OpenAI VO Interview Question #1: Memory Manager malloc/free Implementation - Professional Interview Assistance Guide

Background Analysis & OAVOService Expert Insights

OpenAI, as the hottest AI company today, has extremely challenging interviews, especially in VO (Virtual Onsite) rounds where low-level system design questions frequently appear. This question tests candidates' deep understanding of memory management and is a classic problem at tech giants like OpenAI, Google, and Meta.

OAVOService Professional Tip: This seemingly simple question actually has numerous examination points, from data structure selection to boundary handling - every detail can determine interview success or failure. Our professional interview assistance team has helped hundreds of students successfully pass OpenAI VO interviews.

Complete Problem Statement

Given N bytes of available memory, implement the following two core functions:

• malloc(k) — Allocate k contiguous bytes of memory, return pointer to the beginning of the block
• free(ptr) — Free the memory block previously allocated at pointer ptr

Core Constraints

1. Contiguous Allocation: Each allocated block must be contiguous memory space
2. Fixed Position: Once allocated, blocks cannot be moved (no memory compaction)
3. Proper Deallocation: After freeing memory, that segment becomes available again, but other allocations remain unchanged

Practical Example

# Initialize 1000-byte memory manager
m = MemoryManager(1000)

# Allocation examples
a = m.malloc(100)   # Returns 0, uses positions 0-99
b = m.malloc(500)   # Returns 100, uses positions 100-599  
c = m.malloc(950)   # Error: insufficient space

# Deallocation examples
m.free(b)          # Frees positions 100-599
m.free(a)          # Frees positions 0-99
m.free(a)          # Error: double free

# Reallocation
c = m.malloc(950)   # Success, uses positions 0-949

OAVOService Interview Assistance Solutions

Solution 1: Linked List Free Block Management

class MemoryManager:
    def __init__(self, size):
        # Free block linked list: [(start, size), ...]
        self.free_blocks = [(0, size)]
        # Allocated block mapping: {ptr: size}
        self.allocated = {}
    
    def malloc(self, k):
        # Find suitable free block - First Fit strategy
        for i, (start, size) in enumerate(self.free_blocks):
            if size >= k:
                # Found suitable block, allocate
                if size == k:
                    # Use entire block
                    self.free_blocks.pop(i)
                else:
                    # Use partial block, update remaining
                    self.free_blocks[i] = (start + k, size - k)
                
                self.allocated[start] = k
                return start
        
        # No suitable block found
        raise MemoryError("Not enough contiguous memory")
    
    def free(self, ptr):
        if ptr not in self.allocated:
            raise ValueError("Double free or invalid pointer")
        
        size = self.allocated.pop(ptr)
        
        # Add freed block back to free list and try to merge
        self._add_free_block(ptr, size)
    
    def _add_free_block(self, start, size):
        # Insert new free block and merge adjacent blocks
        new_block = (start, size)
        
        # Find insertion position and check merge opportunities
        inserted = False
        for i, (block_start, block_size) in enumerate(self.free_blocks):
            if start < block_start:
                self.free_blocks.insert(i, new_block)
                inserted = True
                break
        
        if not inserted:
            self.free_blocks.append(new_block)
        
        # Merge adjacent free blocks
        self._merge_adjacent_blocks()
    
    def _merge_adjacent_blocks(self):
        if len(self.free_blocks) <= 1:
            return
        
        self.free_blocks.sort()  # Sort by start position
        merged = [self.free_blocks[0]]
        
        for start, size in self.free_blocks[1:]:
            last_start, last_size = merged[-1]
            
            # Check if adjacent
            if last_start + last_size == start:
                # Merge blocks
                merged[-1] = (last_start, last_size + size)
            else:
                merged.append((start, size))
        
        self.free_blocks = merged

Solution 2: Optimized Bitmap Version

class OptimizedMemoryManager:
    def __init__(self, size):
        self.size = size
        # Use bitmap to mark free/occupied status
        self.bitmap = [False] * size  # False = free, True = occupied
        self.allocated_blocks = {}    # {ptr: size}
    
    def malloc(self, k):
        # Find k consecutive free positions
        start = self._find_free_block(k)
        if start == -1:
            raise MemoryError("Not enough contiguous memory")
        
        # Mark as occupied
        for i in range(start, start + k):
            self.bitmap[i] = True
        
        self.allocated_blocks[start] = k
        return start
    
    def _find_free_block(self, k):
        consecutive_free = 0
        start_pos = 0
        
        for i in range(self.size):
            if not self.bitmap[i]:  # Free position
                if consecutive_free == 0:
                    start_pos = i
                consecutive_free += 1
                
                if consecutive_free >= k:
                    return start_pos
            else:  # Occupied position
                consecutive_free = 0
        
        return -1  # No suitable block found
    
    def free(self, ptr):
        if ptr not in self.allocated_blocks:
            raise ValueError("Double free or invalid pointer")
        
        size = self.allocated_blocks.pop(ptr)
        
        # Clear bitmap markings
        for i in range(ptr, ptr + size):
            self.bitmap[i] = False

Common Interviewer Follow-ups & Response Strategies

Q1: How to optimize allocation strategies?

OAVOService Standard Answer:

• First Fit: Find first suitable block (simple implementation, fast)
• Best Fit: Find smallest suitable block (reduces external fragmentation)
• Worst Fit: Find largest suitable block (reserves space for large allocations)

Q2: How to handle memory fragmentation?

Professional Response:

1. Merge Strategy: Actively merge adjacent free blocks during deallocation
2. Allocation Strategy: Choose appropriate First/Best/Worst Fit
3. Memory Compaction: Advanced versions can support moving allocated blocks

Q3: How to optimize time complexity?

Technical Points:

• Linked list approach: malloc O(n), free O(n)
• Red-black tree optimization: malloc O(log n), free O(log n)
• Bitmap approach: suitable for small memory, space-efficient

OAVOService Exclusive Interview Techniques

1. Clear Thinking: Explain overall architecture first, then dive into implementation details
2. Consider Edge Cases: Empty memory, double free, invalid pointers, etc.
3. Code Quality: Clear variable naming, reasonable logical structure
4. Proactive Optimization: Propose improvements without waiting for interviewer prompts

Related Extension Problems

• Memory Pool Management: Pre-allocate fixed-size blocks
• Garbage Collection: Automatic memory management mechanisms
• Virtual Memory: Page table mapping and replacement algorithms

Summary

Memory manager implementation is a classic problem testing system programming fundamentals, frequently appearing at companies like OpenAI and Google. The key lies in choosing appropriate data structures, properly handling edge cases, and clearly expressing design thinking during interviews.

OAVOService Professional Interview Assistance Services:

• ✅ Real-time voice guidance, no getting stuck at critical moments
• ✅ Code implementation assistance, ensuring logical correctness
• ✅ Follow-up response strategies, demonstrating technical depth
• ✅ Full interview pace control, optimizing performance

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