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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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//!
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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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//!
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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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//!
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//! ```bash
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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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//! cargo bench --bench comparison_benchmark
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//!
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//!
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//! # 测试 Hybrid (SIMD) 实现
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//! # Generate detailed performance report
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//! cargo bench --bench comparison_benchmark --features reed-solomon-simd
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//! cargo bench --bench comparison_benchmark -- --save-baseline simd_analysis
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//!
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//!
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//! # 测试强制 erasure-only 模式
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//! # Run specific test categories
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//! cargo bench --bench comparison_benchmark
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//! cargo bench --bench comparison_benchmark encode_analysis
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//!
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//! cargo bench --bench comparison_benchmark decode_analysis
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//! # 生成对比报告
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//! cargo bench --bench comparison_benchmark shard_analysis
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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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//! ```
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//! ```
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use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
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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 ecstore::erasure_coding::Erasure;
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use std::time::Duration;
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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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struct TestData {
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data: Vec<u8>,
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data: Vec<u8>,
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size_name: &'static str,
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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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}
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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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fn generate_test_datasets() -> Vec<TestData> {
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vec![
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vec![
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TestData::new(1024, "1KB"), // 小数据
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TestData::new(1024, "1KB"), // Small data
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TestData::new(8 * 1024, "8KB"), // 中小数据
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TestData::new(8 * 1024, "8KB"), // Medium-small data
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TestData::new(64 * 1024, "64KB"), // 中等数据
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TestData::new(64 * 1024, "64KB"), // Medium data
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TestData::new(256 * 1024, "256KB"), // 中大数据
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TestData::new(256 * 1024, "256KB"), // Medium-large data
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TestData::new(1024 * 1024, "1MB"), // 大数据
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TestData::new(1024 * 1024, "1MB"), // Large data
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TestData::new(4 * 1024 * 1024, "4MB"), // 超大数据
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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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}
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}
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/// 编码性能比较基准测试
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/// SIMD encoding performance analysis
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fn bench_encode_comparison(c: &mut Criterion) {
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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 datasets = generate_test_datasets();
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let configs = vec![
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let configs = vec![
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(4, 2, "4+2"), // 常用配置
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(4, 2, "4+2"), // Common configuration
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(6, 3, "6+3"), // 50%冗余
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(6, 3, "6+3"), // 50% redundancy
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(8, 4, "8+4"), // 50%冗余，更多分片
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(8, 4, "8+4"), // 50% redundancy, more shards
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];
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];
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for dataset in &datasets {
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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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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.throughput(Throughput::Bytes(dataset.data.len() as u64));
