Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms

Merzky, André; Turilli, Matteo; Titov, Mikhail; Al-Saadi, Aymen; Jha, Shantenu

doi:10.1109/tpds.2021.3105994

Cited by 28 publications

(23 citation statements)

References 35 publications

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“…High-throughput docking was implemented using RADICAL-Pilot (RP) and RAPTOR. 21 RP is a pilot-enabled runtime system, while RAPTOR is a scalable master/worker overlay developed to improve the execution performance of many short-running tasks encoded as Python functions. The runs used between 128 and 7000 concurrent nodes.…”

Section: Methodsmentioning

confidence: 99%

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

Clyde

Galanie

Kneller

et al. 2021

J. Chem. Inf. Model.

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Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent smallmolecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (M pro ) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this M pro inhibitor with an inhibition constant (K i ) of 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of M pro forming stable hydrogen bond and hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by M pro , involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits M pro and offers a springboard for further therapeutic design.

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Section: Methodsmentioning

confidence: 99%

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

Clyde

Galanie

Kneller

et al. 2021

J. Chem. Inf. Model.

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“…In the past few months, we have progressed substantially with the implementation of our workflow. RCT is now fully functional on Summit [ 55 , 73 , 74 ] as well as Theta [ 75 ], in addition to several other HPC resources. It has successfully executed workloads at 95% usage on these machines.…”

Section: Workflow Managementmentioning

confidence: 99%

“…It has successfully executed workloads at 95% usage on these machines. We characterized scaling performance of various components of our workflow using up to 392 000 cores and 24 582 GPUs to execute 24 552 heterogeneous executable tasks and 126 × 10 6 python function tasks [ 74 ]. Recently, we have been able to achieve a performance of 144 M h −1 docking hits screening approximately 10 11 ligands using over 8000 compute nodes, which is better than the previous best by a factor of two [ 76 ].…”

Section: Workflow Managementmentioning

confidence: 99%

“…We have already analysed several million compounds from a set of orderable compound libraries using the current implementation of our workflow and filtered out compounds for the second iteration of our iterative workflow. Recently accepted publications in IEEE TPDS, ACM SIGHPC ICPP, ACM SIGHPC PASC [ 55 , 73 – 74 ], as well as publications under review [ 75 , 76 ] provide evidence of our progress towards the fully optimized implementation of the workflow.…”

Section: Workflow Managementmentioning

confidence: 99%

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Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

et al. 2021

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The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

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“…We integrate RADICAL-Pilot (RP) [4], [5], a pilot system that enables the execution of multi-task, heterogeneous workloads on diverse HPC platforms, and Parsl [6], a parallel scripting library for Python that provides workflow and dataflow capabilities. The result is a looselycoupled system that does not require a dedicated code repository but only an interface with a small code footprint.…”

Section: Introductionmentioning

confidence: 99%

RADICAL-Pilot and Parsl: Executing Heterogeneous Workflows on HPC Platforms

Aymen¹,

Ward²,

Merzky³

et al. 2021

Preprint

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Executing scientific workflows with heterogeneous tasks on HPC platforms poses several challenges which will be further exacerbated by the upcoming exascale platforms. At that scale, bespoke solutions will not enable effective and efficient workflow executions. In preparation, we need to look at ways to manage engineering effort and capability duplication across software systems by integrating independently developed, production-grade software solutions. In this paper, we integrate RADICAL-Pilot (RP) and Parsl and develop an MPI executor to enable the execution of workflows with heterogeneous (non)MPI Python functions at scale. We characterize the strong and weak scaling of the integrated RP-Parsl system when executing two use cases from polar science, and of the function executor on both SDSC Comet and TACC Frontera. We gain engineering insight about how to analyze and integrate workflow and runtime systems, minimizing changes in their code bases and overall development effort. Our experiments show that the overheads of the integrated system are invariant of resource and workflow scale, and measure the impact of diverse MPI overheads. Together, those results define a blueprint towards an ecosystem populated by specialized, efficient, effective and independently-maintained software systems to face the upcoming scaling challenges.

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Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms

Cited by 28 publications

References 35 publications

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

RADICAL-Pilot and Parsl: Executing Heterogeneous Workflows on HPC Platforms

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