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17 results for “Sparsity”
Matrix reorderings for "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs"
<p>The paper "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs" compares various strategies for reordering sparse matrices. The purpose of reordering is to improve performance of sparse matrix operations, for example, by reducing fill-in resulting from sparse Cholesky factorisation or improving data locality in sparse matrix-vector multiplication (SpMV). Many reordering strategies have been proposed in the literature and the current paper provides a thorough comparison of several of the most popular methods.</p> <p>This comparison is based on 490 sparse matrices from the SuiteSparse Matrix Collection (https://sparse.tamu.edu) and 6 matrix reordering algorithms. The dataset provided here supplies the permutations and reordered matrices in Matrix Market file format for 3 matrices and 6 reorderings.</p>
Datasets used in: "Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis"
<p>Datasets included in: Bokan Bao+, Benjamin P. Kellman+, Austin W. T. Chiang, Austin K. York, Mahmoud A. Mohammad, Morey W. Haymond, Lars Bode, and Nathan E. Lewis. 2019. “<strong>Correcting for Sparsity and Non-Independence in Glycomic Data through a System Biology Framework.</strong>” bioRxiv. <a href="https://doi.org/10.1101/693507">https://doi.org/10.1101/693507</a></p> <p><strong>Central Datasets</strong><br> - Github_Yang2019_EPO<br> - paper_hmo<br> These are the HMO and EPO datasets used throughout the majority of the manuscript. They are formatted consistent with the github code repository: https://github.com/LewisLabUCSD/GlyCompare</p> <p><strong>Additional Datasets</strong><br> - Webapp_Jin2017_Mucin<br> - Webapp_Riley2019_SiteSpecN<br> - Webapp_Sibile2016_Glycolipid<br> These are additional datasets explored in the final figure and supplement of the manuscript formatted for the webapp: https://glycompare.herokuapp.com/</p> <p>All datasets but Riley2019 have structural data, Riley2019 only contains compositional data</p> <p><strong>Detailed Descriptions</strong></p> <ul> <li>Github_Yang2019_EPO <ul> <li>Sixteen MALDI-TOF glycoprofiles of EPO, where each EPO glycoprofile was produced in a different glycoengineered</li> </ul> </li> <li>CHO cell line- paper_hmo <ul> <li>Forty-eight HPLC glycoprofiles of HMO from six mothers22.</li> </ul> </li> <li>Webapp_Jin2017_Mucin <ul> <li>Mucin-type O-glycans from tumor and normal samples from gastrointestinal cancers</li> </ul> </li> <li>Webapp_Riley2019_SiteSpecN <ul> <li>Site-specific N-glycosylation in mouse brain</li> </ul> </li> <li>Webapp_Sibile2016_Glycolipid <ul> <li>Glycolipid abundance in Rat eye, brain and blood<br> </li> </ul> </li> </ul>
Sparsity-constrained wavefront optimization by leveraging complex media
<div> <div>Wavefront shaping gains increasing importance in complex photonics, which can manipulate light spatially and temporally to counter the scattering effect. Important applications include deep-tissue imaging, microendoscopy, optical communications, nanofabrication, and remote sensing. However, high-speed and high-fidelity wavefront shaping is fundamentally hindered by the dimensionality limitation of hardware devices, evinced by the competition between the frame rate, pixel count, and modulation depth. To overcome the speed-fidelity tradeoff, we leverage complex media (e.g., diffusers or multimode fibers) as analogue random multiplexers for pattern compression to address the demand for high-dimensional spatiotemporal control. Sparsity-constrained wavefront optimization is designed to solve the problem by seeking a low-dimensional, robust representation of wavefronts with a carefully designed sparsity constraint. This optimization framework can achieve high-fidelity wavefront shaping through complex media using high-speed, yet relatively low-precision spatial light modulation devices (e.g., digital micromirror devices) without compromising the frame rate.</div> </div>
Performance measurements for "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs"
