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18 results for “parallel applications”
Dataset for Numerical modeling of air-vented parallel plate ionization chambers for ultra-high dose rate applications
<p>Dataset for paper: Jose Paz-Martín et al., <a href="https://www.sciencedirect.com/journal/physica-medica">Physica Medica</a> <a href="https://www.sciencedirect.com/journal/physica-medica/vol/103/suppl/C">Volume 103</a>, November 2022, Pages 147-156</p> <p><a href="https://doi.org/10.1016/j.ejmp.2022.10.006">https://doi.org/10.1016/j.ejmp.2022.10.006</a></p>
RPL: a domain-specific language for designing and implementing parallel C++ applications
<p>Parallelising sequential applications is usually a very hard job, due to many different ways in which an application can be parallelised and a large number of programming models (each with its own advantages and disadvantages) that can be used. In this paper, we describe a method to semi-automatically generate and evaluate different parallelisations of the same application, allowing programmers to find the best parallelisation without significant manual reengineering of the code. We describe a novel, high-level domain-specific language, Refactoring Pattern Language (RPL), that is used to represent the parallel structure of an application and to capture its extra-functional properties (such as service time). We then describe a set of RPL rewrite rules that can be used to generate alternative, but semantically equivalent, parallel structures (parallelisations) of the same application. We also describe the RPL Shell that can be used to evaluate these parallelisations, in terms of the desired extra-functional properties. Finally, we describe a set of C++ refactorings, targeting OpenMP, Intel TBB and FastFlow parallel programming models, that semi-automatically apply the desired parallelisation to the application's source code, therefore giving a parallel version of the code. We demonstrate how the RPL and the refactoring rules can be used to derive efficient parallelisations of two realistic C++ use cases (Image Convolution and Ant Colony Optimisation).</p>
Dataset: Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications
<p>Dataset used in the paper "Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications"</p>
Parallel generation of extensive vascular networks with application to an archetypal human kidney model
<p>Given the relevance of the inextricable coupling between microcirculation and physiology, and the relation to organ function and disease progression, the construction of synthetic vascular networks for mathematical modelling and computer simulation is becoming an increasingly broad field of research. Building vascular networks that mimic in-vivo morphometry is feasible through algorithms such as constrained constructive optimisation (CCO) and variations. Nevertheless, these methods are limited by the maximum number of vessels to be generated due to the whole network update required at each vessel addition. In this work, we propose a CCO-based approach endowed with a domain decomposition strategy to concurrently create vascular networks. The performance of this approach is evaluated by analysing the agreement with the sequentially generated networks and studying the scalability when building vascular networks up to 200,000 vascular segments. Finally, we apply our method to vascularise a highly complex geometry corresponding to the cortex of a prototypical human kidney. The technique presented in this work enables the automatic generation of extensive vascular networks, removing the limitation from previous works. Thus, we can extent vascular networks (e.g., obtained from medical images) to pre-arteriolar level, yielding patient-specific whole-organ vascular models with an unprecedented level of detail.</p>
Data for figures in "Reproducibility in Benchmarking Parallel Fast Fourier Transform based Applications"
<p>FFT benchmark data and Python plotting programs</p>
Starspot mapping with adaptive parallel tempering. II. Application to TESS data for M-dwarf flare stars, AU Microscopii, YZ Canis Minoris, and EV Lacertae (Flare Tables)
<p>For TESS PDC-SAP data of AU Mic (Sector 1 and 27), YZ CMi (Sector 7 and 34), and EV Lac (Sector 16), detected flare properties are listed: the peak time (BJD-2745000), amplitude, equivalent duration (sec), and e-folding time (day).</p>
Parallel generation of extensive vascular networks with application to an archetypal human kidney model
Open the record for dataset details and reuse information.
