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App config.xml: Unterschied zwischen den Versionen
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| (2 dazwischenliegende Versionen desselben Benutzers werden nicht angezeigt) | |||
| Zeile 82: | Zeile 82: | ||
<gpu_usage>0.5</gpu_usage> | <gpu_usage>0.5</gpu_usage> | ||
<cpu_usage>1.0</cpu_usage> | <cpu_usage>1.0</cpu_usage> | ||
| + | </gpu_versions> | ||
| + | </app> | ||
| + | </app_config> | ||
| + | ===[[Moo!Wrapper]]=== | ||
| + | <app_config> | ||
| + | <app> | ||
| + | <name>dnetc</name> | ||
| + | <gpu_versions> | ||
| + | <gpu_usage>0.5</gpu_usage> | ||
| + | <cpu_usage>0.05</cpu_usage> | ||
</gpu_versions> | </gpu_versions> | ||
</app> | </app> | ||
| Zeile 330: | Zeile 340: | ||
<avg_ncpus>4</avg_ncpus> | <avg_ncpus>4</avg_ncpus> | ||
</app_version> | </app_version> | ||
| + | </app_config> | ||
| + | ===[[YAFU]]=== | ||
| + | <app_config> | ||
| + | <app> | ||
| + | <name>yafu-64t</name> | ||
| + | <max_concurrent>128</max_concurrent> | ||
| + | </app> | ||
| + | <app> | ||
| + | <name>yafu-128t</name> | ||
| + | <max_concurrent>128</max_concurrent> | ||
| + | </app> | ||
| + | </app_config> | ||
| + | ===[[Yoyo]]=== | ||
| + | <app_config> | ||
| + | <app> | ||
| + | <name>ecmP2</name> | ||
| + | <max_concurrent>1</max_concurrent> | ||
| + | <fraction_done_exact>0</fraction_done_exact> | ||
| + | <report_results_immediately>0</report_results_immediately> | ||
| + | </app> | ||
| + | <app_version> | ||
| + | <app_name>ecmP2</app_name> | ||
| + | <cmdline></cmdline> | ||
| + | <avg_ncpus>1.000000</avg_ncpus> | ||
| + | </app_version> | ||
| + | <app> | ||
| + | <name>ecm</name> | ||
| + | <max_concurrent>7</max_concurrent> | ||
| + | <fraction_done_exact>0</fraction_done_exact> | ||
| + | <report_results_immediately>0</report_results_immediately> | ||
| + | </app> | ||
| + | <app_version> | ||
| + | <app_name>ecm</app_name> | ||
| + | <cmdline></cmdline> | ||
| + | <avg_ncpus>1.000000</avg_ncpus> | ||
| + | </app_version> | ||
| + | <project_max_concurrent>8</project_max_concurrent> | ||
| + | <report_results_immediately>0</report_results_immediately> | ||
</app_config> | </app_config> | ||
Aktuelle Version vom 6. Mai 2023, 07:15 Uhr
Mit einer app_config.xml können Parameter für die Projektberechnung festgelegt werden, die sich über die Projektseite nicht festlegen lassen. Per Suchmaschine lassen sich alle Parameter finden. Für unsere Zwecke geht es um das Einstellen:
- wieviele GPU-WUs von welchem Projekt gleichzeitig, mit wievielen CPU-Kernen, rechnen sollen
- von CPU-Projekten die Multithreading betreiben
- der maximalen Anzahl von WUs, eines Projektes/Unterprojektes, die gleichzeitig gerechnet werden sollen
Werden diese Informationen im Race-Thread oder Plauderchat bekannt gegeben, gehen sie nach ein paar Seiten wieder unter.
Die app_config.xml Datei z. B. mit dem Notepad erstellen und in den jeweiligen BOINC-Projektordner "C:\ProgramData\BOINC\projects\..." abspeichern. Darauf achten, dass die Datei nicht "app_config.xml.txt" heißt.
Analog gilt bei Linux dann idR. /var/lib/boinc-client/projects/... oder /var/lib/boinc/projects/...
Amicable Numbers
<app_config>
<app>
<name>amicable_10_21</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>1.0</cpu_usage>
</gpu_versions>
</app>
</app_config>
Asteroids@home
Anpassung der maximal gleichzeitig gerechneten CPU-Workunits mittels des Tags max_concurrent, da die Workunits die Recheneinheiten und Caches sehr gut auslasten.
