2 changed files with 0 additions and 243 deletions
--- a/src/Quantity.zig
+++ b/src/Quantity.zig
@ -527,112 +527,3 @@ test "Benchmark" {
    std.debug.print("\nAnti-optimisation sink: {d:.4}\n", .{gsink});
    try std.testing.expect(gsink != 0);
 }
 test "Overhead Analysis: Quantity vs Native" {
    const Io = std.Io;
    const ITERS: usize = 100_000;
    const SAMPLES: usize = 5;
    const io = std.testing.io;
    const getTime = struct {
        fn f(i: Io) Io.Timestamp {
            return Io.Clock.awake.now(i);
        }
    }.f;
    const fold = struct {
        fn f(comptime TT: type, s: *f64, v: TT) void {
            s.* += if (comptime @typeInfo(TT) == .float)
                @as(f64, @floatCast(v))
            else
                @as(f64, @floatFromInt(v));
        }
    }.f;
    // Helper to safely get a value of type T from a loop index
    const getValT = struct {
        fn f(comptime TT: type, i: usize) TT {
            const v = (i % 100) + 1;
            return if (comptime @typeInfo(TT) == .float) @floatFromInt(v) else @intCast(v);
        }
    }.f;
    const Types = .{ i32, i64, i128, f32, f64 };
    const TNames = .{ "i32", "i64", "i128", "f32", "f64" };
    const Ops = .{ "add", "mulBy", "divBy" };
    var gsink: f64 = 0;
    std.debug.print(
        \\
        \\ Quantity vs Native Overhead Analysis
        \\
        \\┌───────────┬──────┬───────────┬───────────┬───────────┐
        \\│ Operation │ Type │ Native    │ Quantity  │ Slowdown  │
        \\├───────────┼──────┼───────────┼───────────┼───────────┤
        \\
    , .{});
    inline for (Ops, 0..) |op_name, j| {
        inline for (Types, 0..) |T, tidx| {
            var native_total_ns: f64 = 0;
            var quantity_total_ns: f64 = 0;
            const M = Quantity(T, Dimensions.init(.{ .L = 1 }), Scales.init(.{}));
            const S = Quantity(T, Dimensions.init(.{ .T = 1 }), Scales.init(.{}));
            for (0..SAMPLES) |_| {
                // --- 1. Benchmark Native ---
                var n_sink: T = 0;
                const n_start = getTime(io);
                for (0..ITERS) |i| {
                    const a = getValT(T, i);
                    const b = getValT(T, 2);
                    const r = if (comptime std.mem.eql(u8, op_name, "add"))
                        a + b
                    else if (comptime std.mem.eql(u8, op_name, "mulBy"))
                        a * b
                    else
                        if (comptime @typeInfo(T) == .int) @divTrunc(a, b) else a / b;
                    if (comptime @typeInfo(T) == .float) n_sink += r else n_sink ^= r;
                }
                const n_end = getTime(io);
                native_total_ns += @as(f64, @floatFromInt(n_start.durationTo(n_end).toNanoseconds()));
                fold(T, &gsink, n_sink);
                // --- 2. Benchmark Quantity ---
                var q_sink: T = 0;
                const q_start = getTime(io);
                for (0..ITERS) |i| {
                    const qa = M{ .value = getValT(T, i) };
                    const qb = if (comptime std.mem.eql(u8, op_name, "divBy")) S{ .value = getValT(T, 2) } else M{ .value = getValT(T, 2) };
                    const r = if (comptime std.mem.eql(u8, op_name, "add"))
                        qa.add(qb)
                    else if (comptime std.mem.eql(u8, op_name, "mulBy"))
                        qa.mulBy(qb)
                    else
                        qa.divBy(qb);
                    if (comptime @typeInfo(T) == .float) q_sink += r.value else q_sink ^= r.value;
                }
                const q_end = getTime(io);
                quantity_total_ns += @as(f64, @floatFromInt(q_start.durationTo(q_end).toNanoseconds()));
                fold(T, &gsink, q_sink);
            }
            const avg_n = (native_total_ns / SAMPLES) / @as(f64, @floatFromInt(ITERS));
            const avg_q = (quantity_total_ns / SAMPLES) / @as(f64, @floatFromInt(ITERS));
            const slowdown = avg_q / avg_n;
            std.debug.print("│ {s:<9} │ {s:<4} │ {d:>7.2}ns │ {d:>7.2}ns │ {d:>8.2}x │\n", .{
                op_name, TNames[tidx], avg_n, avg_q, slowdown,
            });
        }
       if (j != Ops.len - 1) std.debug.print("├───────────┼──────┼───────────┼───────────┼───────────┤\n", .{});
    }
    std.debug.print("└───────────┴──────┴───────────┴───────────┴───────────┘\n", .{});
    try std.testing.expect(gsink != 0);
 }
--- a/tmp.md
+++ b/tmp.md
@ -1,134 +0,0 @@
 The slowdown you are seeing (1.5x to 2.1x) is primarily caused by **unnecessary branching and floating-point logic** inside your `to()` conversion function, which is called by every arithmetic operation.
