fumiama/base16384-sycl
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optimize: add xeinfo class & more compl. kernel

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源文雨
2025-09-29 17:01:27 +08:00
parent 78c6dea4c6
commit cbe9cda397
5 changed files with 256 additions and 67 deletions
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135
tests/basic.cpp
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@@ -1,15 +1,23 @@
#include <chrono>
#include <iostream>
#include <sycl/sycl.hpp>
#include <vector>
#include <stdint.h>
#ifdef _WIN32
#include <windows.h>
#undef min
#undef max
#endif
#include "errors.hpp"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <random>
#include <ranges>
#include <sycl/sycl.hpp>
#include <vector>
static const int N = 65536;
static const int work_group_size = 64;
#include "errors.hpp"
#include "xeinfo.hpp"
constexpr int iter_count = 65536;
constexpr int N = 65536;
int main() {
#ifdef _WIN32
@@ -19,8 +27,9 @@ int main() {
#endif
sycl::queue q;
auto device = q.get_device();
std::cout << "执行设备: " << device.get_info<sycl::info::device::name>() << std::endl;
const sycl::device device;
const auto device_name = device.get_info<sycl::info::device::name>();
std::cout << "执行设备: " << device_name << std::endl;
std::cout << "设备类型: ";
if (device.is_cpu()) {
std::cout << "CPU" << std::endl;
@@ -30,47 +39,105 @@ int main() {
std::cout << "其他" << std::endl;
}
int work_group_size = 64;
if (device.is_gpu() && device_name.starts_with("Intel")) {
try {
auto xeinfo = base16384::xeinfo(device);
work_group_size = xeinfo.work_group_size;
std::cout << "\n" << xeinfo.string() << "\n\n";
} catch (const sycl::exception& e) {
std::cout << "获取Intel GPU信息失败 (可能不是Intel设备): " << e.what() << std::endl;
std::cout << "使用默认工作组大小: " << work_group_size << "\n\n";
}
}
// Generate random initial data
std::random_device rd;
std::mt19937 gen{rd()};
std::uniform_int_distribution<int> dis{0, 255};
std::vector<uint8_t> initial_data(N);
for (auto& byte : initial_data) {
byte = static_cast<uint8_t>(dis(gen));
}
// CPU baseline test
std::vector<int> cpu_data(N);
for (int i = 0; i < N; i++) cpu_data[i] = i;
auto cpu_data = initial_data;
auto start_time = std::chrono::high_resolution_clock::now();
for (int i = 0; i < N; i++) cpu_data[i] *= 2;
for (int j = 0; j < iter_count; j++) {
for (auto& byte : cpu_data) {
// 复杂计算：多步数学运算组合
uint8_t temp = byte;
temp = (temp * temp) % 251; // 使用质数避免快速收敛
temp = temp ^ (temp >> 2); // 位运算
temp = (temp + 17) % 256; // 加法和模运算
temp = temp * 3 % 256; // 乘法
byte = temp ^ (temp << 1); // 最终位运算
}
}
auto end_time = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);
std::cout << "CPU (" << duration.count() << " us):" << std::endl;
for (int i = 0; i < min(N, 64); i++) std::cout << " " << cpu_data[i];
std::cout << "CPU (" << std::fixed << std::setprecision(1) << duration.count() / 1000.0
<< " ms):";
for (int i = 0; i < std::min(N, 64); i++) std::cout << " " << static_cast<int>(cpu_data[i]);
std::cout << "..." << std::endl;
int *data = sycl::malloc_shared<int>(N, q);
for (int i = 0; i < N; i++) data[i] = i;
auto* data = sycl::malloc_shared<std::uint8_t>(N, q);
std::copy(initial_data.cbegin(), initial_data.cend(), data);
// test basic parallel kernel
start_time = std::chrono::high_resolution_clock::now();
auto errn = base16384_try_failed(
[&]() { q.parallel_for(sycl::range<1>(N), [=](sycl::id<1> i) { data[i] *= 2; }).wait(); });
end_time = std::chrono::high_resolution_clock::now();
duration = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);
if (errn) return errn;
std::cout << "GPU基本并行 (" << duration.count() << " us):" << std::endl;
for (int i = 0; i < min(N, 64); i++) std::cout << " " << data[i];
std::cout << "..." << std::endl;
start_time = std::chrono::high_resolution_clock::now();
errn = base16384_try_failed([&]() {
q.parallel_for(sycl::nd_range<1>(N, work_group_size), [=](sycl::nd_item<1> item) {
int i = item.get_global_id(0);
data[i] /= 2;
}).wait();
auto errn = base16384::errors::try_failed([&]() {
for (int j = 0; j < iter_count; j++) {
q.parallel_for(sycl::range<1>(N), [=](sycl::id<1> i) {
// 复杂计算：多步数学运算组合
uint8_t temp = data[i];
temp = (temp * temp) % 251; // 使用质数避免快速收敛
temp = temp ^ (temp >> 2); // 位运算
temp = (temp + 17) % 256; // 加法和模运算
temp = temp * 3 % 256; // 乘法
data[i] = temp ^ (temp << 1); // 最终位运算
});
}
q.wait();
});
end_time = std::chrono::high_resolution_clock::now();
duration = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);
if (errn) return errn;
std::cout << "GPU高级并行 (" << duration.count() << " us):" << std::endl;
for (int i = 0; i < min(N, 64); i++) std::cout << " " << data[i];
std::cout << "GPU 基本并行 (" << std::fixed << std::setprecision(1) << duration.count() / 1000.0
<< " ms):";
for (int i = 0; i < std::min(N, 64); i++) std::cout << " " << static_cast<int>(data[i]);
std::cout << "..." << std::endl;
std::copy(initial_data.cbegin(), initial_data.cend(), data);
start_time = std::chrono::high_resolution_clock::now();
errn = base16384::errors::try_failed([&]() {
for (int j = 0; j < iter_count; j++) {
q.parallel_for(sycl::nd_range<1>(N, work_group_size),
[=](sycl::nd_item<1> item) { // sub-group size
const auto i = item.get_global_id(0);
// 复杂计算：多步数学运算组合
uint8_t temp = data[i];
temp = (temp * temp) % 251; // 使用质数避免快速收敛
temp = temp ^ (temp >> 2); // 位运算
temp = (temp + 17) % 256; // 加法和模运算
temp = temp * 3 % 256; // 乘法
data[i] = temp ^ (temp << 1); // 最终位运算
});
}
q.wait();
});
end_time = std::chrono::high_resolution_clock::now();
duration = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);
if (errn) return errn;
std::cout << "GPU 高级并行 (" << std::fixed << std::setprecision(1) << duration.count() / 1000.0
<< " ms):";
for (int i = 0; i < std::min(N, 64); i++) std::cout << " " << static_cast<int>(data[i]);
std::cout << "..." << std::endl;
sycl::free(data, q);