oneAPI简介

Intel 的oneAPI，目的是简化跨CPU、GPU、FPGA、人工智能和其它加速器的各种计算引擎的编程开发。开发人员想达到的目的是一个解决方案，多种架构。2020年5月6月期间发布了Base Kit (Beta)版本。

官网介绍

安装

下载的文件是.exe结尾的，有图形化安装界面，Next下去即可。最终解压安装完成的效果如图：

目录

这里也想说一点，Intel新推出的11代酷睿处理器的核显已经可以赶上入门独显了。而且Intel自家的FPGA产品也是可以支持OpenCL异构计算的。所以Intel迫切的想要退出一种新的解决方案，让开发者不需要过多的了解底层硬件编写语言：比如OpenCL C，Verilog HDL，同样也可以写出高性能代码。

同样新的语言营运而生，DPC++（Data Parallel C++），英特尔在设计DPC++的时候，在语法上和CUDA非常接近，如果程序员对于CUDA非常熟悉的话，那么使用DPC++进行编程应该没有任何问题。本质上还是有C/C++语言基础，看懂代码应该没太大难度。

测试

Intel 给了oneAPI的编程指导，只不过现在还没中文版：

GUIDE

根据编程指导里的介绍，Intel把所有的sample code放到了github上：

oneAPI-github-Sample code

Github

这里使用的是DPC++ compiler下的vector add，放上代码：

dpc_common.hpp

//==============================================================
// Copyright © 2020 Intel Corporation
//
// SPDX-License-Identifier: MIT
// =============================================================

#ifndef _DP_HPP
#define _DP_HPP

#pragma once

#include <stdlib.h>
#include <exception>

#include <CL/sycl.hpp>

namespace dpc {
// this exception handler with catch async exceptions
static auto exception_handler = [](cl::sycl::exception_list eList) {
  for (std::exception_ptr const &e : eList) {
    try {
      std::rethrow_exception(e);
    } catch (std::exception const &e) {
#if _DEBUG
      std::cout << "Failure" << std::endl;
#endif
      std::terminate();
    }
  }
};

class queue : public cl::sycl::queue {
  // Enable profiling by default
  cl::sycl::property_list prop_list =
      cl::sycl::property_list{cl::sycl::property::queue::enable_profiling()};

 public:
  queue()
      : cl::sycl::queue(cl::sycl::default_selector{}, exception_handler, prop_list) {}
  queue(cl::sycl::device_selector &d)
      : cl::sycl::queue(d, exception_handler, prop_list) {}
  queue(cl::sycl::device_selector &d, cl::sycl::property_list &p)
      : cl::sycl::queue(d, exception_handler, p) {}
};

using Duration = std::chrono::duration<double>;

class Timer {
 public:
  Timer() : start(std::chrono::steady_clock::now()) {}

  Duration elapsed() {
    auto now = std::chrono::steady_clock::now();
    return std::chrono::duration_cast<Duration>(now - start);
  }

 private:
  std::chrono::steady_clock::time_point start;
};

};  // namespace dpc

#endif

vector-add-buffers.cpp

//==============================================================
// Vector Add is the equivalent of a Hello, World! sample for data parallel
// programs. Building and running the sample verifies that your development
// environment is setup correctly and demonstrates the use of the core features
// of DPC++. This sample runs on both CPU and GPU (or FPGA). When run, it
// computes on both the CPU and offload device, then compares results. If the
// code executes on both CPU and offload device, the device name and a success
// message are displayed. And, your development environment is setup correctly!
//
// For comprehensive instructions regarding DPC++ Programming, go to
// https://software.intel.com/en-us/oneapi-programming-guide and search based on
// relevant terms noted in the comments.
//
// DPC++ material used in the code sample:
// •    A one dimensional array of data.
// •    A device queue, buffer, accessor, and kernel.
//==============================================================
// Copyright © 2020 Intel Corporation
//
// SPDX-License-Identifier: MIT
// =============================================================
#include <CL/sycl.hpp>
#include <array>
#include <iostream>
#include "dpc_common.hpp"
#if FPGA || FPGA_EMULATOR
#include <CL/sycl/intel/fpga_extensions.hpp>
#endif

using namespace sycl;

// Array type and data size for this example.
constexpr size_t array_size = 10000;
typedef std::array<int, array_size> IntArray;

//************************************
// Vector add in DPC++ on device: returns sum in 4th parameter "sum_parallel".
//************************************
void VectorAdd(queue &q, const IntArray &a_array, const IntArray &b_array,
               IntArray &sum_parallel) {
  // Create the range object for the arrays managed by the buffer.
  range<1> num_items{a_array.size()};

  // Create buffers that hold the data shared between the host and the devices.
  // The buffer destructor is responsible to copy the data back to host when it
  // goes out of scope.
  buffer a_buf(a_array);
  buffer b_buf(b_array);
  buffer sum_buf(sum_parallel.data(), num_items);