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group.sample_size(20);
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group.sample_size(20);
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group.measurement_time(Duration::from_secs(10));
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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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match Erasure::new(*data_shards, *parity_shards, dataset.data.len()).encode_data(&dataset.data) {
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Ok(_) => {
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Ok(_) => {
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group.bench_with_input(
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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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&(&dataset.data, *data_shards, *parity_shards),
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|b, (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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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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}
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}
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Err(e) => {
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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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}
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}
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group.finish();
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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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}
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}
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/// 解码性能比较基准测试
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/// SIMD decoding performance analysis
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fn bench_decode_comparison(c: &mut Criterion) {
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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 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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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 dataset in &datasets {
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for (data_shards, parity_shards, config_name) in &configs {
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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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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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match erasure.encode_data(&dataset.data) {
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Ok(encoded_shards) => {
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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.throughput(Throughput::Bytes(dataset.data.len() as u64));
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group.sample_size(20);
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group.sample_size(20);
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group.measurement_time(Duration::from_secs(10));
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group.measurement_time(Duration::from_secs(10));
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group.bench_with_input(
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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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&(&encoded_shards, *data_shards, *parity_shards),
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|b, (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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let erasure = Erasure::new(*data_shards, *parity_shards, dataset.data.len());
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b.iter(|| {
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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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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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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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for item in shards_opt.iter_mut().take(*parity_shards) {
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*item = None;
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*item = None;
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}
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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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group.finish();
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}
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}
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Err(e) => {
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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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}
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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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/// Shard size sensitivity analysis for SIMD optimization
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fn bench_shard_size_sensitivity(c: &mut Criterion) {
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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 data_shards = 4;
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let parity_shards = 2;
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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 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.sample_size(15);
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group.measurement_time(Duration::from_secs(8));
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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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for shard_size in shard_sizes {
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let total_size = shard_size * data_shards;
