On this common function, HPCwire highlights lately revealed analysis within the excessive efficiency computing neighborhood and associated fields. From parallel programming to exascale to quantum computing, the small print are right here.
ScriptManager: an interactive platform to cut back boundaries to genomic evaluation
Cornell College researchers current ScriptManager, designed to “benefit from accessible computational sources, permitting a life science researcher to carry out scalable genomic evaluation with out having to know the underlying computational methods” . Based on the analysis staff, the software program features a GUI that permits user-friendly navigation “of inputs and choices whereas supporting a command-line interface for automation and integration with workflow managers. like Galaxy”. Of their paper, the researchers describe how customers with little command-line expertise “can leverage nationwide compute-intensive sources utilizing a graphical desktop interface like Open OnDemand.” The objective is to offer a instrument for the genomics neighborhood that streamlines entry and avoids technical difficulties, making it simpler to combine scientists’ workflows into large-scale manufacturing pipelines. The supply code is publicly accessible at https://github.com/CEGRcode/scriptmanager.
Authors: Olivia W. Lang, Franklin Pugh and William Km Lai
The OpenMP cluster programming mannequin
On this article, Brazilian researchers current OpenMP Cluster (OMPC), which is a “parallel activity mannequin that extends OpenMP for cluster programming”. Based on researchers from the College of Campinas, the Federal College of ABC and Petrobras, a Brazilian state-owned firm within the oil business, “OMPC permits complicated scientific duties to be offloaded to the nodes of the cluster HPC in a clear and balanced manner. At present, “experimental outcomes present that OMPC can carry out as much as 1.53x and a pair of.43x higher than Appeal++ on CCR and scalability experiments, respectively.” Moreover, the researchers demonstrated by means of experiments that “OMPC efficiency scales weakly for each Process Bench and real-world seismic imaging software.”
Authors: Hervé Yviquel, Marcio Pereira, Emílio Francesquini, Guilherme Valarini, Gustavo Leite, Pedro Rosso, Rodrigo Ceccato, Carla Cusihualpa, Vitoria Dias, Sandro Rigo, Alan Sousa and Guido Araujo
Coupling of streaming AI and HPC units to realize biomolecular simulations 100 to 1000 instances sooner
A multidisciplinary staff of researchers from Argonne Nationwide Laboratory, College of Illinois at Urbana-Champaign, Rutgers College, and Brookhaven Nationwide Laboratory focus on DeepDriveMD on this article. DeepDriveMD is “a framework for ML-driven driving of scientific simulations that now we have used to realize order-of-magnitude enhancements in molecular dynamics (MD) efficiency through environment friendly coupling of ML and HPC on giant parallel computer systems” . The researchers used “three MD biophysical modeling purposes to judge its design, implementation, and efficiency, and show it by piloting units of MD simulations with ML approaches.” The outcomes “show that DeepDriveMD can obtain 100-1000× speedup for protein folding simulations in comparison with different strategies, measured by the simulation time carried out, whereas protecting the identical conformational panorama quantified by the sampled states. throughout a simulation.
Authors: Alexander Brace, Igor Yakushin, Heng Ma, Anda Trifan, Todd Munson, Ian Foster, Arvind Ramanathan, Hyungro Lee, Matteo Turilli and Shantenu Jha
Quantum Computing Benefit with a Programmable Photonics Processor
Researchers from the Nationwide Institute of Requirements and Expertise element their experiences reaching “the quantum computing benefit utilizing Borealis, a photonics processor that gives dynamic programmability on all gates applied.” The researchers “carry out Gaussian boson sampling (GBS) on 216 entangled compressed modes with three-dimensional connectivity, utilizing a time-division multiplexed, photon-count fixing structure.” Utilizing Borealis, the authors declare “it might take over 9,000 years for the very best accessible algorithms and supercomputers to provide, utilizing actual strategies, a single pattern of the programmed distribution, whereas Borealis requires solely 36 μs”.
Authors: Lars S. Madsen, Fabian Laudenbach, Mohsen Falamarzi. Askarani, Fabien Rortais, Trevor Vincent, Jacob FF Bulmer, Filippo M. Miatto, Leonhard Neuhaus, Lukas G. Helt, Matthew J. Collins, Adriana E. Lita, Thomas Gerrits, Sae Woo Nam, Varun D. Vaidya, Matteo Menotti, Ish Dhand, Zachary Vernon, Nicholas Quesada and Jonathan Lavoie
Machine studying metastable part diagram of covalently bonded carbon
A multidisciplinary staff of researchers from Argonne Nationwide Laboratory, the College of Illinois and the Northwestern Argonne Institute of Science and Engineering in Illinois, and the Middle for Excessive Strain Science and Expertise Superior Analysis in China “presents a automated workflow that integrates first ideas physics and atomic simulations with machine studying and excessive efficiency computing. This workflow “will allow speedy exploration of metastable phases to assemble ‘metastable’ part diagrams for supplies removed from the equilibrium”. The researchers used carbon as a prototype system and demonstrated “the automated development of metastable part diagrams to map a whole lot of metastable states starting from a near-equilibrium state to a distant state (400 meV/ atom)”.
Authors: Srilok Srinivasan, Rohit Batra, Duan Luo, Troy Loeffler, Sukriti Manna, Henry Chan, Liuxiang Yang, Wenge Yang, Jianguo Wen, Pierre Darancet and Subramanian KRS Sankaranarayanan
Era of distributed-memory parallel contigs for de novo long-read genome meeting
A analysis staff affiliated with the College of California at Berkeley and the Lawrence Berkeley Nationwide Laboratory is tackling the computational challenges related to Once more genome meeting, which is a technique used to reconstruct an “unknown genome sequence from redundant and inaccurate quick sequences”. With their paper, the researchers sought to “construct on earlier work within the literature, diBELLA 2D, and current a novel distributed-memory algorithm that generates the set of contigs from a graphical illustration of strings of the genome and utilizing a sparse matrix abstraction”. Utilizing the Haswell partition from NERSC’s Cray XC40 Cori supercomputer and the highest of the IBM supercomputer from Oak Ridge Nationwide Laboratory, the researchers evaluated their contig technology algorithms and the ELBA meeting pipeline. The algorithm created by the researchers demonstrated “good scaling with as much as 80% parallel effectivity over 128 nodes, leading to uniform genome protection and displaying promising outcomes by way of meeting high quality” . “The algorithm localizes the meeting course of to dramatically cut back the quantity of computation spent on this step.”
Authors: Giulia Guidi, Gabriel Raulet, Daniel Rokhsar, Leonid Oliker, Katherine Yelick and Aydin Buluc
HPC-Scale Asynchronous Distributed Bayesian Optimization
A staff of researchers from the Argonne Nationwide Laboratory (United States), INP-ENSEEIHT (France) and the College of Paris-Saclay (France), tackles “computational overload within the diagrams of multipoint technology, [considered] a serious bottleneck in designing BO strategies that may scale to hundreds of staff. Of their paper, the researchers develop an “asynchronous distributed BO methodology during which every employee performs a search and asynchronously reviews the input-output values of the black field evaluations of all different staff with out the supervisor”. The researchers prolong their “methodology as much as 4,096 staff and show improved resolution high quality and sooner convergence.” As well as, they had been capable of present the effectiveness of their “method to tune the hyperparameters of neural networks from the CANDLE benchmarks of the Exascale Computing undertaking”.
Authors: Romain Egele, Joceran Gouneau, Venkatram Vishwanath, Isabelle Guyon and Prasanna Balaprakash
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