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refactor(benches): update benchmarks to use SIMD-only implementation
- Remove all conditional compilation (#[cfg(feature = "reed-solomon-simd")]) - Rewrite erasure_benchmark.rs to focus on SIMD performance testing - Transform comparison_benchmark.rs into SIMD performance analysis - Update all documentation and comments to English - Remove references to reed-solomon-erasure implementation - Streamline benchmark groups and test configurations - Add comprehensive SIMD-specific performance tests Breaking Changes: - Benchmarks no longer compare different implementations - All benchmarks now test SIMD implementation exclusively - Benchmark naming conventions updated for clarity Performance Testing: - Enhanced shard size sensitivity analysis - Improved concurrent performance testing - Added memory efficiency benchmarks - Comprehensive error recovery analysis
This commit is contained in:
weisd
@@ -1,29 +1,28 @@
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//! 专门比较 Pure Erasure 和 Hybrid (SIMD) 模式性能的基准测试
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//! Reed-Solomon SIMD performance analysis benchmarks
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//!
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//! 这个基准测试使用不同的feature编译配置来直接对比两种实现的性能。
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//! This benchmark analyzes the performance characteristics of the SIMD Reed-Solomon implementation
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//! across different data sizes, shard configurations, and usage patterns.
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//!
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//! ## 运行比较测试
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//! ## Running Performance Analysis
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//!
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//! ```bash
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//! # 测试 Pure Erasure 实现 (默认)
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//! # Run all SIMD performance tests
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//! cargo bench --bench comparison_benchmark
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//!
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//! # 测试 Hybrid (SIMD) 实现
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//! cargo bench --bench comparison_benchmark --features reed-solomon-simd
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//! # Generate detailed performance report
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//! cargo bench --bench comparison_benchmark -- --save-baseline simd_analysis
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//!
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//! # 测试强制 erasure-only 模式
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//! cargo bench --bench comparison_benchmark
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//!
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//! # 生成对比报告
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//! cargo bench --bench comparison_benchmark -- --save-baseline erasure
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//! cargo bench --bench comparison_benchmark --features reed-solomon-simd -- --save-baseline hybrid
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//! # Run specific test categories
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//! cargo bench --bench comparison_benchmark encode_analysis
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//! cargo bench --bench comparison_benchmark decode_analysis
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//! cargo bench --bench comparison_benchmark shard_analysis
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//! ```
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use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
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use ecstore::erasure_coding::Erasure;
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use std::time::Duration;
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/// 基准测试数据配置
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/// Performance test data configuration
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struct TestData {
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data: Vec<u8>,
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size_name: &'static str,
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@@ -36,41 +35,41 @@ impl TestData {
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}
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}
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/// 生成不同大小的测试数据集
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/// Generate different sized test datasets for performance analysis
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fn generate_test_datasets() -> Vec<TestData> {
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vec![
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TestData::new(1024, "1KB"), // 小数据
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TestData::new(8 * 1024, "8KB"), // 中小数据
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TestData::new(64 * 1024, "64KB"), // 中等数据
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TestData::new(256 * 1024, "256KB"), // 中大数据
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TestData::new(1024 * 1024, "1MB"), // 大数据
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TestData::new(4 * 1024 * 1024, "4MB"), // 超大数据
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TestData::new(1024, "1KB"), // Small data
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TestData::new(8 * 1024, "8KB"), // Medium-small data
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TestData::new(64 * 1024, "64KB"), // Medium data
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TestData::new(256 * 1024, "256KB"), // Medium-large data
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TestData::new(1024 * 1024, "1MB"), // Large data
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TestData::new(4 * 1024 * 1024, "4MB"), // Extra large data
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]
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}
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/// 编码性能比较基准测试
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fn bench_encode_comparison(c: &mut Criterion) {
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/// SIMD encoding performance analysis
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fn bench_encode_analysis(c: &mut Criterion) {
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let datasets = generate_test_datasets();
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let configs = vec![
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(4, 2, "4+2"), // 常用配置
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(6, 3, "6+3"), // 50%冗余
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(8, 4, "8+4"), // 50%冗余，更多分片
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(4, 2, "4+2"), // Common configuration
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(6, 3, "6+3"), // 50% redundancy
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(8, 4, "8+4"), // 50% redundancy, more shards
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];
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for dataset in &datasets {
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for (data_shards, parity_shards, config_name) in &configs {
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let test_name = format!("{}_{}_{}", dataset.size_name, config_name, get_implementation_name());
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let test_name = format!("{}_{}_{}", dataset.size_name, config_name, "simd");
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let mut group = c.benchmark_group("encode_comparison");