  // Submit a command group to the queue by a lambda function that contains the
  // data access permission and device computation (kernel).
  q.submit([&](handler &h) {
    // Create an accessor for each buffer with access permission: read, write or
    // read/write. The accessor is a mean to access the memory in the buffer.
    auto a = a_buf.get_access<access::mode::read>(h);
    auto b = b_buf.get_access<access::mode::read>(h);

    // The sum_accessor is used to store (with write permission) the sum data.
    auto sum = sum_buf.get_access<access::mode::write>(h);

    // Use parallel_for to run vector addition in parallel on device. This
    // executes the kernel.
    //    1st parameter is the number of work items.
    //    2nd parameter is the kernel, a lambda that specifies what to do per
    //    work item. The parameter of the lambda is the work item id.
    // DPC++ supports unnamed lambda kernel by default.
    h.parallel_for(num_items, [=](id<1> i) { sum[i] = a[i] + b[i]; });
  });
}

//************************************
// Initialize the array from 0 to array_size - 1
//************************************
void InitializeArray(IntArray &a) {
  for (size_t i = 0; i < a.size(); i++) a[i] = i;
}

//************************************
// Demonstrate vector add both in sequential on CPU and in parallel on device.
//************************************
int main() {
  // Create device selector for the device of your interest.
#if FPGA_EMULATOR
  // DPC++ extension: FPGA emulator selector on systems without FPGA card.
  intel::fpga_emulator_selector d_selector;
#elif FPGA
  // DPC++ extension: FPGA selector on systems with FPGA card.
  intel::fpga_selector d_selector;
#else
  // The default device selector will select the most performant device.
  default_selector d_selector;
#endif

  // Create array objects with "array_size" to store the input and output data.
  IntArray a, b, sum_sequential, sum_parallel;

  // Initialize input arrays with values from 0 to array_size - 1
  InitializeArray(a);
  InitializeArray(b);

  try {
    queue q(d_selector, dpc::exception_handler);

    // Print out the device information used for the kernel code.
    std::cout << "Running on device: "
              << q.get_device().get_info<info::device::name>() << "\n";
    std::cout << "Vector size: " << a.size() << "\n";

    // Vector addition in DPC++
    VectorAdd(q, a, b, sum_parallel);
  } catch (exception const &e) {
    std::cout << "An exception is caught for vector add.\n";
    std::terminate();
  }

  // Compute the sum of two arrays in sequential for validation.
  for (size_t i = 0; i < sum_sequential.size(); i++)
    sum_sequential[i] = a[i] + b[i];

  // Verify that the two arrays are equal.
  for (size_t i = 0; i < sum_sequential.size(); i++) {
    if (sum_parallel[i] != sum_sequential[i]) {
      std::cout << "Vector add failed on device.\n";
      return -1;
    }
  }

  int indices[]{0, 1, 2, (a.size() - 1)};
  constexpr size_t indices_size = sizeof(indices) / sizeof(int);

  // Print out the result of vector add.
  for (int i = 0; i < indices_size; i++) {
    int j = indices[i];
    if (i == indices_size - 1) std::cout << "...\n";
    std::cout << "[" << j << "]: " << a[j] << " + " << b[j] << " = "
              << sum_parallel[j] << "\n";
  }

  std::cout << "Vector add successfully completed on device.\n";
  return 0;
}

代码有了，就可以编译看看效果；在这里Intel提供的是非常完整的工具包，所以编译器调试器一应俱全。但是有一点就是没有环境变量，是无法使用这些工具的。Intel提供了环境变量终端，安装完成oneAPI后会提供一个终端：

cmd.png

如上图所示，打开这个终端，它会自动加载环境变量。

加载环境变量成功

编译

参考编译命令：

dpcpp -O2 -g -std=C++17 -o vector-add-buffers.exe src/vector-add-buffers.cpp

没有报错即编译成功，编译成功后，产生的文件如下图：

文件结构

最后，输入.\vector-add-buffers.exe来运行查看结果：

结果

可以看到用的GPU，两个拥有10000个元素的一维向量相加很快便执行出来了。有多快？就真的和你打印Hello World差不多快，所以源文件开始的说明里面也说了，两个元素个数相同的一维向量的相加，便是并行处理的Hello World。

注意：这个编译过程切不可放进普通终端（cmd/powershell）中进行，因为环境变量的缘故。
针对单个源文件，直接采用命令编译毫无问题，但是若是有很多个cpp和终端设备的工程文件，最好考虑Visual Studio这样的IDE，或者是CMake生成Makefile（比较推荐，跨平台工程常用）。在Github的sample code里面，Intel也给了vs的工程文件，可以直接clone整个工程，打开就可以用。