Accelerating Bucket Sort with Intel oneAPI

Bucket sort, also known as bin sort, is a comparison-based sorting algorithm that distributes elements into a fixed number of buckets. Each bucket is then sorted individually, either using a different sorting algorithm or recursively applying the bucket sort algorithm. Bucket sort is useful for sorting data that are uniformly distributed across a range, and it can be easily parallelized for better performance.

In this article, we will explore how to utilize Intel's oneAPI toolkit to accelerate the bucket sort algorithm using Data Parallel C++ (DPC++) and Python. We will demonstrate how to implement the algorithm using oneAPI's features for parallelism and vectorization, and provide a step-by-step guide to integrating it into your project.

Overview of Intel oneAPI

Intel oneAPI is a versatile toolkit that provides a comprehensive set of software development tools for cross-architecture programming. It includes compilers, libraries, and analysis tools designed to work seamlessly with CPUs, GPUs, and other accelerators. The main goal of oneAPI is to simplify the development process and enable code reuse across multiple hardware platforms.

Data Parallel C++ (DPC++) is an open-source, high-level programming language that extends C++ with features designed to work with the oneAPI toolkits. It allows developers to write parallel and heterogeneous code that can run on a variety of architectures. DPC++ provides a simple programming model with a familiar syntax, which makes it easy for developers to transition from other languages like CUDA or OpenCL.

Implementing Bucket Sort with oneAPI

To implement bucket sort using Intel oneAPI, we will first develop a DPC++ kernel that performs the sorting on the GPU. This kernel will receive the input data and the number of buckets as parameters, and it will return the sorted data.

Here is a brief outline of the steps involved in the implementation:

Initialize the oneAPI environment and device selector.
Create a DPC++ queue for executing kernels on the selected device.
Allocate memory for input data, buckets, and output data using oneAPI Unified Shared Memory (USM).
Implement a DPC++ kernel that performs the bucket sort.
Compile and run the kernel on the selected device.
Retrieve the sorted data and clean up resources.

Initializing the oneAPI Environment

First, we will import the necessary libraries and initialize the oneAPI environment. The following code imports the dpctl library for device management and sets up a device selector for choosing the appropriate device:

import dpctl
import numpy as np

device_selector = "gpu"
device = dpctl.select_device(device_selector)
print("Selected device: ", device)

Creating a DPC++ Queue

Next, we will create a DPC++ queue for executing kernels on the selected device:

queue = dpctl.create_queue(device)

Allocating Memory

Now, we need to allocate memory for the input data, buckets, and output data. We will use oneAPI Unified Shared Memory (USM) for this purpose:

input_data = np.array([...], dtype=np.float32)
num_buckets = 10

buckets = np.empty((num_buckets, len(input_data)), dtype=np.float32)
bucket_sizes = np.zeros(num_buckets, dtype=np.uint32)

output_data = np.empty_like(input_data)

Implementing the DPC++ Kernel

The DPC++ kernel for bucket sort will perform the following tasks:

Distribute the input data into buckets.
Sort each bucket independently.
Concatenate the sorted buckets to produce the sorted output data.

Here is a sample DPC++ kernel that accomplishes these tasks:

#include <CL/sycl.hpp>
#include <dpct/dpct.hpp>

void bucket_sort(const float* input_data, float* output_data, int num_elements, float* buckets, uint32_t* bucket_sizes, int num_buckets, sycl::nd_item<3> item_ct1) {
    // ...
}

Compiling and Running the Kernel

We will use Numba's DPPY JIT compiler to compile and run the DPC++ kernel from Python:

from numba import dppy

@dppy.kernel
def bucket_sort(input_data, output_data, num_elements, buckets, bucket_sizes, num_buckets):
    # ...

bucket_sort[global_size, local_size](input_data, output_data, len(input_data), buckets, bucket_sizes, num_buckets)

Retrieving the Sorted Data and Cleaning Up

Finally, we will retrieve the sorted data from the output buffer and clean up any allocated resources:

print("Sorted data: ", output_data)

Conclusion

In this article, we demonstrated how to use Intel's oneAPI toolkit to accelerate the bucket sort algorithm using DPC++ and Python. We provided a step-by-step guide to implementing the algorithm and integrating it into your project. By leveraging oneAPI's powerful features for parallelism and vectorization, you can significantly improve the performance of bucket sort and other algorithms in your applications.

To get started with Intel oneAPI, download the toolkit from the official website and follow the installation guide for your platform. Additionally, consult the oneAPI programming guide and DPC++ programming guide for an in-depth understanding of oneAPI's features and capabilities.

By adopting Intel oneAPI for your projects, you can harness the full potential of modern hardware platforms and develop high-performance, cross-architecture applications with ease.

MomoyamaSawa / AI_oneAPI