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let mut group = c.benchmark_group("encode_analysis");
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group.throughput(Throughput::Bytes(dataset.data.len() as u64));
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group.sample_size(20);
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group.measurement_time(Duration::from_secs(10));
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// 检查是否能够创建erasure实例（某些配置在纯SIMD模式下可能失败）
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// Test SIMD encoding performance
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match Erasure::new(*data_shards, *parity_shards, dataset.data.len()).encode_data(&dataset.data) {
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Ok(_) => {
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group.bench_with_input(
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BenchmarkId::new("implementation", &test_name),
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BenchmarkId::new("simd_encode", &test_name),
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&(&dataset.data, *data_shards, *parity_shards),
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|b, (data, data_shards, parity_shards)| {
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let erasure = Erasure::new(*data_shards, *parity_shards, data.len());
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@@ -82,7 +81,7 @@ fn bench_encode_comparison(c: &mut Criterion) {
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);
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}
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Err(e) => {
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println!("⚠️ 跳过测试 {} - 配置不支持: {}", test_name, e);
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println!("⚠️ Skipping test {} - configuration not supported: {}", test_name, e);
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}
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}
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group.finish();
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@@ -90,35 +89,35 @@ fn bench_encode_comparison(c: &mut Criterion) {
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}
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}
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/// 解码性能比较基准测试
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fn bench_decode_comparison(c: &mut Criterion) {
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/// SIMD decoding performance analysis
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fn bench_decode_analysis(c: &mut Criterion) {
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let datasets = generate_test_datasets();
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let configs = vec![(4, 2, "4+2"), (6, 3, "6+3"), (8, 4, "8+4")];
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for dataset in &datasets {
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for (data_shards, parity_shards, config_name) in &configs {
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let test_name = format!("{}_{}_{}", dataset.size_name, config_name, get_implementation_name());
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let test_name = format!("{}_{}_{}", dataset.size_name, config_name, "simd");
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let erasure = Erasure::new(*data_shards, *parity_shards, dataset.data.len());
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// 预先编码数据 - 检查是否支持此配置
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// Pre-encode data - check if this configuration is supported
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match erasure.encode_data(&dataset.data) {
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Ok(encoded_shards) => {
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let mut group = c.benchmark_group("decode_comparison");
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let mut group = c.benchmark_group("decode_analysis");
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group.throughput(Throughput::Bytes(dataset.data.len() as u64));
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group.sample_size(20);
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group.measurement_time(Duration::from_secs(10));
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group.bench_with_input(
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BenchmarkId::new("implementation", &test_name),
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BenchmarkId::new("simd_decode", &test_name),
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&(&encoded_shards, *data_shards, *parity_shards),
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|b, (shards, data_shards, parity_shards)| {
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let erasure = Erasure::new(*data_shards, *parity_shards, dataset.data.len());
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b.iter(|| {
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// 模拟最大可恢复的数据丢失
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// Simulate maximum recoverable data loss
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let mut shards_opt: Vec<Option<Vec<u8>>> =
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shards.iter().map(|shard| Some(shard.to_vec())).collect();
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// 丢失等于奇偶校验分片数量的分片
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// Lose up to parity_shards number of shards
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for item in shards_opt.iter_mut().take(*parity_shards) {
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*item = None;
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}
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@@ -131,33 +130,33 @@ fn bench_decode_comparison(c: &mut Criterion) {
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group.finish();
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}
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Err(e) => {
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println!("⚠️ 跳过解码测试 {} - 配置不支持: {}", test_name, e);
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println!("⚠️ Skipping decode test {} - configuration not supported: {}", test_name, e);
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}
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}
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}
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}
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}
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/// 分片大小敏感性测试
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fn bench_shard_size_sensitivity(c: &mut Criterion) {
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/// Shard size sensitivity analysis for SIMD optimization
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fn bench_shard_size_analysis(c: &mut Criterion) {
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let data_shards = 4;
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let parity_shards = 2;
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// 测试不同的分片大小，特别关注SIMD的临界点
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// Test different shard sizes, focusing on SIMD optimization thresholds
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let shard_sizes = vec![32, 64, 128, 256, 512, 1024, 2048, 4096, 8192];
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let mut group = c.benchmark_group("shard_size_sensitivity");
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let mut group = c.benchmark_group("shard_size_analysis");
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group.sample_size(15);
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group.measurement_time(Duration::from_secs(8));
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for shard_size in shard_sizes {
