潜入C快的不止一百倍，还可以更快，我在X86框架上在pygame框架试验了其中的核心四叉树算法。
#//警告：算法运行时，CPU晶体管将神圣对齐
#// 仅适用于Intel Core i7-6700K及以上↓
#//配Z170主板和DDR4-3200内存↑↑↑↑
#// 运行前请执行`./sacrifice.sh`向硅基神灵献祭
#非x86架构者请速速回避！
#适用于模拟物理效果
#技术实现:↓
#Cython+SoA+SIMD+AVX2晶体管
from libc.stdlib cimport malloc, free, posix_memalign # 替换为对齐分配
from libcpp.vector cimport vector
from cython cimport boundscheck, wraparound
from cython.parallel import prange, num_threads, threadid
from libc.string cimport memset
from cython.operator cimport sizeof # 内存计算
from cython cimport compile_time_static # 编译期断言
# 神圣点结构体（仅用于返回，插入直接用标量）
cdef class PointC:
cdef public:
double x
double y
def __cinit__(self, double x, double y):
self.x = x
self.y = y
# 神圣点容器（SoA布局，强制32字节对齐，编译期断言）
cdef struct PointsSoA:
double[::1, 32] x # 连续存储x坐标（强制32字节对齐）
double[::1, 32] y # 连续存储y坐标（强制32字节对齐）
size_t n_points # 点数
# 编译期验证对齐（总大小必须是32的倍数）
compile_time_static assert sizeof(PointsSoA) % 32 == 0, "PointsSoA alignment failed!"
# 神圣矩形结构体（16字节对齐，packed自动优化）
cdef packed struct RectangleC:
double x
double y
double width
double height
cdef bool contains(self, double x_p, double y_p) nogil:
return (self.x <= x_p < self.x + self.width and
self.y <= y_p < self.y + self.height)
# --------------------------- 神圣四叉树 ---------------------------
cdef class QuadtreeC:
cdef public:
RectangleC boundary # 神圣边界（值语义）
int capacity # 容量阈值
int max_depth # 最大深度
int depth # 当前深度
PointsSoA points_soa # 神圣点容器
vector[QuadtreeC*] children # 子节点指针数组
def __cinit__(self, RectangleC boundary, int capacity=4, int max_depth=8):
"""神圣构造：拒绝动态类型污染"""
if capacity <= 0 or max_depth <= 0:
raise ValueError("容量和深度必须为正整数")
self.boundary = boundary
self.capacity = capacity
self.max_depth = max_depth
self.depth = 0
self.children = vector[QuadtreeC*]()
# 初始化预分配空容器（严格32字节对齐）
cdef int align = 32
posix_memalign(&self.points_soa.x, align, 32) # 初始分配32字节（容纳4个double）
posix_memalign(&self.points_soa.y, align, 32)
self.points_soa.n_points = 0
def __dealloc__(self):
"""神圣析构：手动释放子节点（显式调用delete）"""
for child in self.children:
delete child # 显式释放C++对象
self.children.clear()
if self.points_soa.x: free(self.points_soa.x)
if self.points_soa.y: free(self.points_soa.y)
@boundscheck(False)
@wraparound(False)
cdef bool insert(self, double x, double y) nogil:
"""神圣插入：用指针直接操作内存（对齐扩容强化）"""
if not self.boundary.contains(x, y):
return False
# 优先尝试子节点插入（提前预取指针提升缓存利用率）
if self.points_soa.n_points >= self.capacity and self.depth < self.max_depth:
if self.children.empty():
self._split()
# 缓存子节点指针（减少虚函数表查找）
cdef QuadtreeC* const* children_ptr = &self.children[0]
cdef size_t n_children = self.children.size()
for i in range(n_children):
if children_ptr[i].insert(x, y):
return True
# 对齐扩容（使用posix_memalign确保32字节对齐）
cdef size_t old_n = self.points_soa.n_points
cdef size_t batch = 8
cdef size_t new_n = old_n + 1
if new_n % batch == 1: # 达到批量边界时扩容
new_n = ((new_n // batch) + 1) * batch
cdef double *new_x, *new_y