 Even though your `ratio` is calculated at `comptime`, the compiler often struggles to optimize out the floating-point paths and the `if/else` logic inside `to()` when it's wrapped in generic struct methods.
 Here are the specific areas to optimize and the corrected code.
 ### 1. The `to` Function (The Bottleneck)
 In your current code, `add` calls `self.to(TargetType)` and `rhs.to(TargetType)`. Even if the scales are identical, the code enters a function that performs floating-point checks. 
 **Optimization:** Add a short-circuit for the identity conversion and use `inline` to ensure the conversion is literally just a primitive op.
 ### 2. The `mulBy` / `divBy` Logic
 Currently, `mulBy` converts both operands to a "min" scale before multiplying. In physics, $1km \times 1s$ is just $1000$ units of $m \cdot s$. There is no need to convert both to a common scale before multiplying; you only need to calculate the **resulting** scale.
 ### 3. `QuantityVec` Loop Overhead
 In `QuantityVec`, you are initializing a new `Quantity` struct *inside* the loop for every element. While Zig is good at optimizing structs, this creates significant pressure on the optimizer.
 ---
 ### Optimized `Quantity.zig`
 Replace your `Quantity` struct methods with these. I have introduced a `Conversion` helper to ensure zero runtime overhead for identical scales.
 ```zig
 pub fn to(self: Self, comptime Dest: type) Dest {
    if (comptime !dims.eql(Dest.dims))
        @compileError("Dimension mismatch");
    // 1. Absolute identity: No-op
    if (comptime @TypeOf(self) == Dest) return self;
    const ratio = comptime (scales.getFactor(dims) / Dest.scales.getFactor(Dest.dims));
    // 2. Scale identity: just cast the value type
    if (comptime ratio == 1.0) {
        return .{ .value = hlp.cast(Dest.ValueType, self.value) };
    }
    // 3. Fast-path: Integer scaling (multiplication)
    if (comptime @typeInfo(T) == .int and @typeInfo(Dest.ValueType) == .int and ratio > 1.0 and @round(ratio) == ratio) {
        const factor: Dest.ValueType = @intFromFloat(ratio);
        return .{ .value = hlp.cast(Dest.ValueType, self.value) * factor };
    }
    // 4. General path: use the most efficient math
    // We use a small inline helper to avoid floating point if ratio is an integer
    return .{ .value = hlp.applyRatio(Dest.ValueType, self.value, ratio) };
 }
 pub fn add(self: Self, rhs: anytype) Quantity(T, dims, scales.min(@TypeOf(rhs).scales)) {
    const ResQ = Quantity(T, dims, scales.min(@TypeOf(rhs).scales));
    // If scales match exactly, skip 'to' logic entirely
    if (comptime @TypeOf(self) == ResQ and @TypeOf(rhs) == ResQ) {
        return .{ .value = self.value + rhs.value };
    }
    return .{ .value = self.to(ResQ).value + rhs.to(ResQ).value };
 }
 pub fn mulBy(self: Self, rhs: anytype) Quantity(T, d.add(@TypeOf(rhs).dims), s.min(@TypeOf(rhs).scales)) {
    const Tr = @TypeOf(rhs);
    const ResQ = Quantity(T, d.add(Tr.dims), s.min(Tr.scales));
    // Physics optimization: 
    // Instead of converting both then multiplying, multiply then apply the cumulative ratio
    const raw_prod = self.value * rhs.value;
    const combined_ratio = comptime (s.getFactor(d) * Tr.scales.getFactor(Tr.dims)) / ResQ.scales.getFactor(ResQ.dims);
    return .{ .value = hlp.applyRatio(T, raw_prod, combined_ratio) };
 }
 ```
 ### Optimized `QuantityVec.zig`
 Using Zig's `@Vector` or ensuring the loop is "clean" will drastically improve performance.
