Materias dentro de su búsqueda.
Mostrando 1,361 - 1,380 Resultados de 2,741 Para Buscar '"CPU"', tiempo de consulta: 0.23s Limitar resultados
  1. 1361
    “…Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.…”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences
  2. 1362
    “…On a collection of 168,311 bacterial genomes, totalling 587 GB, we achieve a compression ratio of approximately a factor of 1,265 and compression (respectively decompression) speed of ∼1,580 MB/s (respectively 780 MB/s) using 8 hardware threads, on a computer with a 14-core/28-thread CPU and a fast SSD, being almost 3 times more succinct and >6 times faster in the compression than the next best competitor.…”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    MBGC: Multiple Bacteria Genome Compressor
  3. 1363
    “…Our results show that the neuromorphic solution is about 2.5 times more energy-efficient compared with an ARM Cortex-A72 CPU and 12.5 times more energy-efficient compared with NVIDIA T4 GPU for inference by a lightweight convolutional neural network when batch size is 1 while maintaining the same level of matching accuracy. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Neuromorphic computing for content-based image retrieval
  4. 1364
    “…NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models
  5. 1365
    “…Eventually, with the proposed CPU+ FPGA framework, we performed experiments and compared the performance against traditional computation strategies in terms of the operation efficiency and energy consumption ratio. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Designing Deep Learning Hardware Accelerator and Efficiency Evaluation
  6. 1366
    “…Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. …”
    Enlace del recurso
    Enlace del recurso
    Enlace del recurso
    Online Artículo Texto
    Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources
  7. 1367
    “…Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) ms with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. …”
    Enlace del recurso
    Enlace del recurso
    FPGA-accelerated machine learning inference as a service for particle physics computing
  8. 1368
    por Stokes-Rees, Ian James
    Publicado 2006
    “…This package was designed to provide for the LHCb particle physics experiment's â€ﾜoff-line” computational infrastructure, and was first exercised during a 6 month data challenge which utilised over 670 years of CPU time and produced 98 TB of data through 300,000 tasks executed at computing centres around the world. …”
    Enlace del recurso
    A REST Model for High Throughput Scheduling in Computational Grids
  9. 1369
    “…The system monitors all the components: computer clusters (all major parameters of each computing node), jobs status and consumed resources (CPU, both in time and SpecInt2k units, memory, disk usage), jobs network traffic while reading/writing files with xrootd, services availability with details in case of failures (both AliEn and LCG services, proxies lifetime), storage monitoring with detailed information on number of files, available space, or staging and migrating operations, FTD/FTS transfers. …”
    Enlace del recurso
    Monitoring, accounting and automated decision support for the ALICE experiment based on the MonALISA framework
  10. 1370
    por Kruse, Daniele Francesco
    Publicado 2011
    “…CPU clock frequency is not likely to be increased significantly in the coming years, and data analysis speed can be improved by using more processors or buying new machines, only if one is willing to change the programming paradigm to a parallel one. …”
    Enlace del recurso
    CMSSW Performance Monitoring on processors based on the Intel Core and Nehalem Microarchitectures
  11. 1371
    “…The HLT algorithms consist of sequences of offline-style reconstruction and filtering modules, executed on a farm of 0(10000) CPU cores built from commodity hardware. Experience from the operation of the HLT system in the collider run 2010/2011 is reported. …”
    Enlace del recurso
    The CMS High Level Trigger System: Experience and Future Development
  12. 1372
    “…Frequent validation and stress testing of the network, storage and CPU resources of a grid site is essential to achieve high performance and reliability. …”
    Enlace del recurso
    Experience in Grid Site Testing for ATLAS, CMS and LHCb with HammerCloud
  13. 1373
    “…However, our experience showed the weakness of this approach both in terms of low total job execution efficiency and failure rates, wasting precious CPU resources. The nature of analysis data makes it inappropriate to use PhEDEx, the core data placement system for CMS. …”
    Enlace del recurso
    CHEP2012
  14. 1374
    por Spiga, Daniele
    Publicado 2012
    “…However, our experience showed the weakness of this approach both in terms of low total job execution efficiency and failure rates, wasting precious CPU resources. The nature of analysis data makes it inappropriate to use PhEDEx, CMS' core data placement system. …”
    Enlace del recurso
    A gLite FTS based solution for managing user output in CMS
  15. 1375
    por Lukas, W
    Publicado 2012
    “…As these simulated data sets must be both large and precise, their production is a CPU-intensive task. Increasing the recorded luminosity at the Large Hadron Collider (LHC), and hence the amount of data to be analyzed, leads to a steadily rising demand for simulated MC statistics for systematics and background studies. …”
    Enlace del recurso
    Enlace del recurso
    Fast Simulation for ATLAS: Atlfast-II and ISF
  16. 1376
    por Pordes, Ruth
    Publicado 2012
    “…We discuss examples of these including: the pilot-job overlay concepts and technologies now in use throughout OSG and delivering 1.4 Million CPU hours/day; the role of campus infrastructures- built out from concepts of sharing across multiple local faculty clusters (made good use of already by many of the HEP Tier-2 sites in the US); the work towards the use of clouds and access to high throughput parallel (multi-core and GPU) compute resources; and the progress we are making towards meeting the data management and access needs of non-HEP communities with general tools derived from the experience of the pariochial tools in HEP (integration of Globus Online, prototyping with IRODS, investigations into Wide Area Lustre). …”
    Enlace del recurso
    The Open Science Grid – Support for Multi-Disciplinary Team Science – the Adolescent Years
  17. 1377
    “…Detector simulation is one of the most CPU intensive tasks in modern High Energy Physics. …”
    Enlace del recurso
    Enlace del recurso
    Rethinking particle transport in the many-core era towards GEANT 5
  18. 1378
    “…The gain in redundancy and reliability comes with a trade-off in the following areas: Increased complexity of the network connectivity CPU intensive parity computations during file creation and recovery Performance loss through remote disk coupling An evaluation and performance figures of several redundancy algorithms are presented for dual parity RAID and Reed-Solomon codecs. …”
    Enlace del recurso
    Enlace del recurso
    Evaluation of software based redundancy algorithms for the EOS storage system at CERN
  19. 1379
    “…Complimenting this,\nGOoDA, an in-house performance tool built in collaboration with\nGoogle, which is based on hardware performance monitoring unit events,\nis used to identify hot-spots in the code for different types of\nhardware limitations, such as CPU resources, caches, or memory\nbandwidth. GOoDA has been used in improvement of the performance of\nnew magnetic field code and identification of potential vectorization\ntargets in several places, such as Runge-Kutta propagation code.…”
    Enlace del recurso
    Enlace del recurso
    Optimizing ATLAS code with different profilers
  20. 1380
    “…During the first proof of concept, this project contributed over 40.000 CPU-days of Monte Carlo production throughput to the ATLAS experiment with marginal manpower required. …”
    Enlace del recurso
    Helix Nebula and CERN: A Symbiotic approach to exploiting commercial clouds
Herramientas de búsqueda: RSS