revival of the async-io functions

This commit is contained in:
Chris Boesch
2026-04-01 23:34:16 +02:00
parent db1fef8b86
commit fcfb0e80a0
5 changed files with 81 additions and 56 deletions

View File

@@ -1131,9 +1131,12 @@ const exercises = [_]Exercise{
},
.{
.main_file = "087_async4.zig",
.output = "1 2 3 4 5",
.skip = true,
.skip_hint = "async has not been implemented in the current compiler version.",
.output =
\\Task 1 done.
\\Task 2 done.
\\Task 3 done.
\\All tasks finished!
, // pay attention to the comma
},
.{
.main_file = "088_async5.zig",

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@@ -1,30 +1,50 @@
//
// It has probably not escaped your attention that we are no
// longer capturing a return value from foo() because the 'async'
// keyword returns the frame instead.
// When you have many tasks that don't return individual values,
// use a Group! A Group is an unordered set of tasks that can
// only be awaited or canceled as a whole:
//
// One way to solve this is to use a global variable.
// var group: std.Io.Group = .init;
// group.async(io, myTask, .{arg1});
// group.async(io, myTask, .{arg2});
// try group.await(io); // blocks until ALL tasks finish
//
// See if you can make this program print "1 2 3 4 5".
// Important rules:
// * The return type of functions spawned in a group must be
// coercible to Cancelable!void (i.e. void, or error{Canceled}!void).
// * Once you call group.async(), you MUST eventually call
// group.await() or group.cancel() to release resources.
// * group.cancel() requests cancellation on ALL members,
// then waits for them to finish.
//
const print = @import("std").debug.print;
// Unlike Future, Group tasks don't return values to the caller.
// They're ideal for parallel work that communicates through
// shared state or side effects (like printing).
//
// Fix this program to await all tasks in the group.
//
const std = @import("std");
const print = std.debug.print;
var global_counter: i32 = 0;
pub fn main(init: std.process.Init) !void {
const io = init.io;
pub fn main() void {
var foo_frame = async foo();
var group: std.Io.Group = .init;
while (global_counter <= 5) {
print("{} ", .{global_counter});
???
// Spawn 3 tasks in any order. Each sleeps for (id * 1) seconds
// before printing, so the output order is deterministic.
group.async(io, doWork, .{ io, 1 });
group.async(io, doWork, .{ io, 3 });
group.async(io, doWork, .{ io, 2 });
// Wait for all tasks to finish.
// What Group method blocks until all tasks complete?
try group.???
print("All tasks finished!\n", .{});
}
print("\n", .{});
}
fn foo() void {
while (true) {
???
???
}
fn doWork(io: std.Io, id: u32) void {
// Sleep ensures deterministic output order.
io.sleep(std.Io.Duration.fromSeconds(id), .awake) catch return;
print("Task {} done.\n", .{id});
}

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@@ -1,31 +1,22 @@
//
// Whenever there is a lot to calculate, the question arises as to how
// tasks can be carried out simultaneously. We have already learned about
// one possibility, namely asynchronous processes, in Exercises 84-91.
// In Exercises 84-91, we learned about Zig's Io interface for
// concurrent execution: io.async(), Group, Select, and Futures.
// Under the hood, the Threaded backend manages a pool of real
// OS threads for you - including scheduling, cancellation, and
// resource cleanup.
//
// However, the computing power of the processor is only distributed to
// the started and running tasks, which always reaches its limits when
// pure computing power is called up.
// But sometimes you need direct control over threads:
// * Long-lived dedicated workers
// * Specific stack sizes or thread counts
// * Code that doesn't have an Io interface available
// * Fine-grained synchronization patterns
//
// For example, in blockchains based on proof of work, the miners have
// to find a nonce for a certain character string so that the first m bits
// in the hash of the character string and the nonce are zeros.
// As the miner who can solve the task first receives the reward, everyone
// tries to complete the calculations as quickly as possible.
// That's where std.Thread comes in. It gives you a raw OS thread
// that you spawn, manage, and join yourself. No pool, no Futures,
// no automatic cancellation - but full control.
//
// This is where multithreading comes into play, where tasks are actually
// distributed across several cores of the CPU or GPU, which then really
// means a multiplication of performance.
//
// The following diagram roughly illustrates the difference between the
// various types of process execution.
// The 'Overall Time' column is intended to illustrate how the time is
// affected if, instead of one core as in synchronous and asynchronous
// processing, a second core now helps to complete the work in multithreading.
//
// In the ideal case shown, execution takes only half the time compared
// to the synchronous single thread. And even asynchronous processing
// is only slightly faster in comparison.
// The following diagram roughly illustrates the difference between
// the various types of process execution:
//
//
// Synchronous Asynchronous
@@ -108,7 +99,7 @@ pub fn main() !void {
// they run in parallel and we can still do some work in between.
var io_instance: std.Io.Threaded = .init_single_threaded;
const io = io_instance.io();
try io.sleep(std.Io.Duration.fromSeconds(4), .awake);
try io.sleep(std.Io.Duration.fromMilliseconds(400), .awake);
std.debug.print("Some weird stuff, after starting the threads.\n", .{});
}
// After we have left the closed area, we wait until
@@ -118,17 +109,17 @@ pub fn main() !void {
// This function is started with every thread that we set up.
// In our example, we pass the number of the thread as a parameter.
fn thread_function(num: usize) !void {
fn thread_function(id: usize) !void {
var io_instance: std.Io.Threaded = .init_single_threaded;
const io = io_instance.io();
try io.sleep(std.Io.Duration.fromSeconds(1 * @as(isize, @intCast(num))), .awake);
std.debug.print("thread {d}: {s}\n", .{ num, "started." });
try io.sleep(std.Io.Duration.fromMilliseconds(100 * @as(isize, @intCast(id))), .awake);
std.debug.print("thread {d}: {s}\n", .{ id, "started." });
// This timer simulates the work of the thread.
const work_time = 3 * ((5 - num % 3) - 2);
try io.sleep(std.Io.Duration.fromSeconds(@intCast(work_time)), .awake);
const work_time = 300 * ((5 - id % 3) - 2);
try io.sleep(std.Io.Duration.fromMilliseconds(@intCast(work_time)), .awake);
std.debug.print("thread {d}: {s}\n", .{ num, "finished." });
std.debug.print("thread {d}: {s}\n", .{ id, "finished." });
}
// This is the easiest way to run threads in parallel.
// In general, however, more management effort is required,

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@@ -0,0 +1,11 @@
--- exercises/087_async4.zig 2026-04-01 23:17:31.066443941 +0200
+++ answers/087_async4.zig 2026-04-01 23:17:39.251612131 +0200
@@ -38,7 +38,7 @@
// Wait for all tasks to finish.
// What Group method blocks until all tasks complete?
- try group.???
+ try group.await(io);
print("All tasks finished!\n", .{});
}

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@@ -1,6 +1,6 @@
--- exercises/104_threading.zig 2025-11-28 14:17:31.552529679 +0100
+++ answers/104_threading.zig 2025-11-28 14:15:36.823931851 +0100
@@ -97,12 +97,12 @@
--- exercises/104_threading.zig 2026-04-01 23:31:10.073198955 +0200
+++ answers/104_threading.zig 2026-04-01 23:29:51.314585919 +0200
@@ -88,12 +88,12 @@
defer handle.join();
// Second thread