Automating environmental computing applications with scientific workflows

Computational environmental science applications have evolved and become more complex over the last decade. In order to cope with the needs of such applications, computational methods and technologies have emerged to support the execution of these applications on heterogeneous, distributed systems. Among them are workflow management systems such as Pegasus. Pegasus is being used by researchers to model seismic wave propagation, to discover new celestial objects, to study RNA critical to human brain development, and to investigate other important research questions. This paper provides an introduction to scientific workflows and describes Pegasus and its main features. The paper highlights how the environmental science community has used Pegasus to automate their scientific workflow executions on high performance and high throughput computing systems by presenting three use cases: two Earth science workflows, and a climate science workflow.</p

Automating environmental computing applications with scientific workflows

Computational environmental science applications have evolved and become more complex over the last decade. In order to cope with the needs of such applications, computational methods and technologies have emerged to support the execution of these applications on heterogeneous, distributed systems. Among them are workflow management systems such as Pegasus. Pegasus is being used by researchers to model seismic wave propagation, to discover new celestial objects, to study RNA critical to human brain development, and to investigate other important research questions. This paper provides an introduction to scientific workflows and describes Pegasus and its main features. The paper highlights how the environmental science community has used Pegasus to automate their scientific workflow executions on high performance and high throughput computing systems by presenting three use cases: two Earth science workflows, and a climate science workflow.</p

Enabling End-to-end Experiment Sharing and Reuse with Workflows via Jupyter Notebooks

Scientific workflows are a mainstream solution to process large-scale modeling, simulations, and data analytics computations in distributed systems, and have supported traditional and breakthrough researches across several domains. While scientific workflows have enabled large-scale scientific computations and data analysis, and lowered the barriers for experiment sharing, preservation (including provenance), and reuse between heterogeneous platforms (HTC and HPC), the reproducibility of an end-to-end scientific experiment is hindered by the lack of methodologies to capture pre- and post-analysis (or steps) performed out of the scope of the workflow execution. Online notebook technologies (e.g., Jupyter Notebook) emerged as an open-source web application that allows scientists to create and share documents that contain live code, equations, visualizations and explanatory text. Jupyter Notebooks has a strong potential to reduce the gap between researchers and the complex knowledge required to run large-scale scientific workflows via a programmatic high-level interface to access/manage workflow capabilities. This poster describes our approach for integrating the Pegasus workflow management system with Jupyter to foster easiness of usage, reproducibility (all the information to run an experiment is in a unique place), and reuse (notebooks are portable if running in equivalent environments). Since Pegasus 4.8, a Python API to declare and manage Pegasus workflows via Jupyter has been provided. The user can create a notebook and declare a workflow application using the Pegasus DAX API – allows the scientists to specify data or control dependencies between computational jobs. This API encapsulates most of Pegasus commands (e.g., plan, run, statistics, among others), and also allows workflow creation, execution, and monitoring. Additionally, the API also provides mechanisms to define Pegasus catalogs (sites, replica, and transformation), as well as to generate tutorial example workflows

CNL and aCML should be considered as single entity based on molecular profiles and outcomes

Chronic neutrophilic leukemia (CNL) and atypical chronic myeloid leukemia (aCML) are rare myeloid disorders that are challenging with regard to diagnosis and clinical management. To study the similarities and differences of these disorders we undertook a multi-center international study of one of the largest case series (CNL, n=24; aCML, n=37 cases, respectively), focusing on the clinical and mutational profiles (n=53 with molecular data) of these diseases. We found no differences in clinical presentation or outcomes between both entities. As previously described, both CNL and aCML share a complex mutational profile with mutations in genes involved in epigenetic regulation, splicing and signaling pathways. Apart from CSF3R, only EZH2 and TET2 were differentially mutated between them. The molecular profiles support the notion of CNL and aCML being a continuum of the same disease that may fit best within the myelodysplastic/myeloproliferative neoplasms (MDS/MPN). We identified four high-risk mutated genes, specifically CEBPA (β=2.26, HR=9.54, p=0.003), EZH2 (β=1.12, HR=3.062, p=0.009), NRAS (β=1.29, HR=3.63, p=0.048) and U2AF1 (β=1.75, HR=5.74, p=0.013) by multivariate analysis. Our findings underscore the relevance of molecular-risk classification in CNL/aCML as well as the importance of CSF3R mutations in these diseases.</p

Search for intermediate mass black hole binaries in the first observing run of Advanced LIGO

International audienceDuring their first observational run, the two Advanced LIGO detectors attained an unprecedented sensitivity, resulting in the first direct detections of gravitational-wave signals produced by stellar-mass binary black hole systems. This paper reports on an all-sky search for gravitational waves (GWs) from merging intermediate mass black hole binaries (IMBHBs). The combined results from two independent search techniques were used in this study: the first employs a matched-filter algorithm that uses a bank of filters covering the GW signal parameter space, while the second is a generic search for GW transients (bursts). No GWs from IMBHBs were detected; therefore, we constrain the rate of several classes of IMBHB mergers. The most stringent limit is obtained for black holes of individual mass 100  M⊙, with spins aligned with the binary orbital angular momentum. For such systems, the merger rate is constrained to be less than 0.93  Gpc−3 yr−1 in comoving units at the 90% confidence level, an improvement of nearly 2 orders of magnitude over previous upper limits

First low-frequency Einstein@Home all-sky search for continuous gravitational waves in Advanced LIGO data

International audienceWe report results of a deep all-sky search for periodic gravitational waves from isolated neutron stars in data from the first Advanced LIGO observing run. This search investigates the low frequency range of Advanced LIGO data, between 20 and 100 Hz, much of which was not explored in initial LIGO. The search was made possible by the computing power provided by the volunteers of the Einstein@Home project. We find no significant signal candidate and set the most stringent upper limits to date on the amplitude of gravitational wave signals from the target population, corresponding to a sensitivity depth of 48.7  [1/Hz]. At the frequency of best strain sensitivity, near 100 Hz, we set 90% confidence upper limits of 1.8×10-25. At the low end of our frequency range, 20 Hz, we achieve upper limits of 3.9×10-24. At 55 Hz we can exclude sources with ellipticities greater than 10-5 within 100 pc of Earth with fiducial value of the principal moment of inertia of 1038  kg m2