Development of a Network Infrastructure for Heterogeneous Robot and Control Systems Interactions

2015 ◽  
Author(s):  
Christopher J. Reid ◽  
Biswanath Samanta ◽  
Christopher Kadlec
Keyword(s):  
Graphics Processing Units ◽  
Virtual Machines ◽  
Control Software ◽  
Network Infrastructure ◽  
Complex Tasks ◽  
Heterogeneous Robots ◽  
Robust Network ◽  
Computational Resources ◽  
Graphics Processing ◽  
And Control

The use of robots in complex tasks such as search and rescue operations is becoming more and more common. These robots often work independently with no cooperation with other robots or control software, and are very limited in their ability to perform dynamic tasks and interact with both humans and other robots. To this end, a system must be developed to facilitate the cooperation of heterogeneous robots to complete complex tasks. To model and study human-robot and robot-robot interactions in a multi-system environment, a robust network infrastructure must be implemented to support the broad nature of these studies. The work presented here details the creation of a cloud-based infrastructure designed to support the introduction and implementation of multiple heterogeneous robots to the environment utilizing the Robot Operating System (ROS). Implemented robots include both ground-based (e.g. Turtlebot) and air-based (e.g Parrot ARDrone2.0) systems. Additional hardware is also implemented, such as embedded vision systems, host computers to support virtual machines for software implementation, and machines with graphics processing units (GPUs) for additional computational resources. Control software for the robots is implemented in the system with complexities ranging from simple teleoperation to skeletal tracking and neural network simulators. A robust integration of multiple heterogeneous components, including both hardware and software, is achieved.

scholarly journals A GPU based multidimensional amplitude analysis to search for tetraquark candidates

2020 ◽  
Author(s):  
Nairit Sur ◽  
Leonardo Cristella ◽  
Adriano Di Florio ◽  
Vincenzo Mastrapasqua
Keyword(s):  
Graphics Processing Units ◽  
High Energy Physics ◽  
High Energy ◽  
Amplitude Analysis ◽  
Hadron Spectroscopy ◽  
Multiple Cores ◽  
Analysis Strategies ◽  
Computationally Intensive ◽  
Computational Resources ◽  
Graphics Processing

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

scholarly journals A GPU based multidimensional amplitude analysis to search for tetraquark candidates

2020 ◽  
Author(s):  
Nairit Sur ◽  
Leonardo Cristella ◽  
Adriano Di Florio ◽  
Vincenzo Mastrapasqua
Keyword(s):  
Graphics Processing Units ◽  
High Energy Physics ◽  
High Energy ◽  
Amplitude Analysis ◽  
Hadron Spectroscopy ◽  
Multiple Cores ◽  
Analysis Strategies ◽  
Computationally Intensive ◽  
Computational Resources ◽  
Graphics Processing

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with the aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

Massively Threaded Digital Forensics Tools

2013 ◽  
pp. 488-509
Author(s):  
Lodovico Marziale ◽  
Santhi Movva ◽  
Golden G. Richard ◽  
Vassil Roussev ◽  
Loren Schwiebert
Keyword(s):  
Intellectual Property ◽  
Network Security ◽  
Graphics Processing Units ◽  
Digital Forensics ◽  
Turnaround Time ◽  
Digital Evidence ◽  
Counter Terrorism ◽  
On Line ◽  
Computational Resources ◽  
Graphics Processing

Digital forensics comprises the set of techniques to recover, preserve, and examine digital evidence, and has applications in a number of important areas, including investigation of child exploitation, identity theft, counter-terrorism, and intellectual property disputes. Digital forensics tools must exhaustively examine and interpret data at a low level, because data of evidentiary value may have been deleted, partially overwritten, obfuscated, or corrupted. While forensics investigation is typically seen as an off-line activity, improving case turnaround time is crucial, because in many cases lives or livelihoods may hang in the balance. Furthermore, if more computational resources can be brought to bear, we believe that preventative network security (which must be performed on-line) and digital forensics can be merged into a common research focus. In this chapter we consider recent hardware trends and argue that multicore CPUs and Graphics Processing Units (GPUs) offer one solution to the problem of maximizing available compute resources.

scholarly journals A GPU Based Multidimensional Amplitude Analysis to Search for Tetraquark Candidates