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let total_size = shard_size * data_shards;
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let data = (0..total_size).map(|i| (i % 256) as u8).collect::<Vec<u8>>();
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let test_name = format!("{}B_shard_{}", shard_size, get_implementation_name());
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let test_name = format!("{}B_shard_simd", shard_size);
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group.throughput(Throughput::Bytes(total_size as u64));
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// 检查此分片大小是否支持
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// Check if this shard size is supported
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let erasure = Erasure::new(data_shards, parity_shards, data.len());
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match erasure.encode_data(&data) {
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Ok(_) => {
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@@ -170,15 +169,15 @@ fn bench_shard_size_sensitivity(c: &mut Criterion) {
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});
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}
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Err(e) => {
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println!("⚠️ 跳过分片大小测试 {} - 不支持: {}", test_name, e);
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println!("⚠️ Skipping shard size test {} - not supported: {}", test_name, e);
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}
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}
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}
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group.finish();
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}
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/// 高负载并发测试
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fn bench_concurrent_load(c: &mut Criterion) {
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/// High-load concurrent performance analysis
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fn bench_concurrent_analysis(c: &mut Criterion) {
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use std::sync::Arc;
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use std::thread;
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@@ -186,14 +185,14 @@ fn bench_concurrent_load(c: &mut Criterion) {
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let data = Arc::new((0..data_size).map(|i| (i % 256) as u8).collect::<Vec<u8>>());
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let erasure = Arc::new(Erasure::new(4, 2, data_size));
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let mut group = c.benchmark_group("concurrent_load");
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let mut group = c.benchmark_group("concurrent_analysis");
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group.throughput(Throughput::Bytes(data_size as u64));
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group.sample_size(10);
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group.measurement_time(Duration::from_secs(15));
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let test_name = format!("1MB_concurrent_{}", get_implementation_name());
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let test_name = "1MB_concurrent_simd";
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group.bench_function(&test_name, |b| {
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group.bench_function(test_name, |b| {
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b.iter(|| {
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let handles: Vec<_> = (0..4)
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.map(|_| {
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@@ -214,42 +213,44 @@ fn bench_concurrent_load(c: &mut Criterion) {
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group.finish();
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}
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/// 错误恢复能力测试
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fn bench_error_recovery_performance(c: &mut Criterion) {
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let data_size = 256 * 1024; // 256KB
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/// Error recovery performance analysis
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fn bench_error_recovery_analysis(c: &mut Criterion) {
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let data_size = 512 * 1024; // 512KB
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let data = (0..data_size).map(|i| (i % 256) as u8).collect::<Vec<u8>>();
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let configs = vec![
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(4, 2, 1), // 丢失1个分片
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(4, 2, 2), // 丢失2个分片（最大可恢复）
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(6, 3, 2), // 丢失2个分片
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(6, 3, 3), // 丢失3个分片（最大可恢复）
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(8, 4, 3), // 丢失3个分片
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(8, 4, 4), // 丢失4个分片（最大可恢复）
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// Test different error recovery scenarios
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let scenarios = vec![
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(4, 2, 1, "single_loss"), // Lose 1 shard
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(4, 2, 2, "double_loss"), // Lose 2 shards (maximum)
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(6, 3, 1, "single_loss_6_3"), // Lose 1 shard with 6+3
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(6, 3, 3, "triple_loss_6_3"), // Lose 3 shards (maximum)
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(8, 4, 2, "double_loss_8_4"), // Lose 2 shards with 8+4
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(8, 4, 4, "quad_loss_8_4"), // Lose 4 shards (maximum)
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];
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let mut group = c.benchmark_group("error_recovery");
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let mut group = c.benchmark_group("error_recovery_analysis");
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group.throughput(Throughput::Bytes(data_size as u64));
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group.sample_size(15);
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group.measurement_time(Duration::from_secs(8));
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group.measurement_time(Duration::from_secs(10));
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for (data_shards, parity_shards, lost_shards) in configs {
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for (data_shards, parity_shards, loss_count, scenario_name) in scenarios {
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let erasure = Erasure::new(data_shards, parity_shards, data_size);
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let test_name = format!("{}+{}_lost{}_{}", data_shards, parity_shards, lost_shards, get_implementation_name());
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// 检查此配置是否支持
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match erasure.encode_data(&data) {
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Ok(encoded_shards) => {
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let test_name = format!("{}+{}_{}", data_shards, parity_shards, scenario_name);
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group.bench_with_input(
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BenchmarkId::new("recovery", &test_name),
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&(&encoded_shards, data_shards, parity_shards, lost_shards),
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|b, (shards, data_shards, parity_shards, lost_shards)| {
