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3 changed files with 0 additions and 167 deletions
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@ -1201,13 +1201,6 @@ const exercises = [_]Exercise{
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.main_file = "108_labeled_switch.zig",
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.output = "The pull request has been merged.",
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},
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.{
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.main_file = "109_vectors.zig",
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.output =
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\\Max difference (old fn): 0.014
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\\Max difference (new fn): 0.014
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,
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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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@ -1,147 +0,0 @@
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// So far in Ziglings, we've seen how for loops can be used to
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// repeat calculations across an array in several ways.
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//
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// For loops are generally great for this kind of task, but
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// sometimes they don't fully utilize the capabilities of the
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// CPU.
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//
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// Most modern CPUs can execute instructions in which SEVERAL
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// calculations are performed WITHIN registers at the SAME TIME.
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// These are known as "single instruction, multiple data" (SIMD)
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// instructions. SIMD instructions can make code significantly
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// more performant.
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//
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// To see why, imagine we have a program in which we take the
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// square root of four (changing) f32 floats.
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//
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// A simple compiler would take the program and produce machine code
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// which calculates each square root sequentially. Most registers on
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// modern CPUs have 64 bits, so we could imagine that each float moves
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// into a 64-bit register, and the following happens four times:
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//
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// 32 bits 32 bits
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// +-------------------+
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// register | 0 | x |
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// +-------------------+
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//
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// |
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// [SQRT instruction]
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// V
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//
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// +-------------------+
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// | 0 | sqrt(x) |
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// +-------------------+
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//
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// Notice that half of the register contains blank data to which
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// nothing happened. What a waste! What if we were able to use
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// that space instead? This is the idea at the core of SIMD.
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//
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// Most modern CPUs contain specialized registers with at least 128 bits
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// for performing SIMD instructions. On a machine with 128-bit SIMD
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// registers, a smart compiler would probably NOT issue four sqrt
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// instructions as above, but instead pack the floats into a single
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// 128-bit register, then execute a single "packed" sqrt
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// instruction to do ALL the square root calculations at once.
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//
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// For example:
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//
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//
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// 32 bits 32 bits 32 bits 32 bits
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// +---------------------------------------+
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// register | 4.0 | 9.0 | 25.0 | 49.0 |
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// +---------------------------------------+
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//
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// |
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// [SIMD SQRT instruction]
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// V
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//
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// +---------------------------------------+
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// register | 2.0 | 3.0 | 5.0 | 7.0 |
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// +---------------------------------------+
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//
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// Pretty cool, right?
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//
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// Code with SIMD instructions is usually more performant than code
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// without SIMD instructions. Zig cares a lot about performance,
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// so it has built-in support for SIMD! It has a data structure that
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// directly supports SIMD instructions:
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//
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// +-----------+
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// | Vectors |
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// +-----------+
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//
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// Operations performed on vectors in Zig will be done in parallel using
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// SIMD instructions, whenever possible.
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//
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// Defining vectors in Zig is straightforwards. No library import is needed.
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const v1 = @Vector(3, i32){ 1, 10, 100 };
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const v2 = @Vector(3, f32){ 2.0, 3.0, 5.0 };
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// Vectors support the same builtin operators as their underlying base types.
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const v3 = v1 + v1; // { 2, 20, 200};
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const v4 = v2 * v2; // { 4.0, 9.0, 25.0};
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// Intrinsics that apply to base types usually extend to vectors.
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const v5: @Vector(3, f32) = @floatFromInt(v3); // { 2.0, 20.0, 200.0}
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const v6 = v4 - v5; // { 2.0, -11.0, -175.0}
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const v7 = @abs(v6); // { 2.0, 11.0, 175.0}
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// We can make constant vectors, and reduce vectors.
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const v8: @Vector(4, u8) = @splat(2); // { 2, 2, 2, 2}
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const v8_sum = @reduce(.Add, v8); // 8
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const v8_min = @reduce(.Min, v8); // 2
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// Fixed-length arrays can be automatically assigned to vectors (and vice-versa).
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const single_digit_primes = [4]i8{ 2, 3, 5, 7 };
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const prime_vector: @Vector(4, i8) = single_digit_primes;
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// Now let's use vectors to simplify and optimize some code!
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//
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// Ewa is writing a program in which they frequently want to compare
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// two lists of four f32s. Ewa expects the lists to be similar, and
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// wants to determine the largest pairwise difference between the lists.
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//
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// Ewa wrote the following function to figure this out.
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fn calcMaxPairwiseDiffOld(list1: [4]f32, list2: [4]f32) f32 {
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var max_diff: f32 = 0;
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for (list1, list2) |n1, n2| {
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const abs_diff = @abs(n1 - n2);
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if (abs_diff > max_diff) {
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max_diff = abs_diff;
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}
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}
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return max_diff;
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}
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// Ewa heard about vectors in Zig, and started writing a new vector
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// version of the function, but has got stuck!
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//
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// Help Ewa finish the vector version! The examples above should help.
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const Vec4 = @Vector(4, f32);
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fn calcMaxPairwiseDiffNew(a: Vec4, b: Vec4) f32 {
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const abs_diff_vec = ???;
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const max_diff = @reduce(???, abs_diff_vec);
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return max_diff;
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}
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// Quite the simplification! We could even write the function in one line
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// and it would still be readable.
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//
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// Since the entire function is now expressed in terms of vector operations,
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// the Zig compiler will easily be able to compile it down to machine code
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// which utilizes the all-powerful SIMD instructions and does a lot of the
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// computation in parallel.
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const std = @import("std");
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const print = std.debug.print;
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pub fn main() void {
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const l1 = [4]f32{ 3.141, 2.718, 0.577, 1.000 };
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const l2 = [4]f32{ 3.154, 2.707, 0.591, 0.993 };
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const mpd_old = calcMaxPairwiseDiffOld(l1, l2);
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const mpd_new = calcMaxPairwiseDiffNew(l1, l2);
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print("Max difference (old fn): {d: >5.3}\n", .{mpd_old});
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print("Max difference (new fn): {d: >5.3}\n", .{mpd_new});
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}
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@ -1,13 +0,0 @@
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--- exercises/109_vectors.zig 2024-11-07 14:57:09.673383618 +0100
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+++ answers/109_vectors.zig 2024-11-07 14:22:59.069150138 +0100
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@@ -121,8 +121,8 @@
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const Vec4 = @Vector(4, f32);
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fn calcMaxPairwiseDiffNew(a: Vec4, b: Vec4) f32 {
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- const abs_diff_vec = ???;
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- const max_diff = @reduce(???, abs_diff_vec);
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+ const abs_diff_vec = @abs(a - b);
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+ const max_diff = @reduce(.Max, abs_diff_vec);
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return max_diff;
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}
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