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135 lines
8.1 KiB
Zig
135 lines
8.1 KiB
Zig
//
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// Whenever there is a lot to calculate, the question arises as to how
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// tasks can be carried out simultaneously. We have already learned about
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// one possibility, namely asynchronous processes, in Exercises 84-91.
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//
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// However, the computing power of the processor is only distributed to
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// the started and running tasks, which always reaches its limits when
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// pure computing power is called up.
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//
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// For example, in blockchains based on proof of work, the miners have
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// to find a nonce for a certain character string so that the first m bits
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// in the hash of the character string and the nonce are zeros.
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// As the miner who can solve the task first receives the reward, everyone
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// tries to complete the calculations as quickly as possible.
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//
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// This is where multithreading comes into play, where tasks are actually
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// distributed across several cores of the CPU or GPU, which then really
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// means a multiplication of performance.
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//
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// The following diagram roughly illustrates the difference between the
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// various types of process execution.
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// The 'Overall Time' column is intended to illustrate how the time is
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// affected if, instead of one core as in synchronous and asynchronous
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// processing, a second core now helps to complete the work in multithreading.
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//
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// In the ideal case shown, execution takes only half the time compared
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// to the synchronous single thread. And even asynchronous processing
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// is only slightly faster in comparison.
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//
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//
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// Synchronous Asynchronous
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// Processing Processing Multithreading
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// ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
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// │ Thread 1 │ │ Thread 1 │ │ Thread 1 │ │ Thread 2 │
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// ├──────────┤ ├──────────┤ ├──────────┤ ├──────────┤ Overall Time
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// └──┼┼┼┼┼───┴─┴──┼┼┼┼┼───┴──┴──┼┼┼┼┼───┴─┴──┼┼┼┼┼───┴──┬───────┬───────┬──
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// ├───┤ ├───┤ ├───┤ ├───┤ │ │ │
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// │ T │ │ T │ │ T │ │ T │ │ │ │
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// │ a │ │ a │ │ a │ │ a │ │ │ │
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// │ s │ │ s │ │ s │ │ s │ │ │ │
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// │ k │ │ k │ │ k │ │ k │ │ │ │
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// │ │ │ │ │ │ │ │ │ │ │
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// │ 1 │ │ 1 │ │ 1 │ │ 3 │ │ │ │
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// └─┬─┘ └─┬─┘ └─┬─┘ └─┬─┘ │ │ │
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// │ │ │ │ 5 Sec │ │
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// ┌────┴───┐ ┌─┴─┐ ┌─┴─┐ ┌─┴─┐ │ │ │
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// │Blocking│ │ T │ │ T │ │ T │ │ │ │
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// └────┬───┘ │ a │ │ a │ │ a │ │ │ │
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// │ │ s │ │ s │ │ s │ │ 8 Sec │
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// ┌─┴─┐ │ k │ │ k │ │ k │ │ │ │
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// │ T │ │ │ │ │ │ │ │ │ │
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// │ a │ │ 2 │ │ 2 │ │ 4 │ │ │ │
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// │ s │ └─┬─┘ ├───┤ ├───┤ │ │ │
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// │ k │ │ │┼┼┼│ │┼┼┼│ ▼ │ 10 Sec
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// │ │ ┌─┴─┐ └───┴────────┴───┴───────── │ │
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// │ 1 │ │ T │ │ │
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// └─┬─┘ │ a │ │ │
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// │ │ s │ │ │
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// ┌─┴─┐ │ k │ │ │
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// │ T │ │ │ │ │
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// │ a │ │ 1 │ │ │
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// │ s │ ├───┤ │ │
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// │ k │ │┼┼┼│ ▼ │
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// │ │ └───┴──────────────────────────────────────────── │
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// │ 2 │ │
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// ├───┤ │
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// │┼┼┼│ ▼
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// └───┴────────────────────────────────────────────────────────────────
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//
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//
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// The diagram was modeled on the one in a blog in which the differences
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// between asynchronous processing and multithreading are explained in detail:
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// https://blog.devgenius.io/multi-threading-vs-asynchronous-programming-what-is-the-difference-3ebfe1179a5
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//
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// Our exercise is essentially about clarifying the approach in Zig and
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// therefore we try to keep it as simple as possible.
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// Multithreading in itself is already difficult enough. ;-)
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//
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const std = @import("std");
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pub fn main() !void {
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// This is where the preparatory work takes place
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// before the parallel processing begins.
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std.debug.print("Starting work...\n", .{});
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// These curly brackets are very important, they are necessary
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// to enclose the area where the threads are called.
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// Without these brackets, the program would not wait for the
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// end of the threads and they would continue to run beyond the
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// end of the program.
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{
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// Now we start the first thread, with the number as parameter
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const handle = try std.Thread.spawn(.{}, thread_function, .{1});
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// Waits for the thread to complete,
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// then deallocates any resources created on `spawn()`.
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defer handle.join();
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// Second thread
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const handle2 = try std.Thread.spawn(.{}, thread_function, .{-4}); // that can't be right?
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defer handle2.join();
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// Third thread
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const handle3 = try std.Thread.spawn(.{}, thread_function, .{3});
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defer ??? // <-- something is missing
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// After the threads have been started,
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// they run in parallel and we can still do some work in between.
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std.time.sleep(1500 * std.time.ns_per_ms);
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std.debug.print("Some weird stuff, after starting the threads.\n", .{});
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}
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// After we have left the closed area, we wait until
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// the threads have run through, if this has not yet been the case.
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std.debug.print("Zig is cool!\n", .{});
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}
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// This function is started with every thread that we set up.
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// In our example, we pass the number of the thread as a parameter.
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fn thread_function(num: usize) !void {
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std.time.sleep(200 * num * std.time.ns_per_ms);
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std.debug.print("thread {d}: {s}\n", .{ num, "started." });
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// This timer simulates the work of the thread.
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const work_time = 3 * ((5 - num % 3) - 2);
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std.time.sleep(work_time * std.time.ns_per_s);
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std.debug.print("thread {d}: {s}\n", .{ num, "finished." });
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}
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// This is the easiest way to run threads in parallel.
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// In general, however, more management effort is required,
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// e.g. by setting up a pool and allowing the threads to communicate
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// with each other using semaphores.
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//
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// But that's a topic for another exercise.
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