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10 results for “parallel computing”
3D magnetotelluric modeling using high-order tetrahedral Nédélec elementson massively parallel computing platforms
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Modesto, D., Queralt, P., Marcuello, A., Ledo, J., Amor-Martin, A., de la Puente, J., García-Castillo, L.E. (2021) 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms. Computers & Geosciences, vol.(160): 105030 DOI: 10.1016/j.cageo.2021.105030. ISSN 0098-3004, Elsevier.</p> </blockquote>
Parallel window decoding enables scalable fault tolerant quantum computation
<p>Dataset containing raw data presented in the publication <em>"Parallel window decoding enables scalable fault tolerant quantum computation"</em> as well as the stim circuits used to sample circuit-level noise.</p> <p> </p>
Data for "Fully Implicit Time Stepping can be Efficient on Parallel Computers"
<p>Benchmark data and python plotting programs</p>
Massively parallel, computationally-guided design of a pro-enzyme
<p>Confining the activity of a designed protein to a specific microenvironment would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as inactive zymogens that can be activated by proteolysis, it has been challenging to re-design any chosen enzyme to be similarly stimulus-responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for pro-enzyme design. As a model system, we employ carboxypeptidase G2 (CPG2), a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug toxicity. Detailed kinetic characterization of most effective designed variants shows that they are inhibited by approximately 80% compared to the unmodified protein, and their activity is fully restored following incubation with site-specific proteases. Introducing disulfide bonds between the pro- and catalytic domains based on the design models increases the degree of inhibition to 98%, but decreases the degree of restoration of activity by proteolysis. A selected disulfide-containing pro-enzyme exhibits significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization provides detailed insights into the pro-domain binding and inhibition mechanisms. The described methodology is general and could enable the design of a variety of pro-proteins with precise spatial regulation.</p>
Data set and simulation code for "Static workspace computation for underactuated cable-driven parallel robots"
<p>See the attached readme file</p>
Efficient parallelization of tensor network contractions for simulating quantum computation
<p> In this paper, we demonstrate a classical simulation framework for quantum computation by contracting tensor networks of sizes previously deemed out of reach. The main contribution of this work is a parallelization scheme called <em>index slicing</em> that breaks down an infeasibly large tensor network contraction task into smaller subtasks that can be executed fully in parallel, without interdependencies or intermediate communications. As a benchmarking example, we show that our algorithm can reduce the simulation of the Sycamore random circuit sampling task to less than 20 days, achieving an acceleration of over five orders of magnitude compared to the original proposal. We then showcase the capabilities of the simulation framework via investigations of near-term quantum algorithms and quantum error correction. Given the ubiquity of tensor networks in quantum information science, we believe that our simulation framework will be a valuable tool in the era of quantum information technology.</p>
Efficient parallelization of tensor network contractions for simulating quantum computation
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WarpX Accelerated Nodes Parallel Computing Paper
<p>This dataset contains the inputs, outputs, job submission scripts, and executables<br> used to create the Figures in "Porting WarpX to GPU-accelerated platforms" by A. Myers<br> et. al, submitted to Parallel Computing as part of the ECP Special Issue on Transitioning<br> to Accelerated nodes.</p> <p>These results were obtained using the October, 2020 release tags of WarpX and AMReX,<br> available on Github here:</p> <p> https://github.com/ECP-WarpX/WarpX</p> <p>and here:</p> <p> https://github.com/AMReX-Codes/amrex</p> <p>The following module files were loaded on Summit:</p> <p> 1) hsi/5.0.2.p5 2) xalt/1.2.0 3) lsf-tools/2.0 4) darshan-runtime/3.1.7<br> 5) DefApps 6) cuda/10.1.243 7) gcc/6.4.0 8) spectrum-mpi/10.3.1.2-20200121</p> <p>To use nsight-compute for the roofline plots, we also loaded:</p> <p> nsight-compute/2020.1.2</p> <p>Manifest:</p> <p>BinScan: contains material used to make Figure 1. To generate the figure, use the<br> Jupyter notebook called "bin_size.ipynb".</p> <p>StrongScaling: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "strong_scaling.ipynb".</p> <p>WeakScalingCPU: contains material used to make Figure 4. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb".</p> <p>WeakScalingGPU: contains material used to make Figure 5. To generate the figure, use the<br> Jupyter notebook called "weak_scaling.ipynb".</p> <p>Roofline: contains material used to make the roofline plots (Figures 2 and 3). This<br> includes output generated using nsight-compute with WarpX and python scripts for<br> processing and plotting these output files. These scripts and methodology originally<br> come from Charlene Yang at NERSC. The file "script.sh" was used to generate the<br> profiler output</p>
Data from: Tensor analysis reveals distinct population structure that parallels the different computational roles of areas M1 and V1
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Parallel window decoding enables scalable fault tolerant quantum computation
<p>Dataset containing raw data presented in the publication <em>"Parallel window decoding enables scalable fault tolerant quantum computation"</em> as well as the stim circuits used to sample circuit-level noise.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.