NCA NVIDIA Software Stack & Developer Tools

Question 1

What is the primary purpose of the cuDNN library in the NVIDIA software stack?

Accepted Answer

Providing optimized primitives for deep neural network operations

Answer

General-purpose dense matrix multiplication for all workloads

Answer

Managing GPU memory allocation and deallocation

Answer

Enabling multi-GPU communication via collectives

Question 2

Which NVIDIA library provides GPU-accelerated implementations of the Basic Linear Algebra Subprograms (BLAS) for dense matrices?

Accepted Answer

cuBLAS

Answer

cuSPARSE

Answer

cuDNN

Answer

NCCL

Question 3

What does NCCL stand for in the NVIDIA ecosystem?

Accepted Answer

NVIDIA Collective Communications Library

Answer

NVIDIA CUDA Compute Library

Answer

NVIDIA Core Clustering Layer

Answer

NVIDIA CUDA Communication Link

Question 4

What best describes the NVIDIA RAPIDS suite?

Accepted Answer

A GPU-accelerated open-source data science and analytics library suite

Answer

A high-speed GPU networking protocol for data centers

Answer

A rendering pipeline for real-time ray tracing applications

Answer

A firmware update tool for NVIDIA GPUs

Question 5

Which NVIDIA library is specifically designed to accelerate data loading and augmentation pipelines for deep learning training?

Accepted Answer

DALI (Data Loading Library)

Answer

cuBLAS

Answer

cuSPARSE

Answer

Triton Inference Server

Question 6

What is the primary function of the nvidia-smi command-line utility?

Accepted Answer

Monitoring and managing NVIDIA GPU devices and their status

Answer

Compiling CUDA kernels into GPU binaries

Answer

Launching distributed training across multiple nodes

Answer

Profiling CUDA kernel execution timing

Question 7

How does cuSPARSE differ from cuBLAS in the NVIDIA software stack?

Accepted Answer

cuSPARSE provides optimized routines for sparse matrix formats; cuBLAS handles dense matrices

Answer

cuSPARSE targets dense matrix operations while cuBLAS handles sparse ones

Answer

cuSPARSE is for CPU computations; cuBLAS is for GPU computations

Answer

cuSPARSE is deprecated and replaced entirely by cuBLAS