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001 286776
003 MX-SnUAN
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008 150903s2013 xxk| o |||| 0|eng d
020 _a9781447144922
_99781447144922
024 7 _a10.1007/9781447144922
_2doi
035 _avtls000339835
039 9 _a201509030841
_bVLOAD
_c201404300405
_dVLOAD
_y201402060945
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTK5105.5-5105.9
100 1 _aLaros III, James H.
_eautor
_9315635
245 1 0 _aEnergy-Efficient High Performance Computing :
_bMeasurement and Tuning /
_cby James H. Laros III, Kevin Pedretti, Suzanne M. Kelly, Wei Shu, Kurt Ferreira, John Van Dyke, Courtenay Vaughan.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _axiv, 67 páginas 19 ilustraciones, 8 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringerBriefs in Computer Science,
_x2191-5768
500 _aSpringer eBooks
505 0 _aIntroduction -- Platforms -- Measuring Power -- Applications -- Reducing Power During Idle Cycles -- Tuning CPU Power During Application Run-Time -- Network Bandwidth Tuning During Application Run-Time -- Energy Delay Product -- Conclusions.
520 _aRecognition of the importance of power and energy in the field of high performance computing (HPC) has never been greater. Research has been conducted in a number of areas related to power and energy, but little existing research has focused on large-scale HPC. Part of the reason is the lack of measurement capability currently available on small or large platforms. Typically, research is conducted using coarse methods of measurement such as inserting a power meter between the power source and the platform, or fine grained measurements using custom instrumented boards (with obvious limitations in scale). To analyze real scientific computing applications at large scale, an in situ measurement capability is necessary that scales to the size of the platform. In response to this challenge, the unique power measurement capabilities of the Cray XT architecture were exploited to gain an understanding of power and energy use and the effects of tuning both CPU and network bandwidth. Modifications were made at the operating system level to deterministically halt cores when idle. Additionally, capabilities were added to alter operating P-state. At the application level, an understanding of the power requirements of a range of important DOE/NNSA production scientific computing applications running at large scale (thousands of nodes) is gained by simultaneously collecting current and voltage measurements on the hosting nodes. The effects of both CPU and network bandwidth tuning are examined and energy savings opportunities of up to 39% with little or no impact on run-time performance is demonstrated. Capturing scale effects was key. This research provides strong evidence that next generation large-scale platforms should not only approach CPU frequency scaling differently, as we will demonstrate, but could also benefit from the capability to tune other platform components, such as the network, to achieve more energy efficient performance.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aPedretti, Kevin.
_eautor
_9315636
700 1 _aKelly, Suzanne M.
_eautor
_9315637
700 1 _aShu, Wei.
_eautor
_9315638
700 1 _aFerreira, Kurt.
_eautor
_9315639
700 1 _aVan Dyke, John.
_eautor
_9315640
700 1 _aVaughan, Courtenay.
_eautor
_9315641
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781447144915
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-4492-2
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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