mirror of https://github.com/BOINC/boinc.git
Updated sample plan classes to show newer features.
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@ -22,6 +22,7 @@
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<gpu_ram_used_mb> 250 </gpu_ram_used_mb>
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<gpu_ram_used_mb> 250 </gpu_ram_used_mb>
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<gpu_peak_flops_scale> .23 </gpu_peak_flops_scale>
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<gpu_peak_flops_scale> .23 </gpu_peak_flops_scale>
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<cpu_frac> .1 </cpu_frac>
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<cpu_frac> .1 </cpu_frac>
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<without_opencl/>
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</plan_class>
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</plan_class>
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<plan_class>
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<plan_class>
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<name> cuda </name>
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<name> cuda </name>
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@ -48,6 +49,19 @@
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<gpu_peak_flops_scale> .22 </gpu_peak_flops_scale>
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<gpu_peak_flops_scale> .22 </gpu_peak_flops_scale>
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<cpu_frac> .01 </cpu_frac>
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<cpu_frac> .01 </cpu_frac>
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</plan_class>
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</plan_class>
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<plan_class>
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<name> cuda42 </name>
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<gpu_type> nvidia </gpu_type>
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<cuda/>
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<min_cuda_version> 4020 </min_cuda_version>
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<min_driver_version> 30124 </min_driver_version>
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<min_nvidia_compcap> 200 </min_nvidia_compcap>
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<ngpus> 1 </ngpus>
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<min_gpu_ram_mb> 384 </min_gpu_ram_mb>
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<gpu_ram_used_mb> 384 </gpu_ram_used_mb>
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<gpu_peak_flops_scale> 0.22 </gpu_peak_flops_scale>
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<cpu_frac> 0.01 </cpu_frac>
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</plan_class>
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<plan_class>
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<plan_class>
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<name> nci </name>
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<name> nci </name>
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<avg_ncpus> .01 </avg_ncpus>
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<avg_ncpus> .01 </avg_ncpus>
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@ -62,6 +76,7 @@
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<name> opencl_nvidia_101 </name>
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<name> opencl_nvidia_101 </name>
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<gpu_type> nvidia </gpu_type>
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<gpu_type> nvidia </gpu_type>
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<opencl/>
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<opencl/>
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<min_core_client_version>70000 </min_core_client_version>
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<min_opencl_version> 101 <min_opencl_version>
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<min_opencl_version> 101 <min_opencl_version>
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<min_gpu_ram_mb> 256 </min_gpu_ram_mb>
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<min_gpu_ram_mb> 256 </min_gpu_ram_mb>
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<gpu_ram_used_mb> 256 </gpu_ram_used_mb>
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<gpu_ram_used_mb> 256 </gpu_ram_used_mb>
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@ -72,6 +87,7 @@
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<name> opencl_ati_101 </name>
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<name> opencl_ati_101 </name>
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<gpu_type> amd </gpu_type>
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<gpu_type> amd </gpu_type>
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<opencl/>
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<opencl/>
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<min_core_client_version>70000 </min_core_client_version>
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<min_opencl_version> 101 <min_opencl_version>
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<min_opencl_version> 101 <min_opencl_version>
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<min_gpu_ram_mb> 256 </min_gpu_ram_mb>
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<min_gpu_ram_mb> 256 </min_gpu_ram_mb>
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<gpu_ram_used_mb> 256 </gpu_ram_used_mb>
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<gpu_ram_used_mb> 256 </gpu_ram_used_mb>
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