goankur commented on code in PR #13572: URL: https://github.com/apache/lucene/pull/13572#discussion_r1819967252
########## lucene/native/src/c/dotProduct.c: ########## @@ -0,0 +1,209 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include <stdint.h> +#include <stdio.h> +#include <stdlib.h> +#include "dotProduct.h" + +#ifdef __ARM_ACLE +#include <arm_acle.h> +#endif + +#if (defined(__ARM_FEATURE_SVE)) +#include <arm_sve.h> + +/** + - ARM intrinsics guide - https://developer.arm.com/architectures/instruction-sets/intrinsics/#q=svptrue + - SVE Programming examples - https://developer.arm.com/documentation/dai0548/latest/ +*/ +void dump(int8_t vec[], int N) { + printf("["); + for (int i = 0; i < N ; i++) { + printf("%d,",vec[i]); + } + printf("]\n"); +} +/* + * Unrolled and vectorized int8 dotProduct implementation using SVE instructions + * NOTE: Clang 15.0 compiler on Apple M3 Max compiles the code below successfully + * with '-march=native+sve' option but throws "Illegal Hardware Instruction" error + * Looks like Apple M3 does not implement SVE and Apple's official documentation + * is not explicit about this or at least I could not find it. + * + */ +int32_t vdot8s_sve(int8_t vec1[], int8_t vec2[], int32_t limit) { Review Comment: > Was the gcc path less performant? Yes. The compiler auto-vectorized dot-product implementation shows `~15%` **LOWER** throughput compared to this manually unrolled and vectorized implementation which uses SVE intrinsics. Here is the JMH benchmark output from the next revision executed on `AWS Graviton 3` instance. C implementation compiled with `GCC 10` and compiler flags `-O3 -march=native -funroll-loops` ``` Benchmark (size) Mode Cnt Score Error Units VectorUtilBenchmark.dot8s 768 thrpt 15 43.233 ± 0.639 ops/us VectorUtilBenchmark.simpleDot8s 768 thrpt 15 36.437 ± 0.572 ops/us ``` Command to reproduce these results with the next revision ``` java --enable-native-access=ALL-UNNAMED \ --enable-preview \ -Djava.library.path="./lucene/native/build/libs/dotProduct/shared" \ -jar lucene/benchmark-jmh/build/benchmarks/lucene-benchmark-jmh-11.0.0-SNAPSHOT.jar regexp \ "(.*)?(ot8s)" \ -p "size=768" ``` A benefit of manually vectorized implementation is that our implementation is less likely to suffer from performance regressions due to auto-vectorization variations across different ARM compilers. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@lucene.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@lucene.apache.org For additional commands, e-mail: issues-h...@lucene.apache.org
