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Added second threading exercise.
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@ -219,7 +219,6 @@ Zig Core Language
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* [X] Bit manipulation
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* [X] Working with C
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* [X] Threading
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* [ ] Interfaces part 2
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Zig Standard Library
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@ -1117,6 +1117,10 @@ const exercises = [_]Exercise{
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\\Zig is cool!
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,
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},
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.{
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.main_file = "105_threading2.zig",
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.output = "PI ≈ 3.14159265",
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},
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.{
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.main_file = "999_the_end.zig",
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.output =
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107
exercises/105_threading2.zig
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107
exercises/105_threading2.zig
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@ -0,0 +1,107 @@
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//
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// Now that we are familiar with the principles of multithreading, we
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// boldly venture into a practical example from mathematics.
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// We will determine the circle number PI with sufficient accuracy.
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//
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// There are different methods for this, and some of them are several
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// hundred years old. For us, the dusty procedures are surprisingly well
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// suited to our exercise. Because the mathematicians of the time didn't
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// have fancy computers with which we can calculate something like this
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// in seconds today.
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// Whereby, of course, it depends on the accuracy, i.e. how many digits
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// after the decimal point we are interested in.
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// But these old procedures can still be tackled with paper and pencil,
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// which is why they are easier for us to understand.
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// At least for me. ;-)
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//
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// So let's take a mental leap back a few years.
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// Around 1672 (if you want to know and read about it in detail, you can
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// do so on Wikipedia, for example), various mathematicians once again
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// discovered a method of approaching the circle number PI.
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// There were the Scottish mathematician Gregory and the German
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// mathematician Leibniz, and even a few hundred years earlier the Indian
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// mathematician Madhava. All of them independently developed the same
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// formula, which was published by Leibnitz in 1682 in the journal
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// "Acta Eruditorum".
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// This is why this method has become known as the "Leibnitz series",
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// although the other names are also often used today.
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// We will not go into the formula and its derivation in detail, but
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// will deal with the series straight away:
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//
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// 4 4 4 4 4
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// PI = --- - --- + --- - --- + --- ...
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// 1 3 5 7 9
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//
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// As you can clearly see, the series starts with the whole number 4 and
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// approaches the circle number by subtracting and adding smaller and
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// smaller parts of 4. Pretty much everyone has learned PI = 3.14 at school,
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// but very few people remember other digits, and this is rarely necessary
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// in practice. Because either you don't need the precision, or you use a
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// calculator in which the number is stored as a very precise constant.
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// But at some point this constant was calculated and we are doing the same
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// now.The question at this point is, how many partial values do we have
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// to calculate for which accuracy?
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//
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// The answer is chewing, to get 8 digits after the decimal point we need
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// 1,000,000,000 partial values. And for each additional digit we have to
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// add a zero.
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// Even fast computers - and I mean really fast computers - get a bit warmer
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// on the CPU when it comes to really many diggits. But the 8 digits are
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// enough for us for now, because we want to understand the principle and
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// nothing more, right?
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//
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// As we have already discovered, the Leibnitz series is a series with a
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// fixed distance of 2 between the individual partial values. This makes
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// it easy to apply a simple loop to it, because if we start with n = 1
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// (which is not necessarily useful now) we always have to add 2 in each
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// round.
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// But wait! The partial values are alternately added and subtracted.
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// This could also be achieved with one loop, but not very elegantly.
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// It also makes sense to split this between two CPUs, one calculates
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// the positive values and the other the negative values. And so we can
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// simply start two threads and add everything up at the end and we're
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// done.
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// We just have to remember that if only the positive or negative values
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// are calculated, the distances are twice as large, i.e. 4.
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//
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// So that the whole thing has a real learning effect, the first thread
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// call is specified and you have to make the second.
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// But don't worry, it will work out. :-)
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//
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const std = @import("std");
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pub fn main() !void {
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const count = 1_000_000_000;
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var pi_plus: f64 = 0;
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var pi_minus: f64 = 0;
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{
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// First thread to calculate the plus numbers.
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const handle1 = try std.Thread.spawn(.{}, thread_pi, .{ &pi_plus, 5, count });
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defer handle1.join();
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// Second thread to calculate the minus numbers.
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???
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}
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// Here we add up the results.
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std.debug.print("PI ≈ {d:.8}\n", .{4 + pi_plus - pi_minus});
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}
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fn thread_pi(pi: *f64, begin: u64, end: u64) !void {
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var n: u64 = begin;
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while (n < end) : (n += 4) {
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pi.* += 4 / @as(f64, @floatFromInt(n));
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}
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}
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// If you wish, you can increase the number of loop passes, which
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// improves the number of digits.
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//
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// But be careful:
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// In order for parallel processing to really show its strengths,
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// the compiler must be given the "-O ReleaseFast" flag when it
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// is created. Otherwise the debug functions slow down the speed
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// to such an extent that seconds become minutes during execution.
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//
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// And you should remove the formatting restriction in "print",
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// otherwise you will not be able to see the additional diggits.
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13
patches/patches/105_threading2.patch
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13
patches/patches/105_threading2.patch
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--- exercises/105_threading2.zig 2024-03-23 16:35:14.754540802 +0100
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+++ answers/105_threading2.zig 2024-03-23 16:38:00.577539733 +0100
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@@ -81,8 +81,8 @@
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defer handle1.join();
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// Second thread to calculate the minus numbers.
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- ???
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-
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+ const handle2 = try std.Thread.spawn(.{}, thread_pi, .{ &pi_minus, 3, count });
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+ defer handle2.join();
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}
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// Here we add up the results.
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std.debug.print("PI ≈ {d:.8}\n", .{4 + pi_plus - pi_minus});
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