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Maximum Subarray (Kadane's Algorithm) at NVIDIA — Interview Guide

How to approach Maximum Subarray (Kadane's Algorithm) in a NVIDIA interview: the optimal approach, what NVIDIA interviewers look for, and company-specific tips.

Updated 2026-09MediumDynamic Programming

The problem at NVIDIA

Given an integer array, find the contiguous subarray with the largest sum and return that sum.

NVIDIA uses this problem to assess systems, performance, and parallel thinking. Expect NVIDIA-specific follow-ups that tie the solution back to production reasoning and the company's engineering bar.

Key concepts

Kadane: at each index, the best subarray ending here is either the element alone or the element plus the best ending at the previous index.
Track the running sum, reset to 0 when it goes negative, and keep the global maximum.
Divide-and-conquer gives O(n log n) and generalizes to 2D problems.

Approach

def max_subarray(nums):
    best = cur = nums[0]
    for x in nums[1:]:
        cur = max(x, cur + x)
        best = max(best, cur)
    return best

What NVIDIA looks for

Low-level fluency — memory, caches, concurrency, SIMD.
Performance mindset — profile, optimize, and reason about bottlenecks.
Domain depth for the team (graphics, ML infra, autonomous systems).
Collaboration across hardware and software boundaries.

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