I started with a free list and a simple first-fit scan, which they were fine with as a baseline, but then they pushed on fragmentation pretty quickly.
Start by clarifying requirements and constraints, then design a data structure that supports efficient allocation and deallocation with coalescing. Implement a free list with boundary tags and use segregated free lists or a balanced tree to achieve better-than-linear performance. Discuss trade-offs between simplicity and performance, and outline testing strategies.
Pro tip: Mention that you would use boundary tags to enable O(1) coalescing and segregated free lists to reduce search time, showing awareness of real-world allocator designs like dlmalloc.
Ask about memory alignment, thread safety, allocation size limits, and performance goals. Confirm that the memory region is fixed and contiguous, and that we need to handle fragmentation.
Propose using a free list with boundary tags (size and status) at the start and end of each block. Consider segregated free lists (bins) for different size classes to speed up allocation.
For malloc, search appropriate free list for a block large enough, split if necessary, and mark as allocated. For free, mark block as free and coalesce with adjacent free blocks using boundary tags.
Use segregated free lists to achieve O(1) allocation for common sizes and O(log n) for larger sizes. Discuss using a balanced tree or bitmap for quick lookup.
Outline test cases: allocation patterns, fragmentation scenarios, coalescing correctness, and performance benchmarks. Mention using tools like Valgrind for memory errors.
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