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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 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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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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let erasure = Erasure::new(data_shards, parity_shards, data.len());
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match erasure.encode_data(&data) {
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match erasure.encode_data(&data) {
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Ok(_) => {
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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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}
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}
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Err(e) => {
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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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}
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}
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}
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group.finish();
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group.finish();
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}
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}
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/// 高负载并发测试
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/// High-load concurrent performance analysis
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fn bench_concurrent_load(c: &mut Criterion) {
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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::sync::Arc;
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use std::thread;
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use std::thread;
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|
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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 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 erasure = Arc::new(Erasure::new(4, 2, data_size));
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|
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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.throughput(Throughput::Bytes(data_size as u64));
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group.sample_size(10);
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group.sample_size(10);
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group.measurement_time(Duration::from_secs(15));
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group.measurement_time(Duration::from_secs(15));
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|
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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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|
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group.bench_function(&test_name, |b| {
|
group.bench_function(test_name, |b| {
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b.iter(|| {
|
b.iter(|| {
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let handles: Vec<_> = (0..4)
|
let handles: Vec<_> = (0..4)
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.map(|_| {
|
.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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group.finish();
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}
|
}
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|
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/// 错误恢复能力测试
|
/// Error recovery performance analysis
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fn bench_error_recovery_performance(c: &mut Criterion) {
|
fn bench_error_recovery_analysis(c: &mut Criterion) {
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let data_size = 256 * 1024; // 256KB
|
let data_size = 512 * 1024; // 512KB
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let data = (0..data_size).map(|i| (i % 256) as u8).collect::<Vec<u8>>();
|
let data = (0..data_size).map(|i| (i % 256) as u8).collect::<Vec<u8>>();
|
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|
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let configs = vec![
|
// Test different error recovery scenarios
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(4, 2, 1), // 丢失1个分片
|
let scenarios = vec![
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(4, 2, 2), // 丢失2个分片（最大可恢复）
|
(4, 2, 1, "single_loss"), // Lose 1 shard
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(6, 3, 2), // 丢失2个分片
|
(4, 2, 2, "double_loss"), // Lose 2 shards (maximum)
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(6, 3, 3), // 丢失3个分片（最大可恢复）
|
(6, 3, 1, "single_loss_6_3"), // Lose 1 shard with 6+3
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(8, 4, 3), // 丢失3个分片
|
(6, 3, 3, "triple_loss_6_3"), // Lose 3 shards (maximum)
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(8, 4, 4), // 丢失4个分片（最大可恢复）
|
(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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|
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let mut group = c.benchmark_group("error_recovery");
|
let mut group = c.benchmark_group("error_recovery_analysis");
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group.throughput(Throughput::Bytes(data_size as u64));
|
group.throughput(Throughput::Bytes(data_size as u64));
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group.sample_size(15);
|
group.sample_size(15);
|
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group.measurement_time(Duration::from_secs(8));
|
group.measurement_time(Duration::from_secs(10));
|
||||||
|
|
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for (data_shards, parity_shards, lost_shards) in configs {
|
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);
|
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());
|
|
||||||
|
|
||||||
// 检查此配置是否支持
|
|
||||||
match erasure.encode_data(&data) {
|
match erasure.encode_data(&data) {
|
||||||
Ok(encoded_shards) => {
|
Ok(encoded_shards) => {
|
||||||
|
let test_name = format!("{}+{}_{}", data_shards, parity_shards, scenario_name);
|
||||||
|
|
||||||
group.bench_with_input(
|
group.bench_with_input(
|
||||||
BenchmarkId::new("recovery", &test_name),
|
BenchmarkId::new("recovery", &test_name),
|
||||||