<p>The paper "Bringing Order to Sparsity: A Sparse Matrix Reordering Study on Multicore CPUs" compares various strategies for reordering sparse matrices. The purpose of reordering is to improve performance of sparse matrix operations, for example, by reducing fill-in resulting from sparse Cholesky factorisation or improving data locality in sparse matrix-vector multiplication (SpMV). Many reordering strategies have been proposed in the literature and the current paper provides a thorough comparison of several of the most popular methods.</p> <p>This comparison is based on performance measurements that were collected on the eX3 cluster, a Norwegian, experimental research infrastructure for exploration of exascale computing. These performance measurements are gathered in the data set provided here, particularly related to the performance of two SpMV kernels with respect to 490 sparse matrices, 6 matrix orderings and 8 multicore CPUs.</p> <p>Experimental results are provided in a human-readable, tabular format using plain-text ASCII. This format may be readily consumed by gnuplot to create plots or imported into commonly used spreadsheet tools for further analysis.</p> <p>Performance measurements are provided based on an SpMV kernel using the compressed sparse row (CSR) storage format with 7 matrix orderings. One file is provided for each of 8 multicore CPU systems considered in the paper:</p> <p> 1. Skylake: csr_all_xeongold16q_032_threads_ss490.txt<br> 2. Ice Lake: csr_all_habanaq_072_threads_ss490.txt<br> 3. Naples: csr_all_defq_064_threads_ss490.txt<br> 4. Rome: csr_all_rome16q_016_threads_ss490.txt<br> 5. Milan A: csr_all_fpgaq_048_threads_ss490.txt<br> 6. Milan B: csr_all_milanq_128_threads_ss490.txt<br> 7. TX2: csr_all_armq_064_threads_ss490.txt<br> 8. Hi1620: csr_all_huaq_128_threads_ss490.txt</p> <p>A corresponding set of files and performance measurements are provided for a second SpMV kernel that is also studied in the paper.</p> <p>Each file consists of 490 rows and 54 columns. Each row corresponds to a different matrix from the SuiteSparse Matrix Collection (https://sparse.tamu.edu/). The first 5 columns specify some general information about the matrix, such as its group and name, as well as the number of rows, columns and nonzeros. Column 6 specifies the number of threads used for the experiment (which depends on the CPU). The remaining columns are grouped according to the 7 different matrix orderings that were studied, in the following order: original, Reverse Cuthill-McKee (RCM), Nested Dissection (ND), Approximate Minimum Degree (AMD), Graph Partitioning (GP), Hypergraph Partitioning (HP), and Gray ordering. For each ordering, the following 7 columns are given:</p> <p><br> 1. Minimum number of nonzeros processed by any thread by the SpMV kernel<br> 2. Maximum number of nonzeros processed by any thread by the SpMV kernel<br> 3. Mean number of nonzeros processed per thread by the SpMV kernel<br> 4. Imbalance factor, which is the ratio of the maximum to the mean number of nonzeros processed per thread by the SpMV kernel<br> 5. Time (in seconds) to perform a single SpMV iteration; this was measured by taking the minimum out of 100 SpMV iterations performed<br> 6. Maximum performance (in Gflop/s) for a single SpMV iteration; this was measured by taking twice the number of matrix nonzeros and dividing by the minimum time out of 100 SpMV iterations performed.<br> 7. Mean performance (in Gflop/s) for a single SpMV iteration; this was measured by taking twice the number of matrix nonzeros and dividing by the mean time of the 97 last SpMV iterations performed (i.e., the first 3 SpMV iterations are ignored).</p> <p>The results in Fig. 1 of the paper show speedup (or slowdown) resulting from reordering with respect to 3 reorderings and 3 selected matrices. These results can be reproduced by inspecting the performance results that were collected on the Milan B and Ice Lake systems for the three matrices Freescale/Freescale2, SNAP/com-Amazon and GenBank/kmer_V1r. Specifically, the numbers displayed in the figure are obtained by dividing the maximum performance measured for the respective orderings (i.e., RCM, ND and GP) by the maximum performance measured for the original ordering.</p> <p>The results presented in Figs. 2 and 3 of the paper show the speedup of SpMV as a result of reordering for the two SpMV kernels considered in the paper. In this case, gnuplot scripts are provided to reproduce the figures from the data files described above.</p>
Sparsity-constrained wavefront optimization by leveraging complex media
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Dataset for On the Sparsity of XORs in Approximate Model Counting (SAT-20 Paper)
