Research Software Sustainability: Lessons Learned at NCSA

This paper discusses why research software is important, and what sustainability means in this context. It then talks about how research software sustainability can be achieved, and what our experiences at NCSA have been using specific examples, what we have learned from this, and how we think these lessons can help others

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators. Here, we present our experiences deploying two AI-guided simulation workflows across such heterogeneous systems. A unique aspect of our approach is our use of cloud-hosted management services to manage challenging aspects of cross-resource authentication and authorization, function-as-a-service (FaaS) function invocation, and data transfer. We show that these methods can achieve performance parity with systems that rely on direct connection between resources. We achieve parity by integrating the FaaS system and data transfer capabilities with a system that passes data by reference among managers and workers, and a user-configurable steering algorithm to hide data transfer latencies. We anticipate that this ease of use can enable routine use of heterogeneous resources in computational science

funcX: A Federated Function Serving Fabric for Science

Exploding data volumes and velocities, new computational methods and platforms, and ubiquitous connectivity demand new approaches to computation in the sciences. These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e.g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available. They also require new design approaches in which monolithic applications can be decomposed into smaller components, that may in turn be executed separately and on the most suitable resources. To address these needs we present funcX---a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. funcX's endpoint software can transform existing clouds, clusters, and supercomputers into function serving systems, while funcX's cloud-hosted service provides transparent, secure, and reliable function execution across a federated ecosystem of endpoints. We motivate the need for funcX with several scientific case studies, present our prototype design and implementation, show optimizations that deliver throughput in excess of 1 million functions per second, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap with arXiv:1908.0490

A Programming Model for Hybrid Workflows: combining Task-based Workflows and Dataflows all-in-one

This paper tries to reduce the effort of learning, deploying, and integrating several frameworks for the development of e-Science applications that combine simulations with High-Performance Data Analytics (HPDA). We propose a way to extend task-based management systems to support continuous input and output data to enable the combination of task-based workflows and dataflows (Hybrid Workflows from now on) using a single programming model. Hence, developers can build complex Data Science workflows with different approaches depending on the requirements. To illustrate the capabilities of Hybrid Workflows, we have built a Distributed Stream Library and a fully functional prototype extending COMPSs, a mature, general-purpose, task-based, parallel programming model. The library can be easily integrated with existing task-based frameworks to provide support for dataflows. Also, it provides a homogeneous, generic, and simple representation of object and file streams in both Java and Python; enabling complex workflows to handle any data type without dealing directly with the streaming back-end.Comment: Accepted in Future Generation Computer Systems (FGCS). Licensed under CC-BY-NC-N

A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers

Software pipelines enable organizations to chain applications for adding value to contents (e.g., confidentially, reliability, and integrity) before either sharing them with partners or sending them to the cloud. However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears represent applications, whereas gearboxes represent software pipelines. This model was implemented as a collaborative system that automatically performs Gear up (by using parallel patterns) and/or Gear down (by using in-memory storage) until all gears produce uniform data processing velocities. This model reduces delays and bottlenecks produced by the heterogeneous performance of applications included in software pipelines. The new container tool has been designed to encapsulate both the collaborative system and the software pipelines into a virtual container and deploy it on IT infrastructures. We conducted case studies to evaluate the performance of when processing medical images and PDF repositories. The incorporation of a capsule to a cloud storage service for pre-processing medical imagery was also studied. The experimental evaluation revealed the feasibility of applying the gearbox model to the deployment of software pipelines in real-world scenarios as it can significantly improve the end-user service experience when pre-processing large-scale data in comparison with state-of-the-art solutions such as Sacbe and Parsl.This work has been partially supported by the “Spanish Ministerio de Economia y Competitividad ” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms”

The BioExcel methodology for developing dynamic, scalable, reliable and portable computational biomolecular workflows

Developing complex biomolecular workflows is not always straightforward. It requires tedious developments to enable the interoperability between the different biomolecular simulation and analysis tools. Moreover, the need to execute the pipelines on distributed systems increases the complexity of these developments. To address these issues, we propose a methodology to simplify the implementation of these workflows on HPC infrastructures. It combines a library, the BioExcel Building Blocks (BioBBs), that allows scientists to implement biomolecular pipelines as Python scripts, and the PyCOMPSs programming framework which allows to easily convert Python scripts into task-based parallel workflows executed in distributed computing systems such as HPC clusters, clouds, containerized platforms, etc. Using this methodology, we have implemented a set of computational molecular workflows and we have performed several experiments to validate its portability, scalability, reliability and malleability.This work has been supported by Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under contract PID2019-107255GB-C21, by the Generalitat de Catalunya under contracts 2017-SGR-01414 and 2017-SGR1110, by the European Commission through the BioExcel Center of Excellence (Horizon 2020 Framework program) under contracts 823830, and 675728. This work is also partially supported by the CECH project which has been co-funded with 50% by the European Regional Development Fund under the framework of the ERFD Operative Programme for Catalunya 2014-2020, with a grant of 1.527.637,88€.Peer ReviewedPostprint (author's final draft

Methods Included:Standardizing Computational Reuse and Portability with the Common Workflow Language

A widely used standard for portable multilingual data analysis pipelines would enable considerable benefits to scholarly publication reuse, research/industry collaboration, regulatory cost control, and to the environment. Published research that used multiple computer languages for their analysis pipelines would include a complete and reusable description of that analysis that is runnable on a diverse set of computing environments. Researchers would be able to easier collaborate and reuse these pipelines, adding or exchanging components regardless of programming language used; collaborations with and within the industry would be easier; approval of new medical interventions that rely on such pipelines would be faster. Time will be saved and environmental impact would also be reduced, as these descriptions contain enough information for advanced optimization without user intervention. Workflows are widely used in data analysis pipelines, enabling innovation and decision-making for the modern society. In many domains the analysis components are numerous and written in multiple different computer languages by third parties. However, lacking a standard for reusable and portable multilingual workflows, then reusing published multilingual workflows, collaborating on open problems, and optimizing their execution would be severely hampered. Moreover, only a standard for multilingual data analysis pipelines that was widely used would enable considerable benefits to research-industry collaboration, regulatory cost control, and to preserving the environment. Prior to the start of the CWL project, there was no standard for describing multilingual analysis pipelines in a portable and reusable manner. Even today / currently, although there exist hundreds of single-vendor and other single-source systems that run workflows, none is a general, community-driven, and consensus-built standard

Greedy nominator heuristic: virtual function placement on fog resources

Fog computing is an intermediate infrastructure between edge devices (e.g., Internet of Things) and cloud systems that is used to reduce latency in real-time applications. An application can be composed of a collection of virtual functions, between which dependency constraints can be captured in a service function chain (SFC). Virtual functions within an SFC can be executed at different geo-distributed locations. However, virtual functions are prone to failure and often do not complete within a deadline. This results in function reallocation to other nodes within the infrastructure; causing delays, potential data loss during function migration, and increased costs. We proposed Greedy Nominator Heuristic (GNH) to address these issues. GNH is based on redundant deployment and failure tracking of virtual functions. GNH places replicas of each function at multiple locations—taking account of expected completion time, failure risk, and cost. We make use of a MapReduce-based mechanism, where Mappers find suitable locations in parallel, and a Reducer then ranks these locations. Our results show that GNH reduces latency by up to 68%, and is more cost effective than other approaches which rely on state-of-the-art optimization algorithms to allocate replicas