| Multitenant Database Containers (MDC) | Multiple Components One System (MCOS) |
| All the tenant DB sharing the same system HANA revision | Independent HANA revision of each HANA instance |
| No additional SAP HANA License | Separate SAP HANA License |
| No additional hypervisor virtualization license and hardware independent | No additional hypervisor virtualization license and hardware independent |
| No additional machine for hardware/vm management console | No additional machine for hardware/vm management console |
| No support on storage snap shot backup | Support on storage snap shot backup |
| Shared SAP HANA Binaries - Yes | Shared SAP HANA Binaries - No |
| 1 Linux license | 1 Linux license |
| Support > 4 socket hardware | Support > 4 socket hardware |
| Support > 1TB memory | Support > 1TB memory |
| Tenant DB only can restore to tenant DB | No dependency of tenant DB backup/restore |
| Multiple BW on HANA - Yes | Multiple BW on HANA - No |
| No Performance degrade | No Performance degrade |
| No additional maintenance required for vsphere patch, Lpar patching | No additional maintenance required for vsphere patch, Lpar patching |
| Hardware Resource Management – SAP HANA internal | Hardware Resource Management - SAP HANA internal |
Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU . It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
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