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Diffstat (limited to 'exercises/109_vectors.zig')
| -rw-r--r-- | exercises/109_vectors.zig | 147 |
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diff --git a/exercises/109_vectors.zig b/exercises/109_vectors.zig deleted file mode 100644 index 96892ca..0000000 --- a/exercises/109_vectors.zig +++ /dev/null @@ -1,147 +0,0 @@ -// 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}); -} |