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Allocations and compute scheduling: Difference between revisions
→What is a GPU equivalent and how is it used by the scheduler?
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Given this separation, a distinction must be made between core equivalents and GPU equivalents. Core equivalents are as described above. The GPU-core-memory bundles that make up a GPU equivalent are similar to core-memory bundles except that a GPU is added to the bundle alongside multiple cores and memory. This means that accounting for GPU-based allocation targets must include the GPU. Similar to how the points system was used above when considering resource use as an expression of the concept of core equivalence, we will use a similar point system here as an expression of GPU equivalence. | Given this separation, a distinction must be made between core equivalents and GPU equivalents. Core equivalents are as described above. The GPU-core-memory bundles that make up a GPU equivalent are similar to core-memory bundles except that a GPU is added to the bundle alongside multiple cores and memory. This means that accounting for GPU-based allocation targets must include the GPU. Similar to how the points system was used above when considering resource use as an expression of the concept of core equivalence, we will use a similar point system here as an expression of GPU equivalence. | ||
[[File:GPU_equivalent_diagram.png|frame|Figure 4 - GPU equivalent diagram.]] | |||
Research groups are charged for the maximum number of GPU-core-memory bundles they use. Assuming a core-memory bundle of 1 GPU, 6 cores, and 32GB of memory: | Research groups are charged for the maximum number of GPU-core-memory bundles they use. Assuming a core-memory bundle of 1 GPU, 6 cores, and 32GB of memory: | ||
[[File:Two_GPU_equivalents.png|frame|Figure 5 - Two GPU equivalents.]] | * [[File:Two_GPU_equivalents.png|frame|Figure 5 - Two GPU equivalents.]] Research groups using more GPUs than cores or memory per GPU-core-memory bundle will be charged by GPU. For example, a research group requests 2 GPUs, 6 cores, and 32GB of memory. The request is for 2 GPU-core-memory bundles worth of GPUs but only one bundle for memory and cores. This job request will be counted as 2 core equivalents when the research group’s priority is calculated. | ||
* [[File:GPU_and_a_half_(cores).png|frame|Figure 6 - 1.5 GPU equivalents, based on cores.]] Research groups using more cores than GPUs or memory per GPU-core-memory bundle will be charged by core. For example, a researcher requests 1 GPU, 9 cores, and 32GB of memory. The request is for 1.5 GPU-core-memory bundles worth of cores, but only one bundle for GPUs and memory. This job request will be counted as 1.5 core equivalent when the research group’s priority is calculated. | |||
* [[File:GPU_and_a_half_(memory).png|frame|Figure 7 - 1.5 GPU equivalents, based on memory.]] Research groups using more memory than GPUs or cores per GPU-core-memory bundle will be charged by memory. For example, a researcher requests 1 GPU, 6 cores, and 48GB of memory. The request is for 1.5 GPU-core-memory bundles worth of memory but only one bundle for GPUs and cores. This job request will be counted as 1.5 core equivalent when the research group’s priority is calculated. | |||
Compute Canada systems have the following GPU-core-memory bundle characteristics: | |||
* Cedar: 1 GPU / 6 cores / 32GB | |||
* Graham: 1 GPU / 8 cores / 32GB | |||
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