| ... | ... | @@ -848,6 +848,10 @@ fn KTHash( |
| 848 | 848 | final_state: ?StateType, // Running TurboSHAKE state for final node |
| 849 | 849 | num_leaves: usize, // Count of leaves processed (after first chunk) |
| 850 | 850 | |
| 851 | // SIMD chunk batching |
| 852 | pending_chunks: [8 * chunk_size]u8 align(cache_line_size), // Buffer for up to 8 chunks |
| 853 | pending_count: usize, // Number of complete chunks in pending_chunks |
| 854 | |
| 851 | 855 | /// Initialize a KangarooTwelve hashing context. |
| 852 | 856 | /// The customization string is optional and used for domain separation. |
| 853 | 857 | pub fn init(options: Options) Self { |
| ... | ... | @@ -861,9 +865,48 @@ fn KTHash( |
| 861 | 865 | .first_chunk = null, |
| 862 | 866 | .final_state = null, |
| 863 | 867 | .num_leaves = 0, |
| 868 | .pending_chunks = undefined, |
| 869 | .pending_count = 0, |
| 864 | 870 | }; |
| 865 | 871 | } |
| 866 | 872 | |
| 873 | /// Flush all pending chunks using SIMD when possible |
| 874 | fn flushPendingChunks(self: *Self) void { |
| 875 | const cv_size = Variant.cv_size; |
| 876 | |
| 877 | // Process all pending chunks using the largest SIMD batch sizes possible |
| 878 | while (self.pending_count > 0) { |
| 879 | // Try SIMD batches in decreasing size order |
| 880 | inline for ([_]usize{ 8, 4, 2 }) |batch_size| { |
| 881 | if (optimal_vector_len >= batch_size and self.pending_count >= batch_size) { |
| 882 | var leaf_cvs: [batch_size * cv_size]u8 align(cache_line_size) = undefined; |
| 883 | processLeaves(Variant, batch_size, self.pending_chunks[0 .. batch_size * chunk_size], &leaf_cvs); |
| 884 | self.final_state.?.update(&leaf_cvs); |
| 885 | self.num_leaves += batch_size; |
| 886 | self.pending_count -= batch_size; |
| 887 | |
| 888 | // Shift remaining chunks to the front |
| 889 | if (self.pending_count > 0) { |
| 890 | const remaining_bytes = self.pending_count * chunk_size; |
| 891 | @memcpy(self.pending_chunks[0..remaining_bytes], self.pending_chunks[batch_size * chunk_size ..][0..remaining_bytes]); |
| 892 | } |
| 893 | break; // Continue outer loop to try next batch |
| 894 | } |
| 895 | } |
| 896 | |
| 897 | // If no SIMD batch was possible, process one chunk with scalar code |
| 898 | if (self.pending_count > 0 and self.pending_count < 2) { |
| 899 | var cv_buffer: [64]u8 = undefined; |
| 900 | const cv_slice = MultiSliceView.init(self.pending_chunks[0..chunk_size], &[_]u8{}, &[_]u8{}); |
| 901 | Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]); |
| 902 | self.final_state.?.update(cv_buffer[0..cv_size]); |
| 903 | self.num_leaves += 1; |
| 904 | self.pending_count -= 1; |
| 905 | break; // No more chunks to process |
| 906 | } |
| 907 | } |
| 908 | } |
| 909 | |
| 867 | 910 | /// Absorb data into the hash state. |
| 868 | 911 | /// Can be called multiple times to incrementally add data. |
| 869 | 912 | pub fn update(self: *Self, data: []const u8) void { |
| ... | ... | @@ -895,15 +938,21 @@ fn KTHash( |
| 895 | 938 | const padding = [_]u8{ 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 }; |
| 896 | 939 | self.final_state.?.update(&padding); |
| 897 | 940 | } else { |
| 898 | | // Subsequent chunks - process as leaf and absorb CV |
| 899 | | const cv_size = Variant.cv_size; |
| 900 | | var cv_buffer: [64]u8 = undefined; // Max CV size |
| 901 | | const cv_slice = MultiSliceView.init(&self.buffer, &[_]u8{}, &[_]u8{}); |
| 902 | | Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]); |
| 903 | | |
| 904 | | // Absorb CV into final state immediately |
| 905 | | self.final_state.?.update(cv_buffer[0..cv_size]); |
| 906 | | self.num_leaves += 1; |
| 941 | // Add chunk to pending buffer for SIMD batch processing |
| 942 | @memcpy(self.pending_chunks[self.pending_count * chunk_size ..][0..chunk_size], &self.buffer); |
| 943 | self.pending_count += 1; |
| 944 | |
| 945 | // Flush when we have enough chunks for optimal SIMD batch |
| 946 | // Determine best batch size for this architecture |
| 947 | const optimal_batch_size = comptime blk: { |
| 948 | if (optimal_vector_len >= 8) break :blk 8; |
| 949 | if (optimal_vector_len >= 4) break :blk 4; |
| 950 | if (optimal_vector_len >= 2) break :blk 2; |
| 951 | break :blk 1; |
| 952 | }; |
| 953 | if (self.pending_count >= optimal_batch_size) { |
| 954 | self.flushPendingChunks(); |
| 955 | } |
| 907 | 956 | } |
| 908 | 957 | self.buffer_len = 0; |
