“…Additionally, Jabba corrects reads using a very low amount of CPU time. From this we conclude that pseudo alignment with MEMs is a fast and reliable method to map long highly erroneous sequences on a de Bruijn graph.…”
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“…Genetic maps for each human population (YRI, CEU, and CHB) extracted from the 1000 Genomes OMNI data set were obtained in less than 3 d using just a single CPU core. The Pearson Pairwise correlation coefficient between the [Formula: see text] and [Formula: see text] maps is very high, ranging between 0.929 and 0.987 at a 5-Mb scale. …”
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“…Our high-throughput predictor can be used on the whole-proteome scale; it needs <1 h to predict entire proteome on a single CPU. When assessed on an independent test dataset with low sequence-identity proteins, it secures area under the receiver operating characteristic curve equal 0.715 and outperforms existing alternatives that include methods for the prediction of flexible linkers, flexible residues, intrinsically disordered residues and various combinations of these methods. …”
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“…GGMAP can map millions of reads per CPU hour on a de Bruijn graph built from a large set of human genomic reads. …”
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“…As a result of the performance investigation, the new preconditioner indicates the following benefits: (1) The new preconditioner is robust; i.e., it converges while conventional preconditioners (the diagonal scaling, and the SSOR preconditioners) fail. (2) In the best case scenarios, it is over 10 times faster than conventional preconditioners on a CPU. (3) Because it requries only simple operations, it performs well on a GPGPU. …”
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“…Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. …”
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“…They have the following features: are fully automatic; allow for implementation of fast segmentation methods; are profiled to hyperspectral image segmentation; use emissivity curves in the model form, can be applied in any type of objects not necessarily biological ones, are faster (SKE—2.3 ms, S3D—1949 ms, SH—844 ms for the computer with Intel(®) Core i7 4960X CPU 3.6 GHz) and more accurate (SKE—accuracy 79 %, S3D—90 %, SH—92 %) in comparison with typical methods known from the literature. …”
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“…RESULTS: The whole-genome joint association analysis was completed in 80 min on a high-performance personal computer with an i7 4700 CPU and 8 GB memory. Although no gene reached statistical significance after adjusting for multiple testing, some top-ranked genes attained a high significance level and may have biological plausibility to hypertension-related phenotypes. …”
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“…RESULTS: The output of OrthoReD was highly similar to the outputs of two other published orthology prediction tools, OrthologID and/or OrthoDB, for the three dataset tested, which represented three phyla with different ranges of species diversity and different number of genomes included. Median CPU time for ortholog prediction per gene by OrthoReD executed on a desktop computer was <15 min even for the largest dataset tested, which included all coding sequences of 100 bacterial species. …”
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“…MIST has a performance-oriented implementation utilizing multicore hybrid CPU/GPU computing resources, which can process terabytes of time-lapse multi-channel mosaics 15 to 100 times faster than existing tools. …”
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“…In our previous work, a bioinformatics platform with a single TK1 (STK platform) was constructed, and this previous work is also used to prove that the Web and mobile services can be implemented in the STK platform with a good cost-performance ratio by comparing a STK platform with the desktop CPU and GPU. In this work, an embedded-based GPU cluster platform will be constructed with multiple TK1s (MTK platform). …”
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“…The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just-in-time optimization and compilation of the model onto CPU or GPU architectures. An alternative implementation based on Theano (Theano Development Team, 2016) is also available, although not fully supported. …”
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“…The processing time of one sample is less than 10 CPU-minutes on a high-end workstation. NIPTmer was validated on a cohort of 583 NIPT samples and it correctly predicted 37 non-mosaic fetal aneuploidies. …”
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“…Using a GPU, the simulation on the chest CT dataset aiming at the heart was within 3.49 s on average: the GPU is 122 times faster than a CPU (Core i7–7700K; Intel Corp.). Our system (using the GPU, the log file, and the CT dataset) estimated the skin dose more rapidly and more accurately than conventional methods.…”
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“…Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.…”
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“…METHODS: We developed a new web-based tool The Cancer Omics Atlas (TCOA, http://tcoa.cpu.edu.cn) for fast and straightforward querying of TCGA “omics” data. …”
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“…BrownieAligner relies on multithreading to take advantage of multi-core/multi-CPU systems. The source code is available at: https://github.com/biointec/browniealigner ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2319-7) contains supplementary material, which is available to authorized users.…”
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“…The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. …”
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“…It takes about 6 seconds to process a sequence of ten frames (on a 2.6 GHZ CPU computer). For those cameras with excessive empty frames due to camera malfunction or blowing vegetation automatically removes 54% of the false‐triggers sequences without influencing the human/animal sequences. …”
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“…Experimental validation on a fresh human femur specimen together with femur strength computations that were consistent with literature findings provide confidence in the workflow: The simulation of an entire squat took only 38 s CPU-time. Owing to the loss (16% cortical, 33% trabecular) of bone mineral density (BMD), the strain measure that is associated with bone fracture increased by 31.4%; and yielded an elevated risk of a femoral hip fracture. …”
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