Expand description
§par_iter_sync: Parallel Iterator With Sequential Output
Crate like rayon
do not offer synchronization mechanism.
This crate provides easy mixture of parallelism and synchronization.
Execute tasks in concurrency with synchronization at any steps.
Consider the case where multiple threads share a cache which can be read only after prior tasks have written to it (e.g., reads of task 4 depends on writes of task 1-4).
Using IntoParallelIteratorSync
trait
// in concurrency: task1 write | task2 write | task3 write | task4 write
// \_____________\_____________\_____________\
// task4 read depends on task 1-4 write \___________
// \
// in concurrency: | task2 read | task3 read | task4 read
use par_iter_sync::IntoParallelIteratorSync;
use std::sync::{Arc, Mutex};
use std::collections::HashSet;
// there are 100 tasks
let tasks = 0..100;
// an in-memory cache for integers
let cache: Arc<Mutex<HashSet<i32>>> = Arc::new(Mutex::new(HashSet::new()));
let cache_clone = cache.clone();
// iterate through tasks
tasks.into_par_iter_sync(move |task_number| {
// writes cache (write the integer in cache), in parallel
cache.lock().unwrap().insert(task_number);
// return the task number to the next iterator
Ok(task_number)
}).into_par_iter_sync(move |task_number| { // <- synced to sequential order
// reads
assert!(cache_clone.lock().unwrap().contains(&task_number));
Ok(())
// append a for each to actually run the whole chain
}).for_each(|_| ());
§Usage Caveat
This crate is designed to clone all resources captured by the closure
for each thread. To prevent unintended RAM usage, you may wrap
large data structure using Arc
.
§Sequential Consistency
The output order is guaranteed to be the same as the upstream iterator, but the execution order is not sequential.
§Examples
§Mix Syncing and Parallelism By Chaining
use par_iter_sync::IntoParallelIteratorSync;
(0..100).into_par_iter_sync(|i| {
Ok(i) // <~ async execution
}).into_par_iter_sync(|i| { // <- sync order
Ok(i) // <~async execution
}).into_par_iter_sync(|i| { // <- sync order
Ok(i) // <~async execution
}).for_each(|x| ()); // <- sync order
§Use std::iter::IntoIterator
interface
use par_iter_sync::IntoParallelIteratorSync;
let mut count = 0;
// for loop
for i in (0..100).into_par_iter_sync(|i| Ok(i)) {
assert_eq!(i, count);
count += 1;
}
// sum
let sum: i32 = (1..=100).into_par_iter_sync(|i| Ok(i)).sum();
// skip, take and collect
let results: Vec<i32> = (0..10)
.into_par_iter_sync(|i| Ok(i))
.skip(1)
.take(5)
.collect();
assert_eq!(sum, 5050);
assert_eq!(results, vec![1, 2, 3, 4, 5])
§Bridge To Rayon
use par_iter_sync::IntoParallelIteratorSync;
use rayon::prelude::*;
// sum with rayon
let sum: i32 = (1..=100)
.into_par_iter_sync(|i| Ok(i))
.par_bridge() // <- switch to rayon
.into_par_iter()
.sum();
assert_eq!(sum, 5050);
§Closure Captures Variables
Variables captured are cloned to each thread automatically.
use par_iter_sync::IntoParallelIteratorSync;
use std::sync::Arc;
// use `Arc` to save RAM
let resource_captured = Arc::new(vec![3, 1, 4, 1, 5, 9, 2, 6, 5, 3]);
let len = resource_captured.len();
let result_iter = (0..len).into_par_iter_sync(move |i| {
// `resource_captured` is moved into the closure
// and cloned to worker threads.
let read_from_resource = resource_captured.get(i).unwrap();
Ok(*read_from_resource)
});
// the result is produced in sequential order
let collected: Vec<i32> = result_iter.collect();
assert_eq!(collected, vec![3, 1, 4, 1, 5, 9, 2, 6, 5, 3])
§Fast Fail During Exception
The iterator stops once the inner function returns an Err
.
use par_iter_sync::IntoParallelIteratorSync;
use std::sync::Arc;
use log::warn;
/// this function returns `Err` when it reads 1000
fn error_at_1000(n: i32) -> Result<i32, ()> {
if n == 1000 {
// you may log this error
warn!("Some Error Occurs");
Err(())
} else {
Ok(n)
}
}
let results: Vec<i32> = (0..10000).into_par_iter_sync(move |a| {
Ok(a)
}).into_par_iter_sync(move |a| {
// error at 1000
error_at_1000(a)
}).into_par_iter_sync(move |a| {
Ok(a)
}).collect();
let expected: Vec<i32> = (0..1000).collect();
assert_eq!(results, expected)
§You may choose to skip error
If you do not want to stop on Err
, this is a workaround.
use par_iter_sync::IntoParallelIteratorSync;
use std::sync::Arc;
let results: Vec<Result<i32, ()>> = (0..5).into_par_iter_sync(move |n| {
// error at 3, but skip
if n == 3 {
Ok(Err(()))
} else {
Ok(Ok(n))
}
}).collect();
assert_eq!(results, vec![Ok(0), Ok(1), Ok(2), Err(()), Ok(4)])
§Overhead Benchmark
Platform: Macbook Air (2015 Late) 8 GB RAM, Intel Core i5, 1.6GHZ (2 Core).
§Result
One million (1,000,000) empty iteration for each run.
test iter_async::test_par_iter_async::bench_into_par_iter_async
... bench: 110,277,577 ns/iter (+/- 28,510,054)
test test_par_iter::bench_into_par_iter_sync
... bench: 121,063,787 ns/iter (+/- 103,787,056)
Result:
- Async iterator overhead
110 ns (+/- 28 ns)
. - Sync iterator overhead
121 ns (+/- 103 ns)
.
§Implementation Note
§Output Buffering
- Each worker use a synced single-producer mpsc channel to buffer outputs. So, when a thread is waiting for its turn to get polled, it does not get blocked. The channel size is hard-coded to 100 for each thread.
- The number of threads equals to the number of logical cores.
§Synchronization and Exception Handling
- When each thread fetch a task, it registers its thread ID and task ID into a registry.
- When
next()
is called, the consumer fetch from the task registry the next thread ID. next()
returns None if there is no more task or if some Error occurs.
Structs§
- ParIter
Async - iterate through blocks according to array index.
- ParIter
Sync - implementation of lock-free sequential parallel iterator
Traits§
- Into
Parallel Iterator Async - This trait implement the async version of
IntoParallelIteratorSync
- Into
Parallel Iterator Sync - lock-free sequential parallel iterator