| 909 | 958 | } |
| ... | ... | @@ -931,24 +980,65 @@ fn KTHash( |
| 931 | 980 | return; |
| 932 | 981 | } |
| 933 | 982 | |
| 934 | | // Tree mode: we've already absorbed first_chunk + padding + intermediate CVs |
| 935 | | // Now handle remaining buffer data |
| 936 | | const remaining_with_custom_len = self.buffer_len + self.customization.len + self.custom_len_enc.len; |
| 983 | // Flush any pending chunks with SIMD |
| 984 | self.flushPendingChunks(); |
| 985 | |
| 986 | // Build view over remaining data (buffer + customization + encoding) |
| 987 | const remaining_view = MultiSliceView.init( |
| 988 | self.buffer[0..self.buffer_len], |
| 989 | self.customization, |
| 990 | self.custom_len_enc.slice(), |
| 991 | ); |
| 992 | const remaining_len = remaining_view.totalLen(); |
| 993 | |
| 937 | 994 | var final_leaves = self.num_leaves; |
| 995 | var leaf_start: usize = 0; |
| 996 | |
| 997 | // Tree mode: initialize if not already done (lazy initialization) |
| 998 | if (self.final_state == null and remaining_len > 0) { |
| 999 | self.final_state = StateType.init(.{}); |
| 1000 | |
| 1001 | // Absorb first chunk (up to chunk_size bytes from remaining data) |
| 1002 | const first_chunk_len = @min(chunk_size, remaining_len); |
| 1003 | if (remaining_view.tryGetSlice(0, first_chunk_len)) |first_chunk| { |
| 1004 | // Data is contiguous, use it directly |
| 1005 | self.final_state.?.update(first_chunk); |
| 1006 | } else { |
| 1007 | // Data spans boundaries, copy to buffer |
| 1008 | var first_chunk_buf: [chunk_size]u8 = undefined; |
| 1009 | remaining_view.copyRange(0, first_chunk_len, first_chunk_buf[0..first_chunk_len]); |
| 1010 | self.final_state.?.update(first_chunk_buf[0..first_chunk_len]); |
| 1011 | } |
| 938 | 1012 | |
| 939 | | if (remaining_with_custom_len > 0) { |
| 940 | | // Build final leaf data with customization |
| 941 | | var final_leaf_buffer: [chunk_size + 256]u8 = undefined; // Extra space for customization |
| 942 | | @memcpy(final_leaf_buffer[0..self.buffer_len], self.buffer[0..self.buffer_len]); |
| 943 | | @memcpy(final_leaf_buffer[self.buffer_len..][0..self.customization.len], self.customization); |
| 944 | | @memcpy(final_leaf_buffer[self.buffer_len + self.customization.len ..][0..self.custom_len_enc.len], self.custom_len_enc.slice()); |
| 945 | | |
| 946 | | // Generate CV for final leaf and absorb it |
| 947 | | var cv_buffer: [64]u8 = undefined; // Max CV size |
| 948 | | const cv_slice = MultiSliceView.init(final_leaf_buffer[0..remaining_with_custom_len], &[_]u8{}, &[_]u8{}); |
| 949 | | Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]); |
| 1013 | // Absorb padding (8 bytes: 0x03 followed by 7 zeros) |
| 1014 | const padding = [_]u8{ 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 }; |
| 1015 | self.final_state.?.update(&padding); |
| 1016 | |
| 1017 | // Process remaining data as leaves |
| 1018 | leaf_start = first_chunk_len; |
| 1019 | } |
| 1020 | |
| 1021 | // Process all remaining data as leaves (starting from leaf_start) |
| 1022 | var offset = leaf_start; |
| 1023 | while (offset < remaining_len) { |
| 1024 | const leaf_end = @min(offset + chunk_size, remaining_len); |
| 1025 | const leaf_size = leaf_end - offset; |
| 1026 | |
| 1027 | var cv_buffer: [64]u8 = undefined; |
| 1028 | if (remaining_view.tryGetSlice(offset, leaf_end)) |leaf_data| { |
| 1029 | // Data is contiguous, use it directly |
| 1030 | const cv_slice = MultiSliceView.init(leaf_data, &[_]u8{}, &[_]u8{}); |
| 1031 | Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]); |
| 1032 | } else { |
| 1033 | // Data spans boundaries, copy to buffer |
| 1034 | var leaf_buf: [chunk_size]u8 = undefined; |
| 1035 | remaining_view.copyRange(offset, leaf_end, leaf_buf[0..leaf_size]); |
| 1036 | const cv_slice = MultiSliceView.init(leaf_buf[0..leaf_size], &[_]u8{}, &[_]u8{}); |
| 1037 | Variant.turboSHAKEToBuffer(&cv_slice, 0x0B, cv_buffer[0..cv_size]); |
| 1038 | } |
| 950 | 1039 | self.final_state.?.update(cv_buffer[0..cv_size]); |
| 951 | 1040 | final_leaves += 1; |
| 1041 | offset = leaf_end; |
| 952 | 1042 | } |
| 953 | 1043 | |
| 954 | 1044 | // Absorb right_encode(num_leaves) and terminator |
Frank Denis <124872+jedisct1@users.noreply.github.com> 2025-11-02 11:31:00+01:00
GitHub <noreply@github.com> 2025-11-02 11:31:00+01:00
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