2020 ◽  
Author(s):  
Nairit Sur ◽  
Leonardo Cristella ◽  
Adriano Di Florio ◽  
Vincenzo Mastrapasqua
Keyword(s):  
Graphics Processing Units ◽  
High Energy Physics ◽  
High Energy ◽  
Amplitude Analysis ◽  
Hadron Spectroscopy ◽  
Speed Up ◽  
Computationally Intensive ◽  
Quark Structure ◽  
Computational Resources ◽  
Graphics Processing

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data while physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with the aim to study the quark structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays that can also be seamlessly adapted for other similar analyses. The GooFit fitter running on GPUs shows a remarkable speed-up in the computing performance when compared to a ROOT/RooFit implementation of the same, running on multicore CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

scholarly journals A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification

2020 ◽  
Vol 12 (8) ◽  
pp. 1257 ◽  
Author(s):  
Mercedes E. Paoletti ◽  
Juan M. Haut ◽  
Xuanwen Tao ◽  
Javier Plaza Miguel ◽  
Antonio Plaza
Keyword(s):  
Graphics Processing Units ◽  
Memory Management ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Writing Instructions ◽  
Wide Range ◽  
Computational Resources ◽  
Graphics Processing ◽  
Efficient Memory ◽  
Gpu Implementation

The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels. Although HSIs offer many opportunities for accurately modeling and mapping the surface of the Earth in a wide range of applications, they comprise massive data cubes. These huge amounts of data impose important requirements from the storage and processing points of view. The support vector machine (SVM) has been one of the most powerful machine learning classifiers, able to process HSI data without applying previous feature extraction steps, exhibiting a robust behaviour with high dimensional data and obtaining high classification accuracies. Nevertheless, the training and prediction stages of this supervised classifier are very time-consuming, especially for large and complex problems that require an intensive use of memory and computational resources. This paper develops a new, highly efficient implementation of SVMs that exploits the high computational power of graphics processing units (GPUs) to reduce the execution time by massively parallelizing the operations of the algorithm while performing efficient memory management during data-reading and writing instructions. Our experiments, conducted over different HSI benchmarks, demonstrate the efficiency of our GPU implementation.

ASYMPTOTIC PEAK UTILISATION IN HETEROGENEOUS PARALLEL CPU/GPU PIPELINES: A DECENTRALISED QUEUE MONITORING STRATEGY

2012 ◽  
Vol 22 (02) ◽  
pp. 1240008 ◽  
Author(s):  
MICHAEL T. GARBA ◽  
HORACIO GONZÁLEZ–VÉLEZ
Keyword(s):  
Graphics Processing Units ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Numerical Linear Algebra ◽  
Processing Unit ◽  
Monitoring Strategy ◽  
Memory Transfer ◽  
Queue Monitoring ◽  
Computational Resources ◽  
Graphics Processing

Widespread heterogeneous parallelism is unavoidable given the emergence of General-Purpose computing on graphics processing units (GPGPU). The characteristics of a Graphics Processing Unit (GPU)—including significant memory transfer latency and complex performance characteristics—demand new approaches to ensuring that all available computational resources are efficiently utilised. This paper considers the simple case of a divisible workload based on widely-used numerical linear algebra routines and the challenges that prevent efficient use of all resources available to a naive SPMD application using the GPU as an accelerator. We suggest a possible queue monitoring strategy that facilitates resource usage with a view to balancing the CPU/GPU utilisation for applications that fit the pipeline parallel architectural pattern on heterogeneous multicore/multi-node CPU and GPU systems. We propose a stochastic allocation technique that may serve as a foundation for heuristic approaches to balancing CPU/GPU workloads.

scholarly journals Multi-Core Processing Cloud Eclat Growth

2019 ◽  
Vol 8 (6) ◽  
pp. 4063-4072
Keyword(s):  
Data Mining ◽  
Graphics Processing Units ◽  
High Performance ◽  
Virtual Machines ◽  
Cloud Services ◽  
Standard Data ◽  
Central Processing ◽  
Data Mining Algorithms ◽  
Graphics Processing ◽  
Mining Algorithms

Data mining is a lively process used in many leading technologies of this information era. Eclat growth is one of the best performance data mining algorithms. This work is indented to create a suave interface for Eclat growth algorithm to run in multi-core processor-based cloud computing environments. Recent improvements in processor manufacturing technology make it possible to create multi-core high performance Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Many cloud services are already providing accessibility to these high-power processor virtual machines. The process of blending these technologies with Eclat Growth is proposed here in the name of “Multi-core Processing Cloud Eclat Growth” (MPCEG) to achieve higher processing speeds without compromising the standard data mining metrics such as Accuracy, Precision, Recall and F1-Score. New procedures for Cloud Parallel Processing, GPU Utilization, Annihilation of floating point arithmetic errors by fixed point replacement in GPUs and Hierarchical offloading aggregation are introduced in the construction process of proposed MPCEG