&(&encoded_shards, data_shards, parity_shards, lost_shards),
|
&(&encoded_shards, data_shards, parity_shards, loss_count),
|
||||||
|b, (shards, data_shards, parity_shards, lost_shards)| {
|
|b, (shards, data_shards, parity_shards, loss_count)| {
|
||||||
let erasure = Erasure::new(*data_shards, *parity_shards, data_size);
|
let erasure = Erasure::new(*data_shards, *parity_shards, data_size);
|
||||||
b.iter(|| {
|
b.iter(|| {
|
||||||
|
// Simulate specific number of shard losses
|
||||||
let mut shards_opt: Vec<Option<Vec<u8>>> = shards.iter().map(|shard| Some(shard.to_vec())).collect();
|
let mut shards_opt: Vec<Option<Vec<u8>>> = shards.iter().map(|shard| Some(shard.to_vec())).collect();
|
||||||
|
|
||||||
// 丢失指定数量的分片
|
// Lose the specified number of shards
|
||||||
for item in shards_opt.iter_mut().take(*lost_shards) {
|
for item in shards_opt.iter_mut().take(*loss_count) {
|
||||||
*item = None;
|
*item = None;
|
||||||
}
|
}
|
||||||
|
|
||||||
@@ -260,71 +261,57 @@ fn bench_error_recovery_performance(c: &mut Criterion) {
|
|||||||
);
|
);
|
||||||
}
|
}
|
||||||
Err(e) => {
|
Err(e) => {
|
||||||
println!("⚠️ 跳过错误恢复测试 {} - 配置不支持: {}", test_name, e);
|
println!("⚠️ Skipping recovery test {}: {}", scenario_name, e);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
group.finish();
|
group.finish();
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 内存效率测试
|
/// Memory efficiency analysis
|
||||||
fn bench_memory_efficiency(c: &mut Criterion) {
|
fn bench_memory_analysis(c: &mut Criterion) {
|
||||||
let data_shards = 4;
|
let data_sizes = vec![64 * 1024, 256 * 1024, 1024 * 1024]; // 64KB, 256KB, 1MB
|
||||||
let parity_shards = 2;
|
let config = (4, 2); // 4+2 configuration
|
||||||
let data_size = 1024 * 1024; // 1MB
|
|
||||||
|
|
||||||
let mut group = c.benchmark_group("memory_efficiency");
|
let mut group = c.benchmark_group("memory_analysis");
|
||||||
group.throughput(Throughput::Bytes(data_size as u64));
|
group.sample_size(15);
|
||||||
group.sample_size(10);
|
|
||||||
group.measurement_time(Duration::from_secs(8));
|
group.measurement_time(Duration::from_secs(8));
|
||||||
|
|
||||||
let test_name = format!("memory_pattern_{}", get_implementation_name());
|
for data_size in data_sizes {
|
||||||
|
let data = (0..data_size).map(|i| (i % 256) as u8).collect::<Vec<u8>>();
|
||||||
|
let size_name = format!("{}KB", data_size / 1024);
|
||||||
|
|
||||||
// 测试连续多次编码对内存的影响
|
group.throughput(Throughput::Bytes(data_size as u64));
|
||||||
group.bench_function(format!("{}_continuous", test_name), |b| {
|
|
||||||
let erasure = Erasure::new(data_shards, parity_shards, data_size);
|
// Test instance reuse vs new instance creation
|
||||||
b.iter(|| {
|
group.bench_with_input(BenchmarkId::new("reuse_instance", &size_name), &data, |b, data| {
|
||||||
for i in 0..10 {
|
let erasure = Erasure::new(config.0, config.1, data.len());
|
||||||
let data = vec![(i % 256) as u8; data_size];
|
b.iter(|| {
|
||||||
let shards = erasure.encode_data(black_box(&data)).unwrap();
|
let shards = erasure.encode_data(black_box(data)).unwrap();
|
||||||
black_box(shards);
|
black_box(shards);
|
||||||
}
|
});
|
||||||
});
|
});
|
||||||
});
|
|
||||||
|
|
||||||
// 测试大量小编码任务
|
group.bench_with_input(BenchmarkId::new("new_instance", &size_name), &data, |b, data| {
|
||||||
group.bench_function(format!("{}_small_chunks", test_name), |b| {
|
b.iter(|| {
|
||||||
let chunk_size = 1024; // 1KB chunks
|
let erasure = Erasure::new(config.0, config.1, data.len());
|
||||||
let erasure = Erasure::new(data_shards, parity_shards, chunk_size);
|
let shards = erasure.encode_data(black_box(data)).unwrap();
|
||||||
b.iter(|| {
|
|
||||||
for i in 0..1024 {
|
|
||||||
let data = vec![(i % 256) as u8; chunk_size];
|
|
||||||
let shards = erasure.encode_data(black_box(&data)).unwrap();
|
|
||||||
black_box(shards);
|
black_box(shards);
|
||||||
}
|
});
|
||||||
});
|
});
|
||||||
});
|
}
|
||||||
|
|
||||||
group.finish();
|
group.finish();
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 获取当前实现的名称
|
// Benchmark group configuration
|
||||||
fn get_implementation_name() -> &'static str {
|
|
||||||
#[cfg(feature = "reed-solomon-simd")]
|
|
||||||
return "hybrid";
|
|
||||||
|
|
||||||
#[cfg(not(feature = "reed-solomon-simd"))]
|
|
||||||
return "erasure";
|
|
||||||
}
|
|
||||||
|
|
||||||
criterion_group!(
|
criterion_group!(
|
||||||
benches,
|
benches,
|
||||||
bench_encode_comparison,
|
bench_encode_analysis,
|
||||||
bench_decode_comparison,
|
bench_decode_analysis,
|
||||||
bench_shard_size_sensitivity,
|
bench_shard_size_analysis,
|
||||||
bench_concurrent_load,
|
bench_concurrent_analysis,
|
||||||
bench_error_recovery_performance,
|
bench_error_recovery_analysis,
|
||||||
bench_memory_efficiency
|
bench_memory_analysis
|
||||||
);
|
);
|
||||||
|
|
||||||
criterion_main!(benches);
|
criterion_main!(benches);
|
||||||
|
|||||||
@@ -1,25 +1,23 @@
|
|||||||
//! Reed-Solomon erasure coding performance benchmarks.
|
//! Reed-Solomon SIMD erasure coding performance benchmarks.
|
||||||
//!
|
//!
|
||||||
//! This benchmark compares the performance of different Reed-Solomon implementations:
|
//! This benchmark tests the performance of the high-performance SIMD Reed-Solomon implementation.
|
||||||
//! - SIMD mode: High-performance reed-solomon-simd implementation
|
|
||||||
//! - `reed-solomon-simd` feature: SIMD mode with optimized performance
|
|
||||||
//!
|
//!
|
||||||
//! ## Running Benchmarks
|
//! ## Running Benchmarks
|
||||||
//!
|
//!
|
||||||
//! ```bash
|
//! ```bash
|
||||||
//! # 运行所有基准测试
|
//! # Run all benchmarks
|
||||||
//! cargo bench
|
//! cargo bench
|
||||||
//!
|
//!
|
||||||
//! # 运行特定的基准测试
|
//! # Run specific benchmark
|
||||||
//! cargo bench --bench erasure_benchmark
|
//! cargo bench --bench erasure_benchmark
|
||||||
//!
|
//!
|
||||||
//! # 生成HTML报告
|
//! # Generate HTML report
|
||||||
//! cargo bench --bench erasure_benchmark -- --output-format html
|
//! cargo bench --bench erasure_benchmark -- --output-format html
|
||||||
//!
|
//!
|
||||||
//! # 只测试编码性能
|
//! # Test encoding performance only
|
||||||
//! cargo bench encode
|
//! cargo bench encode
|
||||||
//!
|
//!
|
||||||
//! # 只测试解码性能
|
//! # Test decoding performance only
|
||||||
//! cargo bench decode
|
//! cargo bench decode
|
||||||
//! ```
|
//! ```
|
||||||
//!
|
//!
