| Feature | Enterprise | Standard |
| Server core support1 | Yes | Yes |
| Log shipping | Yes | Yes |
| Database mirroring | Yes | Yes |
| Full safety only | ||
| Backup compression | Yes | Yes |
| Database snapshot | Yes | Yes 3 |
| Always On failover cluster instances | Yes | Yes |
| Number of nodes is the operating system maximum | Support for 2 nodes | |
| Always On availability groups | Yes | No |
| Up to 8 secondary replicas, including 2 synchronous secondary replicas | ||
| Basic availability groups 2 | No | Yes |
| Support for 2 nodes | ||
| Online page and file restore | Yes | No |
| Online indexing | Yes | No |
| Online schema change | Yes | No |
| Fast recovery | Yes | No |
| Mirrored backups | Yes | No |
| Hot add memory and CPU | Yes | No |
| Database recovery advisor | Yes | Yes |
| Encrypted backup | Yes | Yes |
| Hybrid backup to Windows Azure (backup to URL) | Yes | Yes |
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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