Algebraic computations in derived categories

AbstractThis paper presents explicit algorithms for computations over a finite subspectroid of the bounded derived category of a finite spectroid. We will demonstrate methods for the construction of a projective resolution of a module and for finding the quiver of a finite spectroid given in terms of its radical spaces. This enables us to compute the endomorphism algebra of a tilting complex – or, in fact, any finite complex – in the derived category. In order to carry out these computations, we have to restrict to a finite base field or the field of rational numbers. We will show that it is possible to transfer the results to any extension of the base field, in particular to the algebraic closure

Dispel4Py: A Python Framework for Data-intensive eScience

We present dispel4py, a novel data intensive and high performance computing middleware provided as a standard Python library for describing stream-based workows. It allows its users to develop their scientific applications locally and then run them on a wide range of HPC-infrastructures without any changes to the code. Moreover, it provides automated and efficient parallel mappings toMPI, multiprocessing, Storm and Spark frameworks, commonly used in big data applications. It builds on the wide availability of Python in many environments and only requires familiarity with basic Python syntax. We will show the dispel4py advantages by walking through an example. We will conclude demonstrating how dispel4py can be employed as an easy-to-use tool for designing scientific applications using real-world scenarios.</p

Communicating absolute fracture risk reduction and the acceptance of treatment for osteoporosis

Healthcare professionals frequently communicate the benefits of treatments as a relative risk reduction (RRR) in the likelihood of an event occurring. Here we evaluated whether presenting the benefits of osteoporosis treatment as a RRR in fractures compared with an absolute risk reduction (ARR) changed the patient’s attitudes towards accepting treatment. We surveyed 160 individuals attending a specialised osteoporosis clinic for face-to-face consultations between May 2018 and Jan 2021. They were presented with information on RRR for the treatment being considered followed by ARR and after each question were asked about how likely they would be to start treatment on a 5-point scale (1 = very likely, 5 = very unlikely). Participants were less likely to accept treatment when it was presented as ARR (mean score 2.02 vs. 2.67, p < 0.001, 95% CI for difference − 0.82 vs − 0.47) and thirty-eight participants (23.7%) declined treatment with knowledge of their ARR when they would have accepted the same treatment based on the RRR. Individuals who declined treatment had a lower 5-year risk of fracture than those who accepted treatment (9.0 vs. 12.5%, p < 0.001, 95% CI − 5.0 to − 1.6) and as fracture risk decreased, the participant was less likely to accept treatment (Spearman r − 0.32, 95% CI − 0.46 to − 0.17, p ≤ 0.001). Whilst presentation of data as ARR more accurately reflects individual benefit and helps facilitate shared decision-making, clinicians should be aware that this will lead to a proportion of patients with lower fracture risk declining treatment for osteoporosis

Data-Intensive architecture for scientific knowledge discovery

This paper presents a data-intensive architecture that demonstrates the ability to support applications from a wide range of application domains, and support the different types of users involved in defining, designing and executing data-intensive processing tasks. The prototype architecture is introduced, and the pivotal role of DISPEL as a canonical language is explained. The architecture promotes the exploration and exploitation of distributed and heterogeneous data and spans the complete knowledge discovery process, from data preparation, to analysis, to evaluation and reiteration. The architecture evaluation included large-scale applications from astronomy, cosmology, hydrology, functional genetics, imaging processing and seismology

Comprehensible Control for Researchers and Developers facing Data Challenges

The DARE platform enables researchers and their developers to exploit more capabilities to handle complexity and scale in data, computation and collaboration. Today’s challenges pose increasing and urgent demands for this combination of capabilities. To meet technical, economic and governance constraints, application communities must use use shared digital infrastructure principally via virtualisation and mapping. This requires precise abstractions that retain their meaning while their implementations and infrastructures change. Giving specialists direct control over these capabilities with detail relevant to each discipline is necessary for adoption. Research agility, improved power and retained return on intellectual investment incentivise that adoption. We report on an architecture for establishing and sustaining the necessary optimised mappings and early evaluations of its feasibility with two application communities.PublishedSan Diego (CA, USA)3IT. Calcolo scientific

