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+//
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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 tasks, which always reaches its limits when pure computing
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+// 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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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((1) * std.time.ns_per_s);
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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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+
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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.debug.print("thread {d}: {s}\n", .{ num, "started." });
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+ std.time.sleep((5 - num % 3) * 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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