The database architecture called shared-disk, which eliminates the need to partition data, is ideal for cloud databases. Shared-disk databases allow clusters of low-cost servers to use a single collection of data, typically served up by a Storage Area Network (SAN) or Network Attached Storage (NAS). All of the data is available to all of the servers, there is no partitioning of the data. As a result, if you are using two servers, and your query takes .5 seconds, you can dynamically add another server and the same query might now take .35 seconds. In other words, shared-disk databases support elastic scalability.
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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