|
||||||
@@ -29,24 +27,24 @@
|
|||||||
//! - Different data sizes: 1KB, 64KB, 1MB, 16MB
|
//! - Different data sizes: 1KB, 64KB, 1MB, 16MB
|
||||||
//! - Different erasure coding configurations: (4,2), (6,3), (8,4)
|
//! - Different erasure coding configurations: (4,2), (6,3), (8,4)
|
||||||
//! - Both encoding and decoding operations
|
//! - Both encoding and decoding operations
|
||||||
//! - Small vs large shard scenarios for SIMD optimization
|
//! - SIMD optimization for different shard sizes
|
||||||
|
|
||||||
use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
|
use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
|
||||||
use ecstore::erasure_coding::{Erasure, calc_shard_size};
|
use ecstore::erasure_coding::{Erasure, calc_shard_size};
|
||||||
use std::time::Duration;
|
use std::time::Duration;
|
||||||
|
|
||||||
/// 基准测试配置结构体
|
/// Benchmark configuration structure
|
||||||
#[derive(Clone, Debug)]
|
#[derive(Clone, Debug)]
|
||||||
struct BenchConfig {
|
struct BenchConfig {
|
||||||
/// 数据分片数量
|
/// Number of data shards
|
||||||
data_shards: usize,
|
data_shards: usize,
|
||||||
/// 奇偶校验分片数量
|
/// Number of parity shards
|
||||||
parity_shards: usize,
|
parity_shards: usize,
|
||||||
/// 测试数据大小（字节）
|
/// Test data size (bytes)
|
||||||
data_size: usize,
|
data_size: usize,
|
||||||
/// 块大小（字节）
|
/// Block size (bytes)
|
||||||
block_size: usize,
|
block_size: usize,
|
||||||
/// 配置名称
|
/// Configuration name
|
||||||
name: String,
|
name: String,
|
||||||
}
|
}
|
||||||
|
|
||||||
@@ -62,27 +60,27 @@ impl BenchConfig {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 生成测试数据
|
/// Generate test data
|
||||||
fn generate_test_data(size: usize) -> Vec<u8> {
|
fn generate_test_data(size: usize) -> Vec<u8> {
|
||||||
(0..size).map(|i| (i % 256) as u8).collect()
|
(0..size).map(|i| (i % 256) as u8).collect()
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 基准测试: 编码性能对比
|
/// Benchmark: Encoding performance
|
||||||
fn bench_encode_performance(c: &mut Criterion) {
|
fn bench_encode_performance(c: &mut Criterion) {
|
||||||
let configs = vec![
|
let configs = vec![
|
||||||
// 小数据量测试 - 1KB
|
// Small data tests - 1KB
|
||||||
BenchConfig::new(4, 2, 1024, 1024),
|
BenchConfig::new(4, 2, 1024, 1024),
|
||||||
BenchConfig::new(6, 3, 1024, 1024),
|
BenchConfig::new(6, 3, 1024, 1024),
|
||||||
BenchConfig::new(8, 4, 1024, 1024),
|
BenchConfig::new(8, 4, 1024, 1024),
|
||||||
// 中等数据量测试 - 64KB
|
// Medium data tests - 64KB
|
||||||
BenchConfig::new(4, 2, 64 * 1024, 64 * 1024),
|
BenchConfig::new(4, 2, 64 * 1024, 64 * 1024),
|
||||||
BenchConfig::new(6, 3, 64 * 1024, 64 * 1024),
|
BenchConfig::new(6, 3, 64 * 1024, 64 * 1024),
|
||||||
BenchConfig::new(8, 4, 64 * 1024, 64 * 1024),
|
BenchConfig::new(8, 4, 64 * 1024, 64 * 1024),
|
||||||
// 大数据量测试 - 1MB
|
// Large data tests - 1MB
|
||||||
BenchConfig::new(4, 2, 1024 * 1024, 1024 * 1024),
|
BenchConfig::new(4, 2, 1024 * 1024, 1024 * 1024),
|
||||||
BenchConfig::new(6, 3, 1024 * 1024, 1024 * 1024),
|
BenchConfig::new(6, 3, 1024 * 1024, 1024 * 1024),
|
||||||
BenchConfig::new(8, 4, 1024 * 1024, 1024 * 1024),
|
BenchConfig::new(8, 4, 1024 * 1024, 1024 * 1024),
|
||||||
// 超大数据量测试 - 16MB
|
// Extra large data tests - 16MB
|
||||||
