Chapter4-IntermediateAlgo

Chapter 4: Intermediate Algorithms - CUDA Merge Sort

Overview

This chapter explores parallelizable algorithms, focusing on sorting techniques. We have chosen to implement merge sort using CUDA as an example of an intermediate-level algorithm that can benefit from parallel processing.

Merge Sort in CUDA

Merge sort is an efficient, stable sorting algorithm that follows the divide-and-conquer paradigm. It's well-suited for parallel implementation due to its recursive nature and the independence of its sub-problems.

Key Components

1. merge function (device):

   • Merges two sorted subarrays into a single sorted array.
   • Uses temporary storage for merging to avoid in-place operations.

2. mergeSortRecursive function (device):

   • Implements the recursive part of the merge sort algorithm.
   • Divides the array, recursively sorts subarrays, and merges results.

3. mergeSort kernel (global):

   • Entry point for the GPU execution.
   • Calls the recursive merge sort function.

4. main function:

   • Sets up the problem on the host.
   • Allocates memory on the device.
   • Launches the kernel and retrieves results.

Implementation Details

• The code uses CUDA-specific keywords like __device__ and __global__ to define functions that run on the GPU.
• Memory is allocated on both host and device to facilitate data transfer.
• The sorting is performed entirely on the GPU, with only the final sorted array transferred back to the host.

Optimization Techniques

While this implementation demonstrates the basic structure of a CUDA merge sort, several optimization techniques could be applied:

1. Shared Memory: Utilize shared memory for faster access to frequently used data within a thread block.
2. Coalesced Memory Access: Optimize global memory access patterns for better performance.
3. Dynamic Parallelism: Use CUDA's dynamic parallelism to launch child kernels from within the device code, potentially improving the parallelization of the recursive steps.
4. Hybrid Approach: Combine GPU parallelism for large-scale divisions with CPU processing for smaller subarrays.

Execution

Conclusion

This CUDA implementation of merge sort demonstrates how a classic sorting algorithm can be adapted for parallel execution on a GPU. It serves as a foundation for understanding more complex parallel algorithms and optimization techniques in CUDA programming.