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- //
- // 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.
- //
- // 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.
- //
- // 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.
- //
- // 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.
- //
- //
- // Synchronous Asynchronous
- // Processing Processing Multithreading
- // ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
- // │ Thread 1 │ │ Thread 1 │ │ Thread 1 │ │ Thread 2 │
- // ├──────────┤ ├──────────┤ ├──────────┤ ├──────────┤ Overall Time
- // └──┼┼┼┼┼───┴─┴──┼┼┼┼┼───┴──┴──┼┼┼┼┼───┴─┴──┼┼┼┼┼───┴──┬───────┬───────┬──
- // ├───┤ ├───┤ ├───┤ ├───┤ │ │ │
- // │ T │ │ T │ │ T │ │ T │ │ │ │
- // │ a │ │ a │ │ a │ │ a │ │ │ │
- // │ s │ │ s │ │ s │ │ s │ │ │ │
- // │ k │ │ k │ │ k │ │ k │ │ │ │
- // │ │ │ │ │ │ │ │ │ │ │
- // │ 1 │ │ 1 │ │ 1 │ │ 3 │ │ │ │
- // └─┬─┘ └─┬─┘ └─┬─┘ └─┬─┘ │ │ │
- // │ │ │ │ 5 Sec │ │
- // ┌────┴───┐ ┌─┴─┐ ┌─┴─┐ ┌─┴─┐ │ │ │
- // │Blocking│ │ T │ │ T │ │ T │ │ │ │
- // └────┬───┘ │ a │ │ a │ │ a │ │ │ │
- // │ │ s │ │ s │ │ s │ │ 8 Sec │
- // ┌─┴─┐ │ k │ │ k │ │ k │ │ │ │
- // │ T │ │ │ │ │ │ │ │ │ │
- // │ a │ │ 2 │ │ 2 │ │ 4 │ │ │ │
- // │ s │ └─┬─┘ ├───┤ ├───┤ │ │ │
- // │ k │ │ │┼┼┼│ │┼┼┼│ ▼ │ 10 Sec
- // │ │ ┌─┴─┐ └───┴────────┴───┴───────── │ │
- // │ 1 │ │ T │ │ │
- // └─┬─┘ │ a │ │ │
- // │ │ s │ │ │
- // ┌─┴─┐ │ k │ │ │
- // │ T │ │ │ │ │
- // │ a │ │ 1 │ │ │
- // │ s │ ├───┤ │ │
- // │ k │ │┼┼┼│ ▼ │
- // │ │ └───┴──────────────────────────────────────────── │
- // │ 2 │ │
- // ├───┤ │
- // │┼┼┼│ ▼
- // └───┴────────────────────────────────────────────────────────────────
- //
- //
- // The diagram was modeled on the one in a blog in which the differences
- // between asynchronous processing and multithreading are explained in detail:
- // https://blog.devgenius.io/multi-threading-vs-asynchronous-programming-what-is-the-difference-3ebfe1179a5
- //
- // Our exercise is essentially about clarifying the approach in Zig and
- // therefore we try to keep it as simple as possible.
- // Multithreading in itself is already difficult enough. ;-)
- //
- const std = @import("std");
- pub fn main() !void {
- // This is where the preparatory work takes place
- // before the parallel processing begins.
- std.debug.print("Starting work...\n", .{});
- // These curly brackets are very important, they are necessary
- // to enclose the area where the threads are called.
- // Without these brackets, the program would not wait for the
- // end of the threads and they would continue to run beyond the
- // end of the program.
- {
- // Now we start the first thread, with the number as parameter
- const handle = try std.Thread.spawn(.{}, thread_function, .{1});
- // Waits for the thread to complete,
- // then deallocates any resources created on `spawn()`.
- defer handle.join();
- // Second thread
- const handle2 = try std.Thread.spawn(.{}, thread_function, .{-4}); // that can't be right?
- defer handle2.join();
- // Third thread
- const handle3 = try std.Thread.spawn(.{}, thread_function, .{3});
- defer ??? // <-- something is missing
- // After the threads have been started,
- // they run in parallel and we can still do some work in between.
- std.Thread.sleep(1500 * std.time.ns_per_ms);
- std.debug.print("Some weird stuff, after starting the threads.\n", .{});
- }
- // After we have left the closed area, we wait until
- // the threads have run through, if this has not yet been the case.
- std.debug.print("Zig is cool!\n", .{});
- }
- // 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 {
- std.Thread.sleep(200 * num * std.time.ns_per_ms);
- std.debug.print("thread {d}: {s}\n", .{ num, "started." });
- // This timer simulates the work of the thread.
- const work_time = 3 * ((5 - num % 3) - 2);
- std.Thread.sleep(work_time * std.time.ns_per_s);
- std.debug.print("thread {d}: {s}\n", .{ num, "finished." });
- }
- // This is the easiest way to run threads in parallel.
- // In general, however, more management effort is required,
- // e.g. by setting up a pool and allowing the threads to communicate
- // with each other using semaphores.
- //
- // But that's a topic for another exercise.
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