103,018 research outputs found
NPB Benchmark Kernels for GPU with CUDA
No full textNPB Benchmark Kernels for GPU with CUDA
Reference Paper Citation
[DOI] Araujo, G. A. ; Griebler, D. ; Danelutto, M. ; Fernandes, L. G. Efficient NAS Benchmark Kernels with CUDA. 28th Euromicro International Conference on Parallel, Distributed and Networkbased Processing (PDP), 2020
GPU acceleration for statistical gene classification
No full textThe use of Bioinformatic tools in routine clinical diagnostics is still facing a number of issues. The more complex and advanced bioinformatic tools become, the more performance is required by the computing platforms. Unfortunately, the cost of parallel computing platforms is usually prohibitive for both public and small private medical practices. This paper presents a successful experience in using the parallel processing capabilities of Graphical Processing Units (GPU) to speed up bioinformatic tasks such as statistical classification of gene expression profiles. The results show that using open source CUDA programming libraries allows to obtain a significant increase in performances and therefore to shorten the gap between advanced bioinformatic tools and real medical practic
GPU cards as a low cost solution for efficient and fast classification of high dimensional gene expression datasets
No full textThe days when bioinformatics tools will be so reliable to become a standard aid in routine clinical diagnostics are getting very close. However, it is important to remember that the more complex and advanced bioinformatics tools become, the more performances are required by the computing platforms. Unfortunately, the cost of High Performance Computing (HPC) platforms is still prohibitive for both public and private medical practices. Therefore, to promote and facilitate the use of bioinformatics tools it is important to identify low-cost parallel computing solutions. This paper presents a successful experience in using the parallel processing capabilities of Graphical Processing Units (GPU) to speed up classification of gene expression profiles. Results show that using open source CUDA programming libraries allows to obtain a significant increase in performances and therefore to shorten the gap between advanced bioinformatics tools and real medical practic
Comparison between liberatory manoeuvre and otolith repositioning manoeuvre in the treatment of benign paroxysmal positional vertgo [Confronto fra manovra liberatoria e riposizionamento otolitico nel trattamento della vertigine parossistica posizionale benigna]
No full textAn investigation of the performance portability of OpenCL
No full textThis paper reports on the development of an MPI/OpenCL implementation of LU, an application-level benchmark from the NAS Parallel Benchmark Suite. An account of the design decisions addressed during the development of this code is presented, demonstrating the importance of memory arrangement and work-item/work-group distribution strategies when applications are deployed on different device types. The resulting platform-agnostic, single source application is benchmarked on a number of different architectures, and is shown to be 1.3–1.5× slower than native FORTRAN 77 or CUDA implementations on a single node and 1.3–3.1× slower on multiple nodes. We also explore the potential performance gains of OpenCL’s device fissioning capability, demonstrating up to a 3× speed-up over our original OpenCL implementation
Focused tight dressing does not prevent cochlear implant magnet migration under 1.5 Tesla MRI
No full text
An application of ehealth technology toward the digitization of the health records of older patients with cochlear implants
No full textPurpose: Despite the current legislative indications toward the digitization of patient health records, 80% of health data are unstructured and in a format that cannot be used in electronic archives or in registries of diseases. An innovative automated system is here proposed to efficiently retrieve and digitize clinical information from original unstructured ear, nose, and throat (ENT) medical records, in order to reduce the manual workload in the retrieval and digitization process. Method: The system, based on an eHealth technology named cognitive computing, interprets medical reports to transform unstructured clinical data (e.g., narrative text) into a structured digital format. The system has been tailored to handle the reports of aged cochlear implant (CI) patients by digitizing the information typically requested in electronic CI registries and by the current ENT/audiology guidelines. Results were obtained from the reports generated by an outpatient ENT care service from 52 older adult CI patients. Results: The system allowed a quick and automated interpretation and retrieval of all the information required, such as the patient’s medical history, risk factors, examination outcomes, communicative performances before and after CI implantation, and CI settings. The accuracy of the system in correctly interpreting and retrieving the above information from the original unstructured medical reports was very good (recall = 0.78; precision = 0.95). The system allowed to reduce the time needed to manually digitize the information from 20-30 min/report to only 20 s/report. Conclusion: The proposed system is a viable solution for the automated digitization of unstructured health data as recommended by the ENT/audiology clinical best practices
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