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crypto.kt128: when using incremental hashing, use SIMD when possible #25783

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Merged
merged 1 commit into from
Nov 2, 2025
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crypto.kt128: when using incremental hashing, use SIMD when possible #25783

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1 change: 1 addition & 0 deletions lib/std/crypto/benchmark.zig
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ const hashes = [_]Crypto{
Crypto{ .ty = crypto.hash.sha3.Shake256, .name = "shake-256" },
Crypto{ .ty = crypto.hash.sha3.TurboShake128(null), .name = "turboshake-128" },
Crypto{ .ty = crypto.hash.sha3.TurboShake256(null), .name = "turboshake-256" },
Crypto{ .ty = crypto.hash.sha3.KT128, .name = "kt128" },
Crypto{ .ty = crypto.hash.blake2.Blake2s256, .name = "blake2s" },
Crypto{ .ty = crypto.hash.blake2.Blake2b512, .name = "blake2b" },
Crypto{ .ty = crypto.hash.Blake3, .name = "blake3" },
Expand Down
136 changes: 113 additions & 23 deletions lib/std/crypto/kangarootwelve.zig
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Original file line number Diff line number Diff line change
Expand Up @@ -848,6 +848,10 @@ fn KTHash(
final_state: ?StateType, // Running TurboSHAKE state for final node
num_leaves: usize, // Count of leaves processed (after first chunk)

// SIMD chunk batching
pending_chunks: [8 * chunk_size]u8 align(cache_line_size), // Buffer for up to 8 chunks
pending_count: usize, // Number of complete chunks in pending_chunks

/// Initialize a KangarooTwelve hashing context.
/// The customization string is optional and used for domain separation.
pub fn init(options: Options) Self {
Expand All @@ -861,9 +865,48 @@ fn KTHash(
.first_chunk = null,
.final_state = null,
.num_leaves = 0,
.pending_chunks = undefined,
.pending_count = 0,
};
}

/// Flush all pending chunks using SIMD when possible
fn flushPendingChunks(self: *Self) void {
const cv_size = Variant.cv_size;

// Process all pending chunks using the largest SIMD batch sizes possible
while (self.pending_count > 0) {
// Try SIMD batches in decreasing size order
inline for ([_]usize{ 8, 4, 2 }) |batch_size| {
if (optimal_vector_len >= batch_size and self.pending_count >= batch_size) {
var leaf_cvs: [batch_size * cv_size]u8 align(cache_line_size) = undefined;
processLeaves(Variant, batch_size, self.pending_chunks[0 .. batch_size * chunk_size], &leaf_cvs);
self.final_state.?.update(&leaf_cvs);
self.num_leaves += batch_size;
self.pending_count -= batch_size;

// Shift remaining chunks to the front
if (self.pending_count > 0) {
const remaining_bytes = self.pending_count * chunk_size;
@memcpy(self.pending_chunks[0..remaining_bytes], self.pending_chunks[batch_size * chunk_size ..][0..remaining_bytes]);
}
break; // Continue outer loop to try next batch
}
}

// If no SIMD batch was possible, process one chunk with scalar code
if (self.pending_count > 0 and self.pending_count < 2) {
var cv_buffer: [64]u8 = undefined;
const cv_slice = MultiSliceView.init(self.pending_chunks[0..chunk_size], &[_]u8{}, &[_]u8{});
Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]);
self.final_state.?.update(cv_buffer[0..cv_size]);
self.num_leaves += 1;
self.pending_count -= 1;
break; // No more chunks to process
}
}
}

/// Absorb data into the hash state.
/// Can be called multiple times to incrementally add data.
pub fn update(self: *Self, data: []const u8) void {
Expand Down Expand Up @@ -895,15 +938,21 @@ fn KTHash(
const padding = [_]u8{ 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 };
self.final_state.?.update(&padding);
} else {
// Subsequent chunks - process as leaf and absorb CV
const cv_size = Variant.cv_size;
var cv_buffer: [64]u8 = undefined; // Max CV size
const cv_slice = MultiSliceView.init(&self.buffer, &[_]u8{}, &[_]u8{});
Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]);