Application of spectral library prediction for parallel reaction monitoring of viral peptides_DDA_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_NCE_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Predicting Performance and Power Consumption of Parallel Applications
<p><em><strong>Abstract: </strong>Current architectures provide many control knobs for the reduction of power consumption of applications, like reducing the number of used cores or scaling down their frequency. However, choosing the right values for these knobs in order to satisfy requirements on performance and/or power consumption is a complex task and trying all the possible combinations of these values is an unfeasible solution since it would require too much time. For this reasons, there is the need for techniques that allow an accurate estimation of the performance and power consumption of an application when a specific configuration of the control knobs values is used. Usually, this is done by executing the application with different configurations and by using these information to predict its behaviour when the values of the knobs are changed. However, since this is a time consuming process, we would like to execute the application in the fewest number of configurations possible. In this work, we consider as control knobs the number of cores used by the application and the frequency of these cores. We show that on most Parsec benchmark programs, by executing the application in 1% of the total possible configurations and by applying a multiple linear regression model we are able to achieve an average accuracy of 96% in predicting its execution time and power consumption in all the other possible knobs combinations.</em></p> <p>This dataset includes the raw data of the experiments as well as the scripts used to plot them.</p>
A Reconfiguration Algorithm for Power-Aware Parallel Applications
<p><strong><em>Abstract: </em></strong><em>In current computing systems, many applications require guarantees on their maximum power consumption to not exceed the available power budget. On the other hand, for some applications, it could be possible to decrease their performance, yet maintaining an acceptable level, in order to reduce their power consumption. To provide such guarantees, a possible solution consists in changing the number of cores assigned to the application, their clock frequency and the placement of application threads over the cores. However, power consumption and performance have different trends depending on the application considered and on its input. Finding a configuration of resources satisfying user requirements is in the general case a challenging task. In this paper we propose Nornir, an algorithm to automatically derive, without relying on historical data about previous executions, performance and power consumption models of an application in different configurations. By using these models, we are able to select a close to optimal configuration for the given user requirement, either performance or power consumption. The configuration of the application will be changed on-the-fly throughout the execution to adapt to workload fluctuations, external interferences and/or application's phase changes. We validate the algorithm by simulating it over the applications of the PARSEC benchmark suite. Then, we implement our algorithm and we analyse its accuracy and overhead over some of these applications on a real execution environment. Eventually, we compare the quality of our proposal with that of the optimal algorithm and of some state of the art solutions.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p> <p> </p>
High-Level and Efficient Stream Parallelism on Multi-core Systems with SPar for Data Compression Applications
<p>The stream processing domain is present in several real-world applications that are running on multi-core systems. In this paper, we focus on data compression applications that are an important sub-set of this domain. Our main goal is to assess the programmability and efficiency of domain-specific language called SPar. It was specially designed for expressing stream parallelism and it promises higher-level parallelism abstractions without significant performance losses. Therefore, we parallelized Lzip and Bzip2 compressors<br> with SPar and compared with state-of-the-art frameworks. The results revealed that SPar is able to efficiently exploit stream parallelism as well as provide suitable abstractions with less code intrusion and code re-factoring.</p>
Data from: Metabarcoding for the parallel identification of several hundred predators and their preys: application to bat species diet analysis
Assessing diet variability is of main importance to better understand the biology of bats and design conservation strategies. Although the advent of metabarcoding has facilitated such analyses, this approach does not come without challenges. Biases may occur throughout the whole experiment, from fieldwork to biostatistics, resulting in the detection of false negatives, false positives or low taxonomic resolution. We detail a rigorous metabarcoding approach based on a short COI minibarcode and two-step PCR protocol enabling the 'all at once' taxonomic identification of bats and their arthropod preys for several hundreds of samples. Our study includes faecal pellets collected in France from 357 bats representing 16 species, as well as insect mock communities that mimic bat meals of known composition, negative and positive controls. All samples were analysed using three replicates. We compare the efficiency of DNA extraction methods and we evaluate the effectiveness of our protocol using identification success, taxonomic resolution, sensitivity, and amplification biases. Our parallel identification strategy of predators and preys reduces the risk of mis-assigning preys to wrong predators and decreases the number of molecular steps. Controls and replicates enable to filter the data and limit the risk of false positives, hence guaranteeing high confidence results for both prey occurrence and bat species identification. We validate 551 COI variants from arthropod including 18 orders, 117 family, 282 genus and 290 species. Our method therefore provides a rapid, resolutive and cost-effective screening tool for addressing evolutionary ecological issues or developing 'chirosurveillance' and conservation strategies.
Metabarcoding for parallel identification of species, sex and diet of obligate scavengers: an application to globally-threatened Gyps vultures
Open the record for dataset details and reuse information.
Data from: Metabarcoding for the parallel identification of several hundred predators and their preys: application to bat species diet analysis
Open the record for dataset details and reuse information.
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Performance Curves for better Performance Predictions of Parallel Applications in Multicore Environments
<p><br> Model-based performance prediction for parallel applications on architectural models suffers from significant inaccuracies. <br> A major reason is that current model-based performance prediction approaches consider CPU speed as a single metric for multicore performance. </p> <p>Thus, in this paper, we investigate performance-influencing factors for multicore environments, execute extensive experiments to determine their impact on the performance, and extract performance curves for characteristic behaviours.</p> <p>As a result, we present a set of performance curves to software engineers which enables them to increase the performance prediction power in an easy to use manner. <br> Further, we evaluate the approach using 13 SPEC Benchmarks and could show that our approach reduces the prediction error by up to 60% and increases the accuracy by up to 98% for certain scenarios. </p>
Data for paper StarVZ: Performance Analysis of Task-Based Parallel Applications
<p>This is the companion data of the paper <em>StarVZ: Performance Analysis of Task-Based Parallel Applications</em> submitted to Journal of Statistical Software at Oct 2020.</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.