BenchConfig::new(4, 2, 16 * 1024 * 1024, 16 * 1024 * 1024),
|
BenchConfig::new(4, 2, 16 * 1024 * 1024, 16 * 1024 * 1024),
|
||||||
BenchConfig::new(6, 3, 16 * 1024 * 1024, 16 * 1024 * 1024),
|
BenchConfig::new(6, 3, 16 * 1024 * 1024, 16 * 1024 * 1024),
|
||||||
];
|
];
|
||||||
@@ -90,13 +88,13 @@ fn bench_encode_performance(c: &mut Criterion) {
|
|||||||
for config in configs {
|
for config in configs {
|
||||||
let data = generate_test_data(config.data_size);
|
let data = generate_test_data(config.data_size);
|
||||||
|
|
||||||
// 测试当前默认实现（通常是SIMD）
|
// Test SIMD encoding performance
|
||||||
let mut group = c.benchmark_group("encode_current");
|
let mut group = c.benchmark_group("encode_simd");
|
||||||
group.throughput(Throughput::Bytes(config.data_size as u64));
|
group.throughput(Throughput::Bytes(config.data_size as u64));
|
||||||
group.sample_size(10);
|
group.sample_size(10);
|
||||||
group.measurement_time(Duration::from_secs(5));
|
group.measurement_time(Duration::from_secs(5));
|
||||||
|
|
||||||
group.bench_with_input(BenchmarkId::new("current_impl", &config.name), &(&data, &config), |b, (data, config)| {
|
group.bench_with_input(BenchmarkId::new("simd_impl", &config.name), &(&data, &config), |b, (data, config)| {
|
||||||
let erasure = Erasure::new(config.data_shards, config.parity_shards, config.block_size);
|
let erasure = Erasure::new(config.data_shards, config.parity_shards, config.block_size);
|
||||||
b.iter(|| {
|
b.iter(|| {
|
||||||
let shards = erasure.encode_data(black_box(data)).unwrap();
|
let shards = erasure.encode_data(black_box(data)).unwrap();
|
||||||
@@ -105,99 +103,55 @@ fn bench_encode_performance(c: &mut Criterion) {
|
|||||||
});
|
});
|
||||||
group.finish();
|
group.finish();
|
||||||
|
|
||||||
// 如果SIMD feature启用，测试专用的erasure实现对比
|
// Test direct SIMD implementation for large shards (>= 512 bytes)
|
||||||
#[cfg(feature = "reed-solomon-simd")]
|
let shard_size = calc_shard_size(config.data_size, config.data_shards);
|
||||||
{
|
if shard_size >= 512 {
|
||||||
use ecstore::erasure_coding::ReedSolomonEncoder;
|
let mut simd_group = c.benchmark_group("encode_simd_direct");
|
||||||
|
simd_group.throughput(Throughput::Bytes(config.data_size as u64));
|
||||||
|
simd_group.sample_size(10);
|
||||||
|
simd_group.measurement_time(Duration::from_secs(5));
|
||||||
|
|
||||||
let mut erasure_group = c.benchmark_group("encode_erasure_only");
|
simd_group.bench_with_input(BenchmarkId::new("simd_direct", &config.name), &(&data, &config), |b, (data, config)| {
|
||||||
erasure_group.throughput(Throughput::Bytes(config.data_size as u64));
|
b.iter(|| {
|
||||||
erasure_group.sample_size(10);
|
// Direct SIMD implementation
|
||||||
erasure_group.measurement_time(Duration::from_secs(5));
|
let per_shard_size = calc_shard_size(data.len(), config.data_shards);
|
||||||
|
match reed_solomon_simd::ReedSolomonEncoder::new(config.data_shards, config.parity_shards, per_shard_size) {
|
||||||
|
Ok(mut encoder) => {
|
||||||
|
// Create properly sized buffer and fill with data
|
||||||
|
let mut buffer = vec![0u8; per_shard_size * config.data_shards];
|
||||||
|
let copy_len = data.len().min(buffer.len());
|
||||||
|
buffer[..copy_len].copy_from_slice(&data[..copy_len]);
|
||||||
|
|
||||||
