| OBJECT STORAGE | FILE STORAGE | |
| PERFORMANCE | Performs best for big content and high stream throughput | Performs best for smaller files |
| GEOGRAPHY | Data can be stored across multiple regions | Data typically needs to be shared locally |
| SCALABILITY | Scales infinitely to petabytes and beyond | Potentially scales up to millions of files, but can’t handle more |
| ANALYTICS | Customizable metadata, not limited to number of tags | Limited number of set metadata tags |
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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