Why is Allocating a Single 2D Array Slower than Allocating Multiple 1D Arrays in Java?

What causes a single 2D array allocation in Java to take longer than allocating multiple 1D arrays with the same total size?

@Benchmark
public static int[][] newArray() {
    return new int[num][length];
}

@Benchmark
public static int[][] newArray2() {
    int[][] temps = new int[num][];
    for (int i = 0; i < temps.length; i++) {
        temps[i] = new int[length];
    }
    return temps;
}

When allocating a multi-dimensional array versus multiple one-dimensional arrays in Java, the differences in performance can appear non-intuitive. The key factors influencing this performance include memory allocation, references, and the nature of array data structures in Java.

@Benchmark
public static int[][] newArray() {
    return new int[num][length]; // allocates a contiguous block
}

@Benchmark
public static int[][] newArray2() {
    int[][] temps = new int[num][]; // creates array of references
    for (int i = 0; i < temps.length; i++) {
        temps[i] = new int[length]; // allocates each 1D array separately
    }
    return temps;
}

Causes

• **Memory Allocation Strategy**: A 2D array is a single contiguous block of memory, while multiple 1D arrays are scattered across the heap, resulting in different allocation behaviors.
• **Initialization Overhead**: Although a 2D array initializes all entries at once, it might take more time due to needing to set up more complex memory structures compared to sequentially establishing references in a loop.
• **Garbage Collection**: The garbage collector might perform differently, impacting the time as the allocated structures become more complex. Allocating numerous smaller objects can lead to different GC runtimes than a single larger allocation.

Solutions

• **Benchmark Adjustments**: If you're comparing these approaches, consider running your benchmarks multiple times under varying conditions to average out the results.
• **Profiling**: Use profiling tools to analyze memory usage and performance bottlenecks. Tools like VisualVM can help discern where time is spent during allocation.
• **Consider Other Data Structures**: If the specific allocation model is a significant performance concern, explore using collections (e.g., ArrayList) or custom data structures to manage memory more effectively.

Mistake: Underestimating the cost of memory allocation.

Solution: Run profiling tools to measure and understand where time is being spent. Adjust for more accurate microbenchmarking.

Mistake: Not considering the impact of garbage collection on allocation time.

Solution: Test allocations under different conditions and consider JVM options that can impact GC behavior.