// Absorb CV into final state immediately
self.final_state.?.update(cv_buffer[0..cv_size]);
self.num_leaves += 1;
// Add chunk to pending buffer for SIMD batch processing
@memcpy(self.pending_chunks[self.pending_count * chunk_size ..][0..chunk_size], &self.buffer);
self.pending_count += 1;

// Flush when we have enough chunks for optimal SIMD batch
// Determine best batch size for this architecture
const optimal_batch_size = comptime blk: {
if (optimal_vector_len >= 8) break :blk 8;
if (optimal_vector_len >= 4) break :blk 4;
if (optimal_vector_len >= 2) break :blk 2;
break :blk 1;
};
if (self.pending_count >= optimal_batch_size) {
self.flushPendingChunks();
}
}
self.buffer_len = 0;
}
Expand Down Expand Up @@ -931,24 +980,65 @@ fn KTHash(
return;
}

// Tree mode: we've already absorbed first_chunk + padding + intermediate CVs
// Now handle remaining buffer data
const remaining_with_custom_len = self.buffer_len + self.customization.len + self.custom_len_enc.len;
// Flush any pending chunks with SIMD
self.flushPendingChunks();

// Build view over remaining data (buffer + customization + encoding)
const remaining_view = MultiSliceView.init(
self.buffer[0..self.buffer_len],
self.customization,
self.custom_len_enc.slice(),
);
const remaining_len = remaining_view.totalLen();

var final_leaves = self.num_leaves;
var leaf_start: usize = 0;

// Tree mode: initialize if not already done (lazy initialization)
if (self.final_state == null and remaining_len > 0) {
self.final_state = StateType.init(.{});

// Absorb first chunk (up to chunk_size bytes from remaining data)
const first_chunk_len = @min(chunk_size, remaining_len);
if (remaining_view.tryGetSlice(0, first_chunk_len)) |first_chunk| {
// Data is contiguous, use it directly
self.final_state.?.update(first_chunk);
} else {
// Data spans boundaries, copy to buffer
var first_chunk_buf: [chunk_size]u8 = undefined;
remaining_view.copyRange(0, first_chunk_len, first_chunk_buf[0..first_chunk_len]);
self.final_state.?.update(first_chunk_buf[0..first_chunk_len]);
}

if (remaining_with_custom_len > 0) {
// Build final leaf data with customization
var final_leaf_buffer: [chunk_size + 256]u8 = undefined; // Extra space for customization
@memcpy(final_leaf_buffer[0..self.buffer_len], self.buffer[0..self.buffer_len]);
@memcpy(final_leaf_buffer[self.buffer_len..][0..self.customization.len], self.customization);
@memcpy(final_leaf_buffer[self.buffer_len + self.customization.len ..][0..self.custom_len_enc.len], self.custom_len_enc.slice());

// Generate CV for final leaf and absorb it
var cv_buffer: [64]u8 = undefined; // Max CV size
const cv_slice = MultiSliceView.init(final_leaf_buffer[0..remaining_with_custom_len], &[_]u8{}, &[_]u8{});
Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]);
// Absorb padding (8 bytes: 0x03 followed by 7 zeros)
const padding = [_]u8{ 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 };
self.final_state.?.update(&padding);

// Process remaining data as leaves
leaf_start = first_chunk_len;
}

// Process all remaining data as leaves (starting from leaf_start)
var offset = leaf_start;
while (offset < remaining_len) {
const leaf_end = @min(offset + chunk_size, remaining_len);
const leaf_size = leaf_end - offset;

var cv_buffer: [64]u8 = undefined;
if (remaining_view.tryGetSlice(offset, leaf_end)) |leaf_data| {
// Data is contiguous, use it directly
const cv_slice = MultiSliceView.init(leaf_data, &[_]u8{}, &[_]u8{});
Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]);
} else {
// Data spans boundaries, copy to buffer
var leaf_buf: [chunk_size]u8 = undefined;
remaining_view.copyRange(offset, leaf_end, leaf_buf[0..leaf_size]);
const cv_slice = MultiSliceView.init(leaf_buf[0..leaf_size], &[_]u8{}, &[_]u8{});
Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]);
}
self.final_state.?.update(cv_buffer[0..cv_size]);
final_leaves += 1;
offset = leaf_end;
}

// Absorb right_encode(num_leaves) and terminator
Expand Down