| drs_start | Enables Oracle to determine whether or not the DRMON (Disaster Recovery Monitor) process should be started. | ||
| gc_files_to_locks | A RAC parameter that controls the mapping of pre-release 9.0.1 parallel cache management (PCM) locks to datafiles. | ||
| max_commit_propagation_delay | Used when data consistency between different RAC instances must be guaranteed and immediate i.e. if commits must be seen instantaneously on remote instances. | ||
| plsql_native_library_dir | Specifies the name of a directory where the shared objects produced by the native compiler are stored. | ||
| plsql_native_library_subdir_count | Specifies the number of subdirectories created by the database administrator in the directory specified by plsql_native_library_dir. | ||
| sql_version | SQL language version parameter for compatibility issues. | ||
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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