Ajay Joshi
Keyword(s):  
High Performance ◽  
Graphics Processing Unit ◽  
Pearson Correlation ◽  
High Dimensional ◽  
Processing Unit ◽  
Device Architecture ◽  
Importance Ranking ◽  
Using Data ◽  
Graphics Processing ◽  
Performance Computing

Identifying important features from high dimensional data is usually done using one-dimensional filtering techniques. These techniques discard noisy attributes and those that are constant throughout the data. This is a time-consuming task that has scope for acceleration via high performance computing techniques involving the graphics processing unit (GPU). The proposed algorithm involves acceleration via the Compute Unified Device Architecture (CUDA) framework developed by Nvidia. This framework facilitates the seamless scaling of computation on any CUDA-enabled GPUs. Thus, the Pearson Correlation Coefficient can be applied in parallel on each feature with respect to the response variable. The ranks obtained for each feature can be used to determine the most relevant features to select. Using data from the UCI Machine Learning Repository, our results show an increase in efficiency for multi-dimensional analysis with a more reliable feature importance ranking. When tested on a high-dimensional dataset of 1000 samples and 10,000 features, we achieved a 1,230-time speedup using CUDA. This acceleration grows exponentially, as with any embarrassingly parallel task.

scholarly journals GPU Computing with Python: Performance, Energy Efficiency and Usability

Computation ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Håvard H. Holm ◽  
André R. Brodtkorb ◽  
Martin L. Sætra
Keyword(s):  
Energy Efficiency ◽  
High Performance ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Device Architecture ◽  
Computational Performance ◽  
Graphics Processing ◽  
The Impact ◽  
Performance Computing

In this work, we examine the performance, energy efficiency, and usability when using Python for developing high-performance computing codes running on the graphics processing unit (GPU). We investigate the portability of performance and energy efficiency between Compute Unified Device Architecture (CUDA) and Open Compute Language (OpenCL); between GPU generations; and between low-end, mid-range, and high-end GPUs. Our findings showed that the impact of using Python is negligible for our applications, and furthermore, CUDA and OpenCL applications tuned to an equivalent level can in many cases obtain the same computational performance. Our experiments showed that performance in general varies more between different GPUs than between using CUDA and OpenCL. We also show that tuning for performance is a good way of tuning for energy efficiency, but that specific tuning is needed to obtain optimal energy efficiency.

scholarly journals Splotch

2016 ◽  
Vol 31 (6) ◽  
pp. 550-563
Author(s):  
Timothy Dykes ◽  
Claudio Gheller ◽  
Marzia Rivi ◽  
Mel Krokos
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Xeon Phi ◽  
The Many ◽  
Many Core ◽  
Performance Results ◽  
Graphics Processing ◽  
Performance Computing

With the increasing size and complexity of data produced by large-scale numerical simulations, it is of primary importance for scientists to be able to exploit all available hardware in heterogenous high-performance computing environments for increased throughput and efficiency. We focus on the porting and optimization of Splotch, a scalable visualization algorithm, to utilize the Xeon Phi, Intel’s coprocessor based upon the new many integrated core architecture. We discuss steps taken to offload data to the coprocessor and algorithmic modifications to aid faster processing on the many-core architecture and make use of the uniquely wide vector capabilities of the device, with accompanying performance results using multiple Xeon Phi. Finally we compare performance against results achieved with the Graphics Processing Unit (GPU) based implementation of Splotch.

Optimization of K-Means Clustering on Graphics Processing Unit Using Compute Unified Device Architecture

2017 ◽  
Vol 14 (1) ◽  
pp. 789-795
Author(s):  
V Saveetha ◽  
S Sophia
Keyword(s):  
High Performance ◽  
Programming Model ◽  
Graphics Processing Unit ◽  
Direct Access ◽  
Communication Overhead ◽  
Processing Unit ◽  
Compute Unified Device Architecture ◽  
Central Processing ◽  
Device Architecture ◽  
Graphics Processing

Parallel data clustering aims at using algorithms and methods to extract knowledge from fat databases in rational time using high performance architectures. The computational challenge faced by cluster analysis due to increasing capacity of data can be overcome by exploiting the power of these architectures. The recent development in parallel power of Graphics Processing Unit enables low cost high performance solutions for general purpose applications. The Compute Unified Device Architecture programming model provides application programming interface methods to handle data proficiently on Graphics Processing Unit for iterative clustering algorithms like K-Means. The existing Graphics Processing Unit based K-Means algorithms highly focus on improvising the speedup of the algorithms and fall short to handle the high time spent on transfer of data between the Central Processing Unit and Graphics Processing Unit. A competent K-Means algorithm is proposed in this paper to lessen the transfer time by introducing a novel approach to check the convergence of the algorithm and utilize the pinned memory for direct access. This algorithm outperforms the other algorithms by maximizing parallelism and utilizing the memory features. The relative speedups and the validity measure for the proposed algorithm is elevated when compared with K-Means on Graphics Processing Unit and K-Means using Flag on Graphics Processing Unit. Thus the planned approach proves that communication overhead can be reduced in K-Means clustering.

scholarly journals HYPERDOCK: Improving virtual screening through parallel hyperheuristics

