arXiv subjects

Ioannis Paraskevakos

Publications and source records attributed to Ioannis Paraskevakos.

7 recordsLinked to original sources

arXiv2026

The Simons Observatory: Commissioning of the Mid-Frequency Small Aperture Telescopes

The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment observing from an altitude of 5,200 m in the Atacama Desert in Chile. The SO Small Aperture Telescopes (SATs) will cover six spectral bands between 30 and 280 GHz primarily to search for evidence of primordial gravitational waves in the large angular scale $B$-modes of the CMB polarization. The first two Mid-Frequency (MF) SATs, operating in the 90 and 150 GHz frequency bands, began commissioning observations in late 2023, and we present the results of those observations here. Each MF SAT operates with approximately 9,400 biased transition-edge sensors (TES) at 1 mm of precipitable water vapor (PWV), representing a typical 80% yield. Planet observations confirm the telescope optics performance, calibrate the instrument response, and measure the end-to-end optical efficiency of the SATs. The realized instrument efficiencies are within the range expected based on instrument model predictions. In nominal observing conditions, the noise equivalent temperatures from both telescopes combined are $2.4~\mathrm{μK_{CMB}\sqrt{s}}$ at 90 GHz and $3.2~\mathrm{μK_{CMB}\sqrt{s}}$ at 150 GHz, substantially outperforming the baseline instantaneous sensitivity targets for the observatory.

astro-ph.IM↗

arXiv2020

Comparing Workflow Application Designs for High Resolution Satellite Image Analysis

Very High Resolution satellite and aerial imagery are used to monitor and conduct large scale surveys of ecological systems. Convolutional Neural Networks have successfully been employed to analyze such imagery to detect large animals and salient features. As the datasets increase in volume and number of images, utilizing High Performance Computing resources becomes necessary. In this paper, we investigate three task-parallel, data-driven workflow designs to support imagery analysis pipelines with heterogeneous tasks on HPC. We analyze the capabilities of each design when processing datasets from two use cases for a total of 4,672 satellite and aerial images, and 8.35 TB of data. We experimentally model the execution time of the tasks of the image processing pipelines. We perform experiments to characterize the resource utilization, total time to completion, and overheads of each design. Based on the model, overhead and utilization analysis, we show which design is best suited to scientific pipelines with similar characteristics.

cs.DC↗

arXiv2020

Parallel Performance of Molecular Dynamics Trajectory Analysis

The performance of biomolecular molecular dynamics simulations has steadily increased on modern high performance computing resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is becoming a bottleneck. To close this gap, we studied the performance of parallel trajectory analysis with MPI and the Python MDAnalysis library on three different XSEDE supercomputers where trajectories were read from a Lustre parallel file system. Strong scaling performance was impeded by stragglers, MPI processes that were slower than the typical process. Stragglers were less prevalent for compute-bound workloads, thus pointing to file reading as a bottleneck for scaling. However, a more complicated picture emerged in which both the computation and the data ingestion exhibited close to ideal strong scaling behavior whereas stragglers were primarily caused by either large MPI communication costs or long times to open the single shared trajectory file. We improved overall strong scaling performance by either subfiling (splitting the trajectory into separate files) or MPI-IO with Parallel HDF5 trajectory files. The parallel HDF5 approach resulted in near ideal strong scaling on up to 384 cores (16 nodes), thus reducing trajectory analysis times by two orders of magnitude compared to the serial approach.

cs.DC↗

arXiv2020

Workflow Design Analysis for High Resolution Satellite Image Analysis

Ecological sciences are using imagery from a variety of sources to monitor and survey populations and ecosystems. Very High Resolution (VHR) satellite imagery provide an effective dataset for large scale surveys. Convolutional Neural Networks have successfully been employed to analyze such imagery and detect large animals. As the datasets increase in volume, O(TB), and number of images, O(1k), utilizing High Performance Computing (HPC) resources becomes necessary. In this paper, we investigate a task-parallel data-driven workflows design to support imagery analysis pipelines with heterogeneous tasks on HPC. We analyze the capabilities of each design when processing a dataset of 3,000 VHR satellite images for a total of 4~TB. We experimentally model the execution time of the tasks of the image processing pipeline. We perform experiments to characterize the resource utilization, total time to completion, and overheads of each design. Based on the model, overhead and utilization analysis, we show which design approach to is best suited in scientific pipelines with similar characteristics.

cs.DC↗

arXiv2019

Middleware Building Blocks for Workflow Systems

This paper describes a building blocks approach to the design of scientific workflow systems. We discuss RADICAL-Cybertools as one implementation of the building blocks concept, showing how they are designed and developed in accordance with this approach. This paper offers three main contributions: (i) showing the relevance of the design principles underlying the building blocks approach to support scientific workflows on high performance computing platforms; (ii) illustrating a set of building blocks that enable multiple points of integration, "unifying" conceptual reasoning across otherwise very different tools and systems; and (iii) case studies discussing how RADICAL-Cybertools are integrated with existing workflow, workload, and general purpose computing systems and used to develop domain-specific workflow systems.

cs.DC↗

arXiv2018

Task-parallel Analysis of Molecular Dynamics Trajectories

Different parallel frameworks for implementing data analysis applications have been proposed by the HPC and Big Data communities. In this paper, we investigate three task-parallel frameworks: Spark, Dask and RADICAL-Pilot with respect to their ability to support data analytics on HPC resources and compare them with MPI. We investigate the data analysis requirements of Molecular Dynamics (MD) simulations which are significant consumers of supercomputing cycles, producing immense amounts of data. A typical large-scale MD simulation of a physical system of O(100k) atoms over μsecs can produce from O(10) GB to O(1000) GBs of data. We propose and evaluate different approaches for parallelization of a representative set of MD trajectory analysis algorithms, in particular the computation of path similarity and leaflet identification. We evaluate Spark, Dask and RADICAL-Pilot with respect to their abstractions and runtime engine capabilities to support these algorithms. We provide a conceptual basis for comparing and understanding different frameworks that enable users to select the optimal system for each application. We also provide a quantitative performance analysis of the different algorithms across the three frameworks.

cs.DC↗

arXiv2016

Hadoop on HPC: Integrating Hadoop and Pilot-based Dynamic Resource Management

High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications. Consequently, they have been architected as producers and not consumers of data. The Apache Hadoop ecosystem has evolved to meet the requirements of data processing applications and has addressed many of the limitations of HPC platforms. There exist a class of scientific applications however, that need the collective capabilities of traditional high-performance computing environments and the Apache Hadoop ecosystem. For example, the scientific domains of bio-molecular dynamics, genomics and network science need to couple traditional computing with Hadoop/Spark based analysis. We investigate the critical question of how to present the capabilities of both computing environments to such scientific applications. Whereas this questions needs answers at multiple levels, we focus on the design of resource management middleware that might support the needs of both. We propose extensions to the Pilot-Abstraction to provide a unifying resource management layer. This is an important step that allows applications to integrate HPC stages (e.g. simulations) to data analytics. Many supercomputing centers have started to officially support Hadoop environments, either in a dedicated environment or in hybrid deployments using tools such as myHadoop. This typically involves many intrinsic, environment-specific details that need to be mastered, and often swamp conceptual issues like: How best to couple HPC and Hadoop application stages? How to explore runtime trade-offs (data localities vs. data movement)? This paper provides both conceptual understanding and practical solutions to the integrated use of HPC and Hadoop environments.

cs.DC↗