SQL> SELECT * FROM (
2 SELECT sequence#, archived, applied,
3 TO_CHAR(completion_time, 'RRRR/MM/DD HH24:MI') AS completed
4 FROM sys.v$archived_log
5 ORDER BY sequence# DESC)
6 WHERE ROWNUM <= 10
7 /
SEQUENCE# ARCHIVED APPLIED COMPLETED
---------- -------- ------- ----------------
11211 YES NO 2004/09/16 09:30
11210 YES YES 2004/09/16 09:00
11209 YES YES 2004/09/16 08:30
11208 YES YES 2004/09/16 08:00
11207 YES YES 2004/09/16 07:30
11206 YES YES 2004/09/16 07:00
11205 YES YES 2004/09/16 06:30
11204 YES YES 2004/09/16 06:30
11203 YES YES 2004/09/16 06:30
11202 YES YES 2004/09/16 06:00
10 rows selected.
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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