 ```zig
 pub fn add(self: Self, rhs: anytype) QuantityVec(len, Quantity(T, d, s.min(@TypeOf(rhs).scales))) {
    const Tr = @TypeOf(rhs);
    const ResQ = Quantity(T, d, s.min(Tr.scales));
    var res: QuantityVec(len, ResQ) = undefined;
    // Optimization: Pull the conversion logic OUT of the loop
    const ratio_lhs = comptime s.getFactor(d) / ResQ.scales.getFactor(d);
    const ratio_rhs = comptime Tr.scales.getFactor(Tr.dims) / ResQ.scales.getFactor(d);
    for (&res.data, 0..) |*out, i| {
        const v_lhs = hlp.applyRatio(T, self.data[i], ratio_lhs);
        const v_rhs = hlp.applyRatio(T, rhs.data[i], ratio_rhs);
        out.* = v_lhs + v_rhs;
    }
    return res;
 }
 ```
 ### Optimized `helper.zig`
 The logic for "how to multiply by a ratio" should be centralized to prevent the compiler from generating branches.
 ```zig
 pub inline fn cast(comptime T: type, value: anytype) T {
    const In = @TypeOf(value);
    if (T == In) return value;
    return switch (@typeInfo(T)) {
        .int => @intCast(value),
        .float => @floatCast(value),
        else => @compileError("Unsupported cast"),
    };
 }
 pub inline fn applyRatio(comptime T: type, value: anytype, comptime ratio: f64) T {
    if (comptime ratio == 1.0) return cast(T, value);
    if (comptime @typeInfo(T) == .int) {
        // If ratio is integer, use pure integer mul
        if (comptime @round(ratio) == ratio) {
            return cast(T, value) * @as(T, @intFromFloat(ratio));
        }
        // If ratio is fractional (e.g. 0.001), use float to avoid 0 results
        return @intFromFloat(@round(@as(f64, @floatFromInt(value)) * ratio));
    } else {
        return cast(T, value) * @as(T, @floatCast(ratio));
    }
 }
 ```
 ### Key Changes Explained
 1.  **Removing the `Quantity` Wrapper in Loops**: In `QuantityVec`, we no longer call `Q.add(R)`. We calculate the scale ratios at comptime, apply them to the raw values, and add. This allows the CPU to pipe the instructions much more effectively.
 2.  **Short-Circuiting**: The `if (comptime ratio == 1.0)` check is vital. It turns your `to()` function into a simple `value` return, which the compiler inlines away completely.
 3.  **Float avoidance for Integers**: In your original `to`, you were doing `scales.getFactor(dims) / Dest.scales.getFactor(Dest.dims)`. For large integer types (i128), converting to `f64` causes precision loss and uses the slow XMM/FPU registers. The new `applyRatio` logic favors pure integer multiplication where the ratio is a whole number.
 4.  **MulBy/DivBy Efficiency**: Your original code converted *before* multiplying. If you had $10km \times 10km$, it converted to $10000m \times 10000m$ (potentially overflowing an `i32`) and then multiplied. The new version multiplies first, then scales the result, which is fewer operations and safer for precision.
 ### Expected Result
 With these changes, the **Slowdown** column in your benchmark should drop from **~2.0x** to **~1.05x - 1.1x**. The remaining 5-10% is usually the overhead of the Zig compiler not being able to perfectly vectorize struct-wrapped arrays compared to raw slices.