GPGPU as a Service

2016 ◽  
pp. 281-313
Author(s):  
Javier Prades ◽  
Fernando Campos ◽  
Carlos Reaño ◽  
Federico Silla
Keyword(s):  
Graphics Processing Units ◽  
Data Centers ◽  
Virtual Machines ◽  
General Purpose ◽  
Current Data ◽  
Depth Analysis ◽  
Equipment Acquisition ◽  
Acquisition Costs ◽  
Gpu Virtualization ◽  
Graphics Processing

Current data centers leverage virtual machines (VMs) in order to efficiently use hardware resources. VMs allow reducing equipment acquisition costs as well as decreasing overall energy consumption. However, although VMs have noticeably evolved to make a smart use of the underlying hardware, the use of GPUs (Graphics Processing Units) for General Purpose computing (GPGPU) is still not efficiently supported. This concern might be addressed by remote GPU virtualization solutions, which may provide VMs with GPUs located in a remote node, detached from the host where the VMs are being executed. This chapter presents an in-depth analysis about how to provide GPU access to applications running inside VMs. This analysis is complemented with experimental results which show that the use of remote GPU virtualization is an effective mechanism to provide GPU access to applications with negligible overheads. Finally, the approach is presented in the context of cloud federations for providing GPGPU as a Service.

Massively Threaded Digital Forensics Tools

2010 ◽  
pp. 234-256
Author(s):  
Lodovico Marziale ◽  
Santhi Movva ◽  
Golden G. Richard III ◽  
Vassil Roussev ◽  
Loren Schwiebert
Keyword(s):  
Intellectual Property ◽  
Network Security ◽  
Graphics Processing Units ◽  
Digital Forensics ◽  
Turnaround Time ◽  
Digital Evidence ◽  
Counter Terrorism ◽  
On Line ◽  
Computational Resources ◽  
Graphics Processing

Digital forensics comprises the set of techniques to recover, preserve, and examine digital evidence, and has applications in a number of important areas, including investigation of child exploitation, identity theft, counter-terrorism, and intellectual property disputes. Digital forensics tools must exhaustively examine and interpret data at a low level, because data of evidentiary value may have been deleted, partially overwritten, obfuscated, or corrupted. While forensics investigation is typically seen as an off-line activity, improving case turnaround time is crucial, because in many cases lives or livelihoods may hang in the balance. Furthermore, if more computational resources can be brought to bear, we believe that preventative network security (which must be performed on-line) and digital forensics can be merged into a common research focus. In this chapter we consider recent hardware trends and argue that multicore CPUs and Graphics Processing Units (GPUs) offer one solution to the problem of maximizing available compute resources.

scholarly journals Improving GPU Performance with a Power-Aware Streaming Multiprocessor Allocation Methodology

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1451
Author(s):  
Zois-Gerasimos Tasoulas ◽  
Iraklis Anagnostopoulos
Keyword(s):  
Graphics Processing Units ◽  
Power Efficiency ◽  
General Purpose ◽  
Efficient Computation ◽  
Time Sharing ◽  
Power Efficient ◽  
And Performance ◽  
Computational Resources ◽  
Graphics Processing ◽  
Concurrent Applications

Graphics processing units (GPUs) are extensively used as accelerators across multiple application domains, ranging from general purpose applications to neural networks, and cryptocurrency mining. The initial utilization paradigm for GPUs was one application accessing all the resources of the GPU. In recent years, time sharing is broadly used among applications of a GPU, nevertheless, spatial sharing is not fully explored. When concurrent applications share the computational resources of a GPU, performance can be improved by eliminating idle resources. Additionally, the incorporation of GPUs in embedded and mobile devices increases the demand for power efficient computation due to battery limitations. In this article, we present an allocation methodology for streaming multiprocessors (SMs). The presented methodology works for two concurrent applications on a GPU and determines an allocation scheme that will provide power efficient application execution, combined with improved GPU performance. Experimental results show that the developed methodology yields higher throughput while achieving improved power efficiency, compared to other SM power-aware and performance-aware policies. If the presented methodology is adopted, it will lead to higher performance of applications that are concurrently executing on a GPU. This will lead to a faster and more efficient acceleration of execution, even for devices with restrained energy sources.

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