NCA Tensor Core & Mixed Precision 2

Question 1

Which NVIDIA GPU architecture first introduced Tensor Cores?

Accepted Answer

Volta

Answer

Tensor Cores were introduced in the Volta architecture (V100) in 2017.

Question 2

In the context of Tensor Cores, what does the term 'mixed precision' mean?

Accepted Answer

Performing multiply-accumulate in lower precision with accumulation in higher precision

Answer

Mixed precision uses FP16 (or BF16) for multiply operations and FP32 for accumulation to balance speed and numerical stability.

Question 3

What native matrix multiplication shape does a single Volta Tensor Core operation compute?

Accepted Answer

4×4×4

Answer

Each Volta Tensor Core performs a 4×4×4 matrix multiply-accumulate per clock cycle.

Question 4

Which PyTorch function enables automatic mixed precision (AMP) training with Tensor Cores?

Accepted Answer

torch.cuda.amp.autocast()

Answer

torch.cuda.amp.autocast() automatically casts operations to FP16 where safe, enabling Tensor Core acceleration.

Question 5

Why must tensor dimensions often be multiples of 8 (or 16) to fully utilize Tensor Cores?

Accepted Answer

Tensor Cores operate on fixed tile sizes and padding wastes cycles

Answer

Tensor Cores process fixed-size matrix tiles, so non-aligned dimensions cause padding that wastes compute cycles.

(NCA) NVIDIA-Certified Associate Practice Test