000 04237nam a22005655i 4500
001 978-1-4471-4492-2
003 DE-He213
005 20200421112226.0
007 cr nn 008mamaa
008 120904s2013 xxk| s |||| 0|eng d
020 _a9781447144922
_9978-1-4471-4492-2
024 7 _a10.1007/978-1-4471-4492-2
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM075000
_2bisacsh
082 0 4 _a004.6
_223
100 1 _aLaros III, James H.
_eauthor.
245 1 0 _aEnergy-Efficient High Performance Computing
_h[electronic resource] :
_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 p. 19 illus., 8 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
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.
650 0 _aComputer science.
650 0 _aComputer software
_xReusability.
650 0 _aComputer communication systems.
650 0 _aOperating systems (Computers).
650 1 4 _aComputer Science.
650 2 4 _aComputer Communication Networks.
650 2 4 _aPerformance and Reliability.
650 2 4 _aOperating Systems.
700 1 _aPedretti, Kevin.
_eauthor.
700 1 _aKelly, Suzanne M.
_eauthor.
700 1 _aShu, Wei.
_eauthor.
700 1 _aFerreira, Kurt.
_eauthor.
700 1 _aVan Dyke, John.
_eauthor.
700 1 _aVaughan, Courtenay.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447144915
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-4492-2
912 _aZDB-2-SCS
942 _cEBK
999 _c57667
_d57667