dispel4py: An Open-Source Python library for Data-Intensive Seismology

Scientific workflows are a necessary tool for many scientific communities as they enable easy composition and execution of applications on computing resources while scientists can focus on their research without being distracted by the computation management. Nowadays, scientific communities (e.g. Seismology) have access to a large variety of computing resources and their computational problems are best addressed using parallel computing technology. However, successful use of these technologies requires a lot of additional machinery whose use is not straightforward for non-experts: different parallel frameworks (MPI, Storm, multiprocessing, etc.) must be used depending on the computing resources (local machines, grids, clouds, clusters) where applications are run. This implies that for achieving the best applications' performance, users usually have to change their codes depending on the features of the platform selected for running them. This work presents dispel4py, a new open-source Python library for describing abstract stream-based workflows for distributed data-intensive applications. Special care has been taken to provide dispel4py with the ability to map abstract workflows to different platforms dynamically at run-time. Currently dispel4py has four mappings: Apache Storm, MPI, multi-threading and sequential. The main goal of dispel4py is to provide an easy-to-use tool to develop and test workflows in local resources by using the sequential mode with a small dataset. Later, once a workflow is ready for long runs, it can be automatically executed on different parallel resources. dispel4py takes care of the underlying mappings by performing an efficient parallelisation. Processing Elements (PE) represent the basic computational activities of any dispel4Py workflow, which can be a seismologic algorithm, or a data transformation process. For creating a dispel4py workflow, users only have to write very few lines of code to describe their PEs and how they are connected by using Python, which is widely supported on many platforms and is popular in many scientific domains, such as in geosciences. Once, a dispel4py workflow is written, a user only has to select which mapping they would like to use, and everything else (parallelisation, distribution of data) is carried on by dispel4py without any cost to the user. Among all dispel4py features we would like to highlight the following: * The PEs are connected by streams and not by writing to and reading from intermediate files, avoiding many IO operations. * The PEs can be stored into a registry. Therefore, different users can recombine PEs in many different workflows. * dispel4py has been enriched with a provenance mechanism to support runtime provenance analysis. We have adopted the W3C-PROV data model, which is accessible via a prototypal browser-based user interface and a web API. It supports the users with the visualisation of graphical products and offers combined operations to access and download the data, which may be selectively stored at runtime, into dedicated data archives. dispel4py has been already used by seismologists in the VERCE project to develop different seismic workflows. One of them is the Seismic Ambient Noise Cross-Correlation workflow, which preprocesses and cross-correlates traces from several stations. First, this workflow was tested on a local machine by using a small number of stations as input data. Later, it was executed on different parallel platforms (SuperMUC cluster, and Terracorrelator machine), automatically scaling up by using MPI and multiprocessing mappings and up to 1000 stations as input data. The results show that the dispel4py achieves scalable performance in both mappings tested on different parallel platforms

Asterism: Pegasus and dispel4py hybrid workflows for data-intensive science

We present Asterism, an open source data-intensive framework, which combines the strengths of traditional workflow management systems with new parallel stream-based dataflow systems to run data-intensive applications across multiple heterogeneous resources, without users having to: re-formulate their methods according to different enactment engines; manage the data distribution across systems; parallelize their methods; co-place and schedule their methods with computing resources; and store and transfer large/small volumes of data. We also present the Data-Intensive workflows as a Service (DIaaS) model, which enables easy data-intensive workflow composition and deployment on clouds using containers. The feasibility of Asterism and DIaaS model have been evaluated using a real domain application on the NSF-Chameleon cloud. Experimental results shows how Asterism successfully and efficiently exploits combinations of diverse computational platforms, whereas DIaaS delivers specialized software to execute data-intensive applications in a scalable, efficient, and robust way reducing the engineering time and computational cost

dispel4py: An Open Source Python Framework for Encoding, Mapping and Reusing Seismic Continuous Data Streams: Intensive Analysis and Data Mining

Scientific workflows are needed by many scientific communities, such as seismology, as they enable easy composition and execution of applications, enabling scientists to focus on their research without being distracted by arranging computation and data management. However, there are challenges to be addressed. In many systems users have to adapt their codes and data movement as they change from one HPC-architecture to another. They still need to be aware of the computing architectures available for achieving the best application performance. We present dispel4py, an open-source framework presented as a Python library for encoding and automating data-intensive scientific methods as a graph of operations coupled together by data-streams. It enables scientists to develop and experiment with their own data-intensive applications using their familiar work environment. These are then automatically mapped to a variety of HPC-architectures, i.e., MPI, multiprocessing, Storm and Spark frameworks, increasing the chances to reuse their applications in different computing resources. dispel4py comes with data provenance, as shown in the screenshot, and with an information registry that can be accessed transparently from within workflows. dispel4py has been enhanced with a new run-time adaptive compression strategy to reduce the data stream volume and a diagnostic tool which monitors workflow performance and computes the most efficient parallelisation to use. dispel4py has been used by seismologists in the project VERCE for seismic ambient noise cross-correlation applications and for orchestrated HPC wave simulation and data misfit analysis workflows; two data-intensive problems that are common in today's research practice. Both have been tested in several local computing resources and later submitted to a variety of European PRACE HPC-architectures (e.g. SuperMUC &amp; CINECA) for longer runs without change. Results show that dispel4py is an easy tool for developing, sharing and reusing data-intensive scientific methods

DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.PublishedSan Diego (CA, USA)3IT. Calcolo scientific