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&(&encoded_shards, data_shards, parity_shards, loss_count),
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|b, (shards, data_shards, parity_shards, loss_count)| {
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let erasure = Erasure::new(*data_shards, *parity_shards, data_size);
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b.iter(|| {
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// Simulate specific number of shard losses
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let mut shards_opt: Vec<Option<Vec<u8>>> = shards.iter().map(|shard| Some(shard.to_vec())).collect();
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// 丢失指定数量的分片
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for item in shards_opt.iter_mut().take(*lost_shards) {
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// Lose the specified number of shards
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for item in shards_opt.iter_mut().take(*loss_count) {
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*item = None;
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}
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@@ -260,71 +261,57 @@ fn bench_error_recovery_performance(c: &mut Criterion) {
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);
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}
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Err(e) => {
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println!("⚠️ 跳过错误恢复测试 {} - 配置不支持: {}", test_name, e);
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println!("⚠️ Skipping recovery test {}: {}", scenario_name, e);
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}
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}
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}
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group.finish();
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}
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/// 内存效率测试
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fn bench_memory_efficiency(c: &mut Criterion) {
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let data_shards = 4;
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let parity_shards = 2;
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let data_size = 1024 * 1024; // 1MB
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/// Memory efficiency analysis
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fn bench_memory_analysis(c: &mut Criterion) {
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let data_sizes = vec![64 * 1024, 256 * 1024, 1024 * 1024]; // 64KB, 256KB, 1MB
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let config = (4, 2); // 4+2 configuration
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let mut group = c.benchmark_group("memory_efficiency");
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group.throughput(Throughput::Bytes(data_size as u64));
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group.sample_size(10);
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let mut group = c.benchmark_group("memory_analysis");
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group.sample_size(15);
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group.measurement_time(Duration::from_secs(8));
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let test_name = format!("memory_pattern_{}", get_implementation_name());
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for data_size in data_sizes {
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let data = (0..data_size).map(|i| (i % 256) as u8).collect::<Vec<u8>>();
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let size_name = format!("{}KB", data_size / 1024);
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// 测试连续多次编码对内存的影响
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group.bench_function(format!("{}_continuous", test_name), |b| {
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let erasure = Erasure::new(data_shards, parity_shards, data_size);
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b.iter(|| {
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for i in 0..10 {
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let data = vec![(i % 256) as u8; data_size];
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let shards = erasure.encode_data(black_box(&data)).unwrap();
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group.throughput(Throughput::Bytes(data_size as u64));
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// Test instance reuse vs new instance creation
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group.bench_with_input(BenchmarkId::new("reuse_instance", &size_name), &data, |b, data| {
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let erasure = Erasure::new(config.0, config.1, data.len());
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b.iter(|| {
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let shards = erasure.encode_data(black_box(data)).unwrap();
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black_box(shards);
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}
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});
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});
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});
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// 测试大量小编码任务
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group.bench_function(format!("{}_small_chunks", test_name), |b| {
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let chunk_size = 1024; // 1KB chunks
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let erasure = Erasure::new(data_shards, parity_shards, chunk_size);
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b.iter(|| {
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for i in 0..1024 {
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let data = vec![(i % 256) as u8; chunk_size];
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let shards = erasure.encode_data(black_box(&data)).unwrap();
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group.bench_with_input(BenchmarkId::new("new_instance", &size_name), &data, |b, data| {
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b.iter(|| {
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let erasure = Erasure::new(config.0, config.1, data.len());
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let shards = erasure.encode_data(black_box(data)).unwrap();
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black_box(shards);
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}
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});
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});
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});
|
||||
|
||||
}
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group.finish();
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}
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/// 获取当前实现的名称
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fn get_implementation_name() -> &'static str {
|
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#[cfg(feature = "reed-solomon-simd")]
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return "hybrid";
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|
||||
#[cfg(not(feature = "reed-solomon-simd"))]
|
||||
return "erasure";
|
||||
}
|
||||
|
||||
// Benchmark group configuration
|
||||
criterion_group!(
|
||||
benches,
|
||||
bench_encode_comparison,
|
||||
bench_decode_comparison,
|
||||
bench_shard_size_sensitivity,
|
||||
bench_concurrent_load,
|
||||
bench_error_recovery_performance,
|
||||
bench_memory_efficiency
|
||||
bench_encode_analysis,
|
||||
bench_decode_analysis,
|
||||
bench_shard_size_analysis,
|
||||
bench_concurrent_analysis,
|
||||
bench_error_recovery_analysis,
|
||||
bench_memory_analysis
|
||||
);
|
||||
|
||||
criterion_main!(benches);
|
||||
|
||||
Reference in New Issue
Block a user