- We must not consider Auto Scaling for Database Tier. It may not be applicable. It needs distributed architecture ; might be suitable for certain NoSQL DBs. Needs a test case prior to take decision for Production.
- For very small EC2 instance we must not consider Auto Scaling. Rather a bit lager EC2 instance can fix the issue.
- For very frequent changes, EC2 auto scaling may not be right choice. e.g: Cricket match update. Need to plan for CDN.
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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