TensorFlow 2.0 stand by GPU The acceleration is mainly manifested in the parallel operation of the addition, subtraction, multiplication and division of the matrix accelerate.

Question

Options

• ATRUE
• BFALSE

• A
  78% (39)
• B
  22% (11)

Explanation

Option A (TRUE) is correct because GPU acceleration in TensorFlow 2.0 is fundamentally built around parallelizing matrix operations - addition, subtraction, multiplication, and division - which are the core computations in neural networks. GPUs contain thousands of smaller cores designed to perform many floating-point arithmetic operations simultaneously, making them ideal for the element-wise and dot-product operations that dominate deep learning workloads.

Option B is incorrect because denying this would contradict how GPU acceleration actually works in TensorFlow; matrix math parallelism is not a minor benefit but the primary mechanism by which GPUs provide speedups over CPUs for ML tasks.

Memory tip: Think "GPU = Grid of Parallel Units" - just as a grid applies operations to every cell at once, a GPU applies arithmetic to every matrix element simultaneously. If you remember that neural networks are just chains of matrix math, it's clear why GPU acceleration shows up exactly there.

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