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IoT devices trigger real-time applications by receiving data from their vicinity. Modeling these applications in the form of workflows enables automating their procedure, especially for the business and industry. Depending on the features…
Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a…
The advances in data, computing and networking over the last two decades led to a shift in many application domains that includes machine learning on big data as a part of the scientific process, requiring new capabilities for integrated…
We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware. Our approach…
During the first observation run the LIGO collaboration needed to offload some of its most, intense CPU workflows from its dedicated computing sites to opportunistic resources. Open Science Grid enabled LIGO to run PyCbC, RIFT and Bayeswave…
Many real-world scientific workflows can be represented by a Directed Acyclic Graph (DAG), where each node represents a task and a directed edge signifies a dependency between two tasks. Due to the increasing computational resource…
HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and rarely is built with security in mind. In this paper we…
Trusting simulation output is crucial for Sandia's mission objectives. We rely on these simulations to perform our high-consequence mission tasks given national treaty obligations. Other science and modeling applications, while they may…
Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase…
In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are…
Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science…
Scientific computing sometimes involves computation on sensitive data. Depending on the data and the execution environment, the HPC (high-performance computing) user or data provider may require confidentiality and/or integrity guarantees.…
Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination…
In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of…
In complex data analyses it is increasingly important to capture information about the usage of data sets in addition to their preservation over time to ensure reproducibility of results, to verify the work of others and to ensure…
(An updated version of this manuscript has been accepted to Scientific Reports in 2016, please refer to http://www.nature.com/articles/srep31900) The highly anticipated transition from next generation sequencing (NGS) to third generation…
Collecting flow records is a common practice of network operators and researchers for monitoring, diagnosing and understanding a network. Traditional tools like NetFlow face great challenges when both the speed and the complexity of the…
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread…
In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing…
Recent advancements in detector technology have significantly increased the size and complexity of experimental data, and high-performance computing (HPC) provides a path towards more efficient and timely data processing. However, movement…