Grid Engine is a Distributed Resource Manager (DRM), that manages the resources of distributed systems (such as Grid, HPC, or Cloud systems) and executes designated jobs which have requested to occupy or consume those resources. Grid Engine applies ...

Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocation, improving turnaround times and utilization of science gateways. ...

With the increase in data-driven analytics, the demand for high performing computing resources has risen. There are many high-performance computing centers providing cyberinfrastructure (CI) for academic research. However, there exists access barriers in ...

We present an open source extensible web framework for the analysis of different farm practices and programs and easy dissemination of their results to the users. Currently, this framework is being applied to two use cases --- a web-based decision ...

We are in the era of Spatial Big Data. Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity and heterogeneity. For example, ...

Diabetes mellitus (DM) is a major public health concern that requires continuing medical care. It is also a leading cause of other serious health complications associated with longer hospital stays and increased mortality rates. Fluctuation of blood ...

Science gateways are a mechanism for delivering scientific software as a service, especially when the software requires high performance computing (HPC) resources to run effectively. The existence of a science gateway eliminates the user's need to learn ...

The Science Gateways Community Institute (SGCI) serves the gateway community with free, NSF-funded resources, services, experts, and ideas. Our community shares expertise, technologies, and best practices for creating and sustaining gateways. Gateways ...

Science Gateways are virtual environments that accelerate scientific discovery by enabling scientific communities to more easily and effectively utilize distributed computing and data resources. Successful Science Gateways provide access to sophisticated ...

Science gateways are distributed computing systems that provide science-centric, end-user environments that simplify and expand the use of scientific software and data on diverse scientific software on backend resources. In this poster we describe the ...

Convolutional Neural Networks (CNNs) are one of the most extensively used tools in machine learning, but they are still not well understood and in many cases they are over-parameterized, leading to slow inference and impeding their deployment on low-...

Due to its non-invasive nature, diffusion-weighted MRI (dMRI) has shown promise as a method to quantify skeletal muscle's microstructure; however, connecting the dMRI signal of muscle to the underlying microstructure is difficult. Numerical models of ...

Edge devices and robots have access to an abundance of raw data that needs to be processed on the edge. Deep neural networks (DNNs) can help these devices understand and learn from this complex data; however, executing DNNs while achieving high ...

Containerization has taken the software world by storm. Deployment complications, like requiring elevated (i.e. "root") permissions to run, have slowed the adoption of containers in shared advanced research computing (ARC) environments. Singularity is a ...

MagmaDNN [17] is a deep learning framework driven using the highly optimized MAGMA dense linear algebra package. The library offers comparable performance to other popular frameworks, such as TensorFlow, PyTorch, and Theano. C++ is used to implement the ...

Datasets in the computer vision academic research community are primarily static. Once a dataset is accepted as a benchmark for a computer vision task, researchers working on this task will not alter it in order to make their results reproducible. At the ...

This study demonstrates application of convolutional neural networks (CNNs) for the analysis of a unique image analysis problem in fluorescence microscopy. We employed the U-Net CNN architecture and trained a model to segment nuclear regions in images of ...

High-Performance Computing (HPC) systems are resources utilized for data capture, sharing, and analysis. The majority of our HPC users come from other disciplines than Computer Science. HPC users including computer scientists have difficulties and do not ...

Research in life science domains is producing larger data sets that require the use of computational approaches to understand biological phenomena. Academic institutions, industry, and other sectors in the life sciences are creating jobs that involve ...

The purpose of this project was to analyze which image pre-processing technique was most beneficial in improving the performance of Facial Expression Recognition through Deep Learning and High-Performance Computing. Contrary to our expectations, the ...