Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and optimizing them increasingly challenging. Furthermore, their complexity and heterogeneity make performance reproducibility hard. In this paper, we propose workflow mini-apps as a tool to address the challenges in building and testing workflows while controlling the fidelity of representing real-world workflows. Workflow mini-apps are deployed and run on various HPC systems and architectures without workflow-specific constraints. We offer insight into their design and implementation, providing an analysis of their performance and reproducibility. Workflow mini-apps thus advance the science of workflows by providing simple, portable, and managed (fidelity) representations of otherwise complex and difficult-to-control real workflows.

Heterogeneous workflows represent a promising approach for overcoming traditional application performance limitations and to accelerate scientific insight on high-performance computing (HPC) platforms. As HPC platforms grow in size and complexity, managing and optimizing workflow resources while maximizing scientific output assumes vital importance. Optimal workflow resource allocation requires high-quality and timely information about the state of the hardware resources, the status of the pending tasks, the performance of the tasks that have already been executed, and the current status of the workflow itself. A robust performance observability framework that captures and delivers this information can fundamentally improve the quality of decision-making within the workflow system, setting the stage for the adaptive execution of workflow tasks. We propose the use of SOMA, a service-based performance observability framework for such HPC workflows. With the RADICAL-Pilot runtime system as a development vehicle, SOMA demonstrates that service-based architectures coupled with an appropriate data model can serve the performance monitoring needs of large-scale ensemble workflows in a low-overhead fashion. Effective observability of workflow performance requires exporting, storing, and analyzing several types of performance data from across the application and workflow software stacks. Our study finds significant benefits in integrating observability frameworks as first-class citizens within an HPC workflow software stack. In this paper, we demonstrate how SOMA can simultaneously observe the performance states of the individual tasks, system hardware, and the workflow as a whole. Such information can then be employed to calculate better resource allocation and task configuration.

Scientific computing communities increasingly run their experiments using complex data- and compute-intensive workflows that utilize distributed and heterogeneous architectures targeting numerical simulations and machine learning, often executed on the Department of Energy Leadership Computing Facilities (LCFs). We argue that a principled, systematic approach to implementing FAIR principles at scale, including fine-grained metadata extraction and organization, can help with the numerous challenges to performance reproducibility posed by such workflows. We extract workflow patterns, propose a set of tools to manage the entire life cycle of performance metadata, and aggregate them in an HPC-ready framework for reproducibility (RECUP). We describe the challenges in making these tools interoperable, preliminary work, and lessons learned from this experiment.

The importance of ensemble computing is well established. However, executing ensembles at scale introduces interesting performance fluctuations that have not been well investigated. In this paper, we trace our experience uncovering performance fluctuations of ensemble applications (primarily constituting a workflow of GROMACS tasks), and unsuccessful attempts, so far, at trying to discern the underlying cause(s) of performance fluctuations. Is the failure to discern the causative or contributing factors a failure of capability? Or imagination? Do the fluctuations have their genesis in some inscrutable aspect of the system or software? Does it warrant a fundamental reassessment and rethinking of how we assume and conceptualize performance reproducibility? Answers to these questions are not straightforward, nor are they immediate or obvious. We conclude with a discussion about the performance of ensemble applications and ruminate over the implications for how we define and measure application performance.

Many extreme scale scientific applications have workloads comprised of a large number of individual high-performance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible pilot-enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show its ability to scalably execute workloads comprised of tens of thousands heterogeneous tasks on DOE and NSF leadership-class HPC platforms. Specifically, we investigate RP's weak/strong scaling with CPU/GPU, single/multi core, (non)MPI tasks and Python functions when using most of ORNL Summit and TACC Frontera. RADICAL-Pilot can be used stand-alone, as well as the runtime for third-party workflow systems.

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼ 1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine.

The analysis of the hundreds of petabytes of raw and derived HEP (High Energy Physics) data will necessitate exascale computing. In addition to unprecedented volume, these data are distributed over hundreds of computing centers. In response to these application requirement, as well as performance requirement by using parallel processing (i.e., parallelism), and as a consequence of technology trends, there has been an increase in the uptake of supercomputers by HEP projects.

UNLABELLED: The database NPIDB (Nucleic Acids-Protein Interaction DataBase) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from PDB (1834 complexes in July 2007). It is organized as a collection of files in PDB format and is equipped with a web-interface and a set of tools for extracting biologically meaningful characteristics of complexes. The content of the database is weekly updated. AVAILABILITY: http://monkey.belozersky.msu.ru/NPIDB/