<app_config>
<app>
<name>period_search</name>
<max_concurrent>12</max_concurrent>
</app>
</app_config>
Collatz Conjecture
<app_config>
<app>
<name>collatz_sieve</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>1.0</cpu_usage>
</gpu_versions>
</app>
</app_config>
Einstein@Home
<app_config>
<app>
<name>hsgamma_FGRPB1G</name>
<gpu_versions>
<gpu_usage>0.333</gpu_usage>
<cpu_usage>1.0</cpu_usage>
</gpu_versions>
</app>
<app>
<name>einstein_O2MDF</name>
<gpu_versions>
<gpu_usage>0.2</gpu_usage>
<cpu_usage>1.0</cpu_usage>
</gpu_versions>
</app>
</app_config>
MilkyWay@home
<app_config>
<app>
<name>milkyway</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.05</cpu_usage>
</gpu_versions>
</app>
<app>
<name>milkyway_separation__modified_fit</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.05</cpu_usage>
</gpu_versions>
</app>
</app_config>
MLC@home
<app_config>
<app>
<name>mlds-gpu</name>
<gpu_versions>
<gpu_usage>0.5</gpu_usage>
<cpu_usage>1.0</cpu_usage>
</gpu_versions>
</app>
</app_config>
Moo!Wrapper
<app_config>
<app>
<name>dnetc</name>
<gpu_versions>
<gpu_usage>0.5</gpu_usage>
<cpu_usage>0.05</cpu_usage>
</gpu_versions>
</app>
</app_config>
PrimeGrid
Für Nvidia und Sieve
<app_config>
<app_version>
<app_name>pps_sr2sieve</app_name>
<plan_class>cudaPPSsieve</plan_class>
<cmdline>-m64</cmdline>
<avg_ncpus>1</avg_ncpus>
<ngpus>0.5</ngpus>
</app_version>
</app_config>
Für AMD und Sieve
<app_config> <app_version> <app_name>pps_sr2sieve</app_name> <plan_class>openclatiPPSsieve</plan_class> <cmdline>-m16 --vecsize=1</cmdline> <avg_ncpus>1</avg_ncpus> <ngpus>0.5</ngpus> </app_version> </app_config>
Für alle GPU-Unterprojekte
<app_config>
<app>
<name>pps_sr2sieve</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>1.0</cpu_usage>
</gpu_versions>
</app>
<app>
<name>ap26</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>1.0</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer_wr</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer15</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer16</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer17low</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer17mega</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer18</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer19</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer20</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>genefer_extreme</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
<app>
<name>ww</name>
<gpu_versions>
<gpu_usage>1.0</gpu_usage>
<cpu_usage>0.084</cpu_usage>
</gpu_versions>
</app>
</app_config>
Private GFN Server
Multithreading
<app_config>
<app_version>
<app_name>gfn13_mega</app_name>
<max_concurrent>1</max_concurrent>
<report_results_immediately/>
<cmdline>-t 4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>llr2</app_name>
<max_concurrent>1</max_concurrent>
<report_results_immediately/>
<cmdline>-t 4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
</app_config>
SRBase
Multithreading
<app_config>
<app>
<name>srbase</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase2</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase3</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase4</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase5</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase6</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase7</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase8</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase9</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase10</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase11</name>
<max_concurrent>1</max_concurrent>
</app>
<app>
<name>srbase12</name>
<max_concurrent>1</max_concurrent>
</app>
<app_version>
<app_name>srbase</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase2</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase3</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase4</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase5</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase6</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase7</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase8</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase9</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase10</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase11</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
<app_version>
<app_name>srbase12</app_name>
<cmdline>-t4</cmdline>
<avg_ncpus>4</avg_ncpus>
</app_version>
</app_config>
YAFU
<app_config>
<app>
<name>yafu-64t</name>
<max_concurrent>128</max_concurrent>
</app>
<app>
<name>yafu-128t</name>
<max_concurrent>128</max_concurrent>
</app>
</app_config>
Yoyo
<app_config>
<app>
<name>ecmP2</name>
<max_concurrent>1</max_concurrent>
<fraction_done_exact>0</fraction_done_exact>
<report_results_immediately>0</report_results_immediately>
</app>
<app_version>
<app_name>ecmP2</app_name>
<cmdline></cmdline>
<avg_ncpus>1.000000</avg_ncpus>
</app_version>
<app>
<name>ecm</name>
<max_concurrent>7</max_concurrent>
<fraction_done_exact>0</fraction_done_exact>
<report_results_immediately>0</report_results_immediately>
</app>
<app_version>
<app_name>ecm</app_name>
<cmdline></cmdline>
<avg_ncpus>1.000000</avg_ncpus>
</app_version>
<project_max_concurrent>8</project_max_concurrent>
<report_results_immediately>0</report_results_immediately>
</app_config>