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+// So far in Ziglings, we've seen how for loops can be used to
+// repeat calculations across an array in several ways.
+//
+// For loops are generally great for this kind of task, but
+// sometimes they don't fully utilize the capabilities of the
+// CPU.
+//
+// Most modern CPUs can execute instructions in which SEVERAL
+// calculations are performed WITHIN registers at the SAME TIME.
+// These are known as "single instruction, multiple data" (SIMD)
+// instructions. SIMD instructions can make code significantly
+// more performant.
+//
+// To see why, imagine we have a program in which we take the
+// square root of four (changing) f32 floats.
+//
+// A simple compiler would take the program and produce machine code
+// which calculates each square root sequentially. Most registers on
+// modern CPUs have 64 bits, so we could imagine that each float moves
+// into a 64-bit register, and the following happens four times:
+//
+//            32 bits   32 bits
+//          +-------------------+
+// register |    0    |    x    |
+//          +-------------------+
+//
+//                    |
+//           [SQRT instruction]
+//                    V
+//
+//          +-------------------+
+//          |    0    | sqrt(x) |
+//          +-------------------+
+//
+// Notice that half of the register contains blank data to which
+// nothing happened. What a waste! What if we were able to use
+// that space instead? This is the idea at the core of SIMD.
+//
+// Most modern CPUs contain specialized registers with at least 128 bits
+// for performing SIMD instructions. On a machine with 128-bit SIMD
+// registers, a smart compiler would probably NOT issue four sqrt
+// instructions as above, but instead pack the floats into a single
+// 128-bit register, then execute a single "packed" sqrt
+// instruction to do ALL the square root calculations at once.
+//
+// For example:
+//
+//
+//             32 bits   32 bits   32 bits   32 bits
+//           +---------------------------------------+
+// register  |   4.0   |   9.0   |  25.0   |  49.0   |
+//           +---------------------------------------+
+//
+//                              |
+//                  [SIMD SQRT instruction]
+//                              V
+//
+//           +---------------------------------------+
+// register  |   2.0   |   3.0   |   5.0   |   7.0   |
+//           +---------------------------------------+
+//
+// Pretty cool, right?
+//
+// Code with SIMD instructions is usually more performant than code
+// without SIMD instructions. Zig cares a lot about performance,
+// so it has built-in support for SIMD! It has a data structure that
+// directly supports SIMD instructions:
+//
+//                        +-----------+
+//                        |  Vectors  |
+//                        +-----------+
+//
+// Operations performed on vectors in Zig will be done in parallel using
+// SIMD instructions, whenever possible.
+//
+// Defining vectors in Zig is straightforwards. No library import is needed.
+const v1 = @Vector(3, i32){ 1, 10, 100 };
+const v2 = @Vector(3, f32){ 2.0, 3.0, 5.0 };
+
+// Vectors support the same builtin operators as their underlying base types.
+const v3 = v1 + v1; // {   2,  20,  200};
+const v4 = v2 * v2; // { 4.0, 9.0, 25.0};
+
+// Intrinsics that apply to base types usually extend to vectors.
+const v5: @Vector(3, f32) = @floatFromInt(v3); // { 2.0,  20.0,  200.0}
+const v6 = v4 - v5; // { 2.0, -11.0, -175.0}
+const v7 = @abs(v6); // { 2.0,  11.0,  175.0}
+
+// We can make constant vectors, and reduce vectors.
+const v8: @Vector(4, u8) = @splat(2); // { 2, 2, 2, 2}
+const v8_sum = @reduce(.Add, v8); // 8
+const v8_min = @reduce(.Min, v8); // 2
+
+// Fixed-length arrays can be automatically assigned to vectors (and vice-versa).
+const single_digit_primes = [4]i8{ 2, 3, 5, 7 };
+const prime_vector: @Vector(4, i8) = single_digit_primes;
+
+// Now let's use vectors to simplify and optimize some code!
+//
+// Ewa is writing a program in which they frequently want to compare
+// two lists of four f32s. Ewa expects the lists to be similar, and
+// wants to determine the largest pairwise difference between the lists.
+//
+// Ewa wrote the following function to figure this out.
+
+fn calcMaxPairwiseDiffOld(list1: [4]f32, list2: [4]f32) f32 {
+    var max_diff: f32 = 0;
+    for (list1, list2) |n1, n2| {
+        const abs_diff = @abs(n1 - n2);
+        if (abs_diff > max_diff) {
+            max_diff = abs_diff;
+        }
+    }
+    return max_diff;
+}
+
+// Ewa heard about vectors in Zig, and started writing a new vector
+// version of the function, but has got stuck!
+//
+// Help Ewa finish the vector version! The examples above should help.
+
+const Vec4 = @Vector(4, f32);
+fn calcMaxPairwiseDiffNew(a: Vec4, b: Vec4) f32 {
+    const abs_diff_vec = ???;
+    const max_diff = @reduce(???, abs_diff_vec);
+    return max_diff;
+}
+
+// Quite the simplification! We could even write the function in one line
+// and it would still be readable.
+//
+// Since the entire function is now expressed in terms of vector operations,
+// the Zig compiler will easily be able to compile it down to machine code
+// which utilizes the all-powerful SIMD instructions and does a lot of the
+// computation in parallel.
+
+const std = @import("std");
+const print = std.debug.print;
+
+pub fn main() void {
+    const l1 = [4]f32{ 3.141, 2.718, 0.577, 1.000 };
+    const l2 = [4]f32{ 3.154, 2.707, 0.591, 0.993 };
+    const mpd_old = calcMaxPairwiseDiffOld(l1, l2);
+    const mpd_new = calcMaxPairwiseDiffNew(l1, l2);
+    print("Max difference (old fn): {d: >5.3}\n", .{mpd_old});
+    print("Max difference (new fn): {d: >5.3}\n", .{mpd_new});
+}