2019 ◽  
Vol 34 (1) ◽  
pp. 30-41
Author(s):  
Baldomero Imbernón ◽  
Antonio Llanes ◽  
José-Matías Cutillas-Lozano ◽  
Domingo Giménez
Keyword(s):  
Virtual Screening ◽  
High Performance ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Central Processing ◽  
Pharmacological Targets ◽  
Graphics Processing ◽  
Computational Systems ◽  
Performance Computing ◽  
Different Levels

Virtual screening (VS) methods aid clinical research by predicting the interaction of ligands with pharmacological targets. The computational requirements of VS, along with the size of the databases, propitiate the use of high-performance computing. METADOCK is a tool for the application of metaheuristics to VS in heterogeneous clusters of computers based on central processing unit (CPU) and graphics processing unit (GPU). HYPERDOCK represents a step forward; the exploration for satisfactory metaheuristics is systematically approached by means of hyperheuristics working on top of the metaheuristic schema of METADOCK. Multiple metaheuristics are explored, so the process is computationally demanding. HYPERDOCK exploits the parallelism of METADOCK and includes parallelism at its own level. The different levels of parallelism can be used to exploit the parallelism offered by computational systems composed of multicore CPU + multi-GPUs. The efficient exploitation of these systems enables HYPERDOCK to improve ligand–receptor binding.

scholarly journals Real-time Visualisation and Analysis of Tera-scale Datasets

2012 ◽  
Vol 10 (H16) ◽  
pp. 679-680
Author(s):  
Christopher J. Fluke
Keyword(s):  
Real Time ◽  
High Performance ◽  
Graphics Processing Unit ◽  
Low Cost ◽  
Processing Unit ◽  
Computing Environments ◽  
Graphics Processing ◽  
Interactive Visualisation ◽  
Performance Computing ◽  
Scale Data

AbstractAs we move ever closer to the Square Kilometre Array era, support for real-time, interactive visualisation and analysis of tera-scale (and beyond) data cubes will be crucial for on-going knowledge discovery. However, the data-on-the-desktop approach to analysis and visualisation that most astronomers are comfortable with will no longer be feasible: tera-scale data volumes exceed the memory and processing capabilities of standard desktop computing environments. Instead, there will be an increasing need for astronomers to utilise remote high performance computing (HPC) resources. In recent years, the graphics processing unit (GPU) has emerged as a credible, low cost option for HPC. A growing number of supercomputing centres are now investing heavily in GPU technologies to provide O(100) Teraflop/s processing. I describe how a GPU-powered computing cluster allows us to overcome the analysis and visualisation challenges of tera-scale data. With a GPU-based architecture, we have moved the bottleneck from processing-limited to bandwidth-limited, achieving exceptional real-time performance for common visualisation and data analysis tasks.

scholarly journals High Performance Computing Environment using General Purpose Computations on Graphics Processing Unit

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Andreas Widjaja ◽  
Tjatur Kandaga Gautama ◽  
Sendy Ferdian Sujadi ◽  
Steven Rumanto Harnandy
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Superior Performance ◽  
Processing Unit ◽  
Development Phase ◽  
And Performance ◽  
Graphics Processing ◽  
Performance Computing

Here a report of a development phase of an environment of high performance computing (HPC) using general purpose computations on the graphics processing unit (GPGPU) is presented. The HPC environment accommodates computational tasks which demand massive parallelisms or multi-threaded computations. For this purpose, GPGPU is utilized because such tasks require many computing cores running in parallel. The development phase consists of several stages, followed by testing its capabilities and performance. For starters, the HPC environment will be served for computational projects of students and members of the Faculty of Information Technology, Universitas Kristen Maranatha. The goal of this paper is to show a design of a HPC which is capable of running complex and multi-threaded computations. The test results of the HPC show that the GPGPU numerical computations have superior performance than the CPU, with the same level of precision.

scholarly journals A lightweight approach to performance portability with targetDP

2016 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Alan Gray ◽  
Kevin Stratford
Keyword(s):  
Particle Physics ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Message Passing Interface ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Performance Portability ◽  
Graphics Processing

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

scholarly journals Embedded GPU Implementation for High-Performance Ultrasound Imaging

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 884
Author(s):  
Stefano Rossi ◽  
Enrico Boni
Keyword(s):  
High Performance ◽  
Graphics Processing Unit ◽  
Digital Signal ◽  
Processing Unit ◽  
Embedded Computing ◽  
Field Programmable ◽  
Peripheral Component Interconnect ◽  
Programmable Gate Arrays ◽  
Graphics Processing ◽  
Signal Processors

Methods of increasing complexity are currently being proposed for ultrasound (US) echographic signal processing. Graphics Processing Unit (GPU) resources allowing massive exploitation of parallel computing are ideal candidates for these tasks. Many high-performance US instruments, including open scanners like ULA-OP 256, have an architecture based only on Field-Programmable Gate Arrays (FPGAs) and/or Digital Signal Processors (DSPs). This paper proposes the implementation of the embedded NVIDIA Jetson Xavier AGX module on board ULA-OP 256. The system architecture was revised to allow the introduction of a new Peripheral Component Interconnect Express (PCIe) communication channel, while maintaining backward compatibility with all other embedded computing resources already on board. Moreover, the Input/Output (I/O) peripherals of the module make the ultrasound system independent, freeing the user from the need to use an external controlling PC.

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