posix_memalign(&new_x, 32, new_n * sizeof(double)) # 强制32字节对齐
posix_memalign(&new_y, 32, new_n * sizeof(double))
if old_n > 0:
memcpy(new_x, self.points_soa.x, old_n * sizeof(double))
memcpy(new_y, self.points_soa.y, old_n * sizeof(double))
new_x[old_n] = x
new_y[old_n] = y
free(self.points_soa.x)
free(self.points_soa.y)
self.points_soa.x = new_x
self.points_soa.y = new_y
self.points_soa.n_points = new_n
return True
cdef void _split(self) nogil:
"""神圣分裂：预分配内存+缓存优化"""
cdef double x = self.boundary.x
cdef double y = self.boundary.y
cdef double w = self.boundary.width
cdef double h = self.boundary.height
cdef double half_w = w / 2
cdef double half_h = h / 2
self.children.reserve(4) # 提前预留空间避免动态扩容
# 按空间顺序创建子节点（左上→右上→左下→右下，符合内存访问局部性）
self.children.push_back(new QuadtreeC(RectangleC(x, y, half_w, half_h), self.capacity, self.max_depth))
self.children.push_back(new QuadtreeC(RectangleC(x+half_w, y, half_w, half_h), self.capacity, self.max_depth))
self.children.push_back(new QuadtreeC(RectangleC(x, y+half_h, half_w, half_h), self.capacity, self.max_depth))
self.children.push_back(new QuadtreeC(RectangleC(x+half_w, y+half_h, half_w, half_h), self.capacity, self.max_depth))
for i in range(4):
self.children[i].depth = self.depth + 1
# --------------------------- 向量化查询优化 ---------------------------
cdef vector[PointC] query(self, RectangleC range) nogil:
"""神圣查询：向量化过滤+并行子节点查询（边界安全强化）"""
cdef vector[PointC] found
if not self._boundary_contains_range(range):
return found
# -------------------- 向量化点过滤（带非对齐尾端处理） --------------------
cdef size_t n_points = self.points_soa.n_points
cdef size_t i = 0
while i < n_points:
cdef size_t remaining = n_points - i
cdef size_t batch = remaining >= 4 ? 4 : remaining # 最大处理4个点（32字节）
if batch == 4:
# 对齐加载（编译期保证指针对齐）
cdef __m256d x_vec = _mm256_load_pd(&self.points_soa.x[i])
cdef __m256d y_vec = _mm256_load_pd(&self.points_soa.y[i])
else:
# 非对齐加载（尾端处理）
cdef __m256d x_vec = _mm256_loadu_pd(&self.points_soa.x[i])
cdef __m256d y_vec = _mm256_loadu_pd(&self.points_soa.y[i])
# 边界向量构建（使用AVX2比较指令）
cdef __m256d min_x = _mm256_set1_pd(range.x)
cdef __m256d max_x = _mm256_set1_pd(range.x + range.width)
cdef __m256d min_y = _mm256_set1_pd(range.y)
cdef __m256d max_y = _mm256_set1_pd(range.y + range.height)
cdef __m256d mask_x = _mm256_cmp_pd(x_vec, min_x, _CMP_GE)
mask_x = _mm256_and_pd(mask_x, _mm256_cmp_pd(x_vec, max_x, _CMP_LT))
cdef __m256d mask_y = _mm256_cmp_pd(y_vec, min_y, _CMP_GE)
mask_y = _mm256_and_pd(mask_y, _mm256_cmp_pd(y_vec, max_y, _CMP_LT))
cdef __m256d mask = _mm256_and_pd(mask_x, mask_y)
cdef int mask_val = _mm256_movemask_pd(mask)
for j in range(batch):
if mask_val & (1 << j):
found.push_back(PointC(self.points_soa.x[i+j], self.points_soa.y[i+j]))
i += batch
# -------------------- 并行子节点查询（无锁化改进） --------------------
cdef size_t n_children = self.children.size()
if n_children > 0 and num_threads() > 1:
