000 | 03682nam a22005415i 4500 | ||
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001 | 978-3-030-14568-2 | ||
003 | DE-He213 | ||
005 | 20240423125052.0 | ||
007 | cr nn 008mamaa | ||
008 | 190417s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030145682 _9978-3-030-14568-2 |
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024 | 7 |
_a10.1007/978-3-030-14568-2 _2doi |
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050 | 4 | _aQA76.9.A43 | |
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_aUMB _2bicssc |
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_a518.1 _223 |
100 | 1 |
_aSedighi, Art. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aFair Scheduling in High Performance Computing Environments _h[electronic resource] / _cby Art Sedighi, Milton Smith. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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300 |
_aXI, 132 p. 77 illus., 74 illus. in color. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aChapter 1 Introduction 1 -- Chapter 2 Financial Market Risk 9 -- Chapter 3 Scheduling in High Performance Computing 24 -- Chapter 4 Fairshare Scheduling 33 -- Chapter 5 Multi-Criteria Scheduling: A Mathematical Model 43 -- Chapter 6 Simulation & Methodology 56 -- Chapter 7 DSIM 67 -- Chapter 8 Simulation Scenarios 73 -- Chapter 9 Overview of Results 90 -- Chapter 10 Class A Results and Analysis 101 -- Chapter 11 Class B Results and Analysis 118 -- Chapter 12 Class C Results and Analysis 139 -- Chapter 13 Class D Results and Simulations 153 -- Chapter 14 Conclusion 173. . | |
520 | _aThis book introduces a new scheduler to fairly and efficiently distribute system resources to many users of varying usage patterns compete for them in large shared computing environments. The Rawlsian Fair scheduler developed for this effort is shown to boost performance while reducing delay in high performance computing workloads of certain types including the following four types examined in this book: i. Class A – similar but complementary workloads ii. Class B – similar but steady vs intermittent workloads iii. Class C – Large vs small workloads iv. Class D – Large vs noise-like workloads This new scheduler achieves short-term fairness for small timescale demanding rapid response to varying workloads and usage profiles. Rawlsian Fair scheduler is shown to consistently benefit workload Classes C and D while it only benefits Classes A and B workloads where they become disproportionate as the number of users increases. A simulation framework, dSim, simulates the new Rawlsian Fair scheduling mechanism. The dSim helps achieve instantaneous fairness in High Performance Computing environments, effective utilization of computing resources, and user satisfaction through the Rawlsian Fair scheduler. | ||
650 | 0 | _aAlgorithms. | |
650 | 0 | _aComputer networks . | |
650 | 0 | _aMicroprocessors. | |
650 | 0 | _aComputer architecture. | |
650 | 1 | 4 | _aAlgorithms. |
650 | 2 | 4 | _aComputer Communication Networks. |
650 | 2 | 4 | _aProcessor Architectures. |
700 | 1 |
_aSmith, Milton. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030145675 |
776 | 0 | 8 |
_iPrinted edition: _z9783030145699 |
776 | 0 | 8 |
_iPrinted edition: _z9783030145705 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-14568-2 |
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