高级并行技术
分布式计算策略
1. 消息传递接口（MPI）
from mpi4py import MPI
def distributed_computation():
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
## 分布式数据处理
data = list(range(rank * 10, (rank + 1) * 10))
result = sum(data)
## 从所有进程收集结果
total_result = comm.reduce(result, op=MPI.SUM, root=0)
if rank == 0:
print(f"总结果: {total_result}")
并行处理模式
2. 任务队列与工作线程模型
graph LR
A[任务队列] --> B[工作线程1]
A --> C[工作线程2]
A --> D[工作线程3]
B --> E[结果聚合]
C --> E
D --> E
import multiprocessing
from queue import Queue
def worker(task_queue, result_queue):
while not task_queue.empty():
task = task_queue.get()
result = process_task(task)
result_queue.put(result)
def parallel_task_processing(tasks, num_workers):
task_queue = multiprocessing.Queue()
result_queue = multiprocessing.Queue()
## 填充任务队列
for task in tasks:
task_queue.put(task)
## 创建工作进程
processes = []
for _ in range(num_workers):
p = multiprocessing.Process(
target=worker,
args=(task_queue, result_queue)
)
p.start()
processes.append(p)
## 等待所有进程完成
for p in processes:
p.join()
## 收集结果
results = []
while not result_queue.empty():
results.append(result_queue.get())
return results
高级同步技术
3. 屏障同步
import threading
import time
class BarrierSync:
def __init__(self, num_threads):
self.num_threads = num_threads
self.barrier = threading.Barrier(num_threads)
def worker(self, thread_id):
print(f"线程 {thread_id} 开始")
time.sleep(thread_id)
## 同步点
self.barrier.wait()
print(f"线程 {thread_id} 继续")
def run(self):
threads = []
for i in range(self.num_threads):
t = threading.Thread(target=self.worker, args=(i,))
threads.append(t)
t.start()
for t in threads:
t.join()
并行算法策略
4. 映射规约范式
from functools import reduce
from multiprocessing import Pool
def map_reduce_example(data):
def mapper(x):
return x * x
def reducer(x, y):
return x + y
with Pool() as pool:
## 映射阶段
mapped_data = pool.map(mapper, data)
## 规约阶段
result = reduce(reducer, mapped_data)
return result
性能优化技术
| 技术 |
描述 |
用例 |
| 数据分区 |
将数据划分为更小的块 |
大型数据集处理 |
| 负载均衡 |
均匀分配工作 |
异构计算资源 |
| 缓存 |
存储中间结果 |
重复计算 |
并行计算框架
- Dask
- PySpark
- Ray
- Joblib
并行系统中的错误处理
def robust_parallel_execution(tasks):
try:
with multiprocessing.Pool() as pool:
results = pool.map(safe_task_execution, tasks)
return results
except Exception as e:
print(f"并行执行错误: {e}")
return None
def safe_task_execution(task):
try:
return task()
except Exception as e:
print(f"单个任务失败: {e}")
return None
最佳实践
- 尽量减少共享状态
- 设计容错机制
- 使用适当的同步机制
- 进行性能分析和优化
通过掌握这些高级并行技术，开发者可以使用 LabEx 的前沿计算工具构建高度可扩展且高效的应用程序。