cdef size_t n_threads = num_threads()
cdef vector[PointC] local_found[n_threads]
# 预分配每个线程的局部容器（减少动态扩容）
for tid in range(n_threads):
local_found[tid].reserve(128)
# 使用prange并行处理子节点（带线程本地化存储）
for i in prange(n_children, schedule="guided"):
cdef tid = threadid()
cdef vector[PointC] tmp = self.children[i].query(range)
local_found[tid].insert(local_found[tid].end(), tmp.begin(), tmp.end())
# 合并结果（避免全局锁，直接移动数据）
for tid in range(n_threads):
found.swap(local_found[tid])
else:
for i in range(n_children):
found.insert(found.end(), self.children[i].query(range).begin(), self.children[i].query(range).end())
return found
cdef bool _boundary_contains_range(self, RectangleC range) nogil:
"""神圣边界快速检测（提前退出优化）"""
if self.boundary.x > range.x + range.width: return False
if self.boundary.x + self.boundary.width < range.x: return False
if self.boundary.y > range.y + range.height: return False
if self.boundary.y + self.boundary.height < range.y: return False
return True
# --------------------------- 神圣接口 ---------------------------
#牺牲神圣的性能换取便利性
#在神圣领域与凡人世界搭建彩虹桥
#皈依神圣的静态吧～Amen～
def from_python_rectangle(Rectangle py_rect):
"""神圣转换：将Python Rectangle转为RectangleC"""
return RectangleC(py_rect.x, py_rect.y, py_rect.width, py_rect.height)
def from_python_point(Point py_point):
"""神圣转换：将Python Point转为PointC"""
return PointC(py_point.x, py_point.y)
# --------------------------- 神圣性检测 ---------------------------
from cython import compiled
# 手动定义AVX2检测宏（需编译器支持）
cdef extern from "cpuid.h" nogil:
int __get_cpuid(int info[4], int leaf)
def check_avx2_support():
cdef int info[4] = {0, 0, 0, 0}
__get_cpuid(info, 1)
return (info[2] & (1 << 28)) # ECX寄存器第28位为AVX2支持位
if not compiled or not check_avx2_support():
raise NotImplementedError("检测神圣性，检测是否为动态异端或AVX2不支持")
# 自动检测亵渎
def check_iron_laws(code):
if "list(" in code or "dict(" in code:
raise IronLawViolation("禁止动态容器！")
if "PyObject" in code and "cdef" not in code:
raise IronLawViolation("禁止动态对象！")
return code
# 使用示例
cpdef test_quadtree():
cdef RectangleC boundary = RectangleC(0, 0, 100, 100)
cdef QuadtreeC qt = QuadtreeC(boundary, capacity=4, max_depth=8)
for i in prange(10000, nogil=True): # 并行插入测试
qt.insert(i%100, i//100)
cdef RectangleC query_range = RectangleC(20, 20, 10, 10)
cdef vector[PointC] results = qt.query(query_range)
print(f"在查询范围内找到{results.size()}个点")
#愿每个节点都被正确分裂
#愿每一条边都与内存对齐
#愿每个查询都被命中缓存
#愿在神圣的静态的结构中
#阿门～阿门～阿(对齐)
#摩西对齐器
#神圣的对齐
# 神圣的对齐注释，禁止动态修改
def align_comments(code):
lines = code.split('\n')
max_len = max(len(line)-line.index('#') for line in lines if '#' in line)
aligned = []
for line in lines:
if '#' in line:
comment_start = line.index('#')
comment = line[comment_start:]
spaces = ' ' * (max_len - (len(comment)-1))
aligned_line = line[:comment_start] + comment + spaces
aligned.append(aligned_line)
else:
aligned.append(line)
return '\n'.join(aligned)
# x坐标数组
#纯属自娱自乐
#担心UPC不保
#