<p>The artifact consists of the necessary data to reproduce the results reported in the SAT-20 Paper titled "On the Sparsity of XORs in Approximate Model Counting". <br> <br> In particular, the artifact consists of the binaries, the log files generated by our computing cluster, and scripts to generate tables and the plots used in the paper. </p>
Research data accompanying the paper 'Exploiting Sparsity in Free Energy Basin-Hopping'
<p>Input and output files for the results presented in Tables 1-5 of the paper 'Exploiting Sparsity in Free Energy Basin-Hopping'. Further information on these files can be found in the README files within the tar file. </p> <p> </p>
A Survival Analysis based Volatility and Sparsity Modeling Network for Student Dropout Prediction
<p>KDDCup15.rar and XuetangX.rar are the metadata used in the paper "A Survival Analysis based Volatility and Sparsity ModelingNetwork for Student Dropout Prediction". Both of them have be drawn from the largest MOOC platform in China, XuetangX (see https://www.xuetangx.com/). If any interested parties want to fetch the original dataset, they may follow the URLs bellow:</p> <p>KDDCup 2015 dataset is available at <a href="https://www.biendata.xyz/competition/kddcup2015/data/">https://www.biendata.xyz/competition/kddcup2015/data/</a>.</p> <p>XuetangX dataset is available at <a href="http://moocdata.cn/data/user-activity">http://moocdata.cn/data/user-activity</a>.</p>
Data for: Model-based myocardial T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH Cardiovascular Magnetic Resonance
<p>Magnetic Resonance Imaging measurement data used in our paper about model-based myocardial T1 mapping with sparsity constraints. The data was obtained using a single-short inversion-recovery radial FLASH sequence and is provided in a file format used by the BART toolbox (<a href="http://doi.org/10.5281/zenodo.592960">DOI: 10.5281/zenodo.592960</a>).</p>
Data from: Adaptation to visual sparsity enhances responses to isolated stimuli
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AD/AE of SC24 Submission: A sparsity-aware distributed-memory algorithm for sparse-sparse matrix multiplication
<p>AD/AE for SC24 submission: A sparsity-aware distributed-memory algorithm for sparse-sparse matrix multiplication</p> <p> </p>
Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference [Artefact Evaluation]
<p>Source code of paper "Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference" submitted to FPGA'22 for artefact evaluation.</p>
Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity
<p>Raw data for Profiling single cancer cell metabolism via high-content SRS imaging with chemical sparsity </p>
Data from: How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography
We introduce phase-diagram analysis, a standard tool in compressed sensing (CS), to the X-ray computed tomography (CT) community as a systematic method for determining how few projections suffice for accurate sparsity-regularized reconstruction. In CS, a phase diagram is a convenient way to study and express certain theoretical relations between sparsity and sufficient sampling. We adapt phase-diagram analysis for empirical use in X-ray CT for which the same theoretical results do not hold. We demonstrate in three case studies the potential of phase-diagram analysis for providing quantitative answers to questions of undersampling. First, we demonstrate that there are cases where X-ray CT empirically performs comparably with a near-optimal CS strategy, namely taking measurements with Gaussian sensing matrices. Second, we show that, in contrast to what might have been anticipated, taking randomized CT measurements does not lead to improved performance compared with standard structured sampling patterns. Finally, we show preliminary results of how well phase-diagram analysis can predict the sufficient number of projections for accurately reconstructing a large-scale image of a given sparsity by means of total-variation regularization.
Data from: How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography
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Sparsity-based Magnetic Resonance Imaging of Cardiac Arrhythmias
ClinicalTrials.gov study NCT02741180. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data for mmvec response (Sparsity Run)
<p>These data supports our response to the mmvec publication</p>
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International Brain Laboratory public data
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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.