erasure_group.bench_with_input(
|
// Add data shards with correct shard size
|
||||||
BenchmarkId::new("erasure_impl", &config.name),
|
for chunk in buffer.chunks_exact(per_shard_size) {
|
||||||
&(&data, &config),
|
encoder.add_original_shard(black_box(chunk)).unwrap();
|
||||||
|b, (data, config)| {
|
|
||||||
let encoder = ReedSolomonEncoder::new(config.data_shards, config.parity_shards).unwrap();
|
|
||||||
b.iter(|| {
|
|
||||||
// 创建编码所需的数据结构
|
|
||||||
let per_shard_size = calc_shard_size(data.len(), config.data_shards);
|
|
||||||
let total_size = per_shard_size * (config.data_shards + config.parity_shards);
|
|
||||||
let mut buffer = vec![0u8; total_size];
|
|
||||||
buffer[..data.len()].copy_from_slice(data);
|
|
||||||
|
|
||||||
let slices: smallvec::SmallVec<[&mut [u8]; 16]> = buffer.chunks_exact_mut(per_shard_size).collect();
|
|
||||||
|
|
||||||
encoder.encode(black_box(slices)).unwrap();
|
|
||||||
black_box(&buffer);
|
|
||||||
});
|
|
||||||
},
|
|
||||||
);
|
|
||||||
erasure_group.finish();
|
|
||||||
}
|
|
||||||
|
|
||||||
// 如果使用SIMD feature，测试直接SIMD实现对比
|
|
||||||
#[cfg(feature = "reed-solomon-simd")]
|
|
||||||
{
|
|
||||||
// 只对大shard测试SIMD（小于512字节的shard SIMD性能不佳）
|
|
||||||
let shard_size = calc_shard_size(config.data_size, config.data_shards);
|
|
||||||
if shard_size >= 512 {
|
|
||||||
let mut simd_group = c.benchmark_group("encode_simd_direct");
|
|
||||||
simd_group.throughput(Throughput::Bytes(config.data_size as u64));
|
|
||||||
simd_group.sample_size(10);
|
|
||||||
simd_group.measurement_time(Duration::from_secs(5));
|
|
||||||
|
|
||||||
simd_group.bench_with_input(
|
|
||||||
BenchmarkId::new("simd_impl", &config.name),
|
|
||||||
&(&data, &config),
|
|
||||||
|b, (data, config)| {
|
|
||||||
b.iter(|| {
|
|
||||||
// 直接使用SIMD实现
|
|
||||||
let per_shard_size = calc_shard_size(data.len(), config.data_shards);
|
|
||||||
match reed_solomon_simd::ReedSolomonEncoder::new(
|
|
||||||
config.data_shards,
|
|
||||||
config.parity_shards,
|
|
||||||
per_shard_size,
|
|
||||||
) {
|
|
||||||
Ok(mut encoder) => {
|
|
||||||
// 创建正确大小的缓冲区，并填充数据
|
|
||||||
let mut buffer = vec![0u8; per_shard_size * config.data_shards];
|
|
||||||
let copy_len = data.len().min(buffer.len());
|
|
||||||
buffer[..copy_len].copy_from_slice(&data[..copy_len]);
|
|
||||||
|
|
||||||
// 按正确的分片大小添加数据分片
|
|
||||||
for chunk in buffer.chunks_exact(per_shard_size) {
|
|
||||||
encoder.add_original_shard(black_box(chunk)).unwrap();
|
|
||||||
}
|
|
||||||
|
|
||||||
let result = encoder.encode().unwrap();
|
|
||||||
black_box(result);
|
|
||||||
}
|
|
||||||
Err(_) => {
|
|
||||||
// SIMD不支持此配置，跳过
|
|
||||||
black_box(());
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
});
|
|
||||||
},
|
let result = encoder.encode().unwrap();
|
||||||
);
|
black_box(result);
|
||||||
simd_group.finish();
|
}
|
||||||
}
|
Err(_) => {
|
||||||
|
// SIMD doesn't support this configuration, skip
|
||||||
|
black_box(());
|
||||||
|
}
|
||||||
|
}
|
||||||
|
});
|
||||||
|
});
|
||||||
|
simd_group.finish();
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 基准测试: 解码性能对比
|
/// Benchmark: Decoding performance
|
||||||
fn bench_decode_performance(c: &mut Criterion) {
|
fn bench_decode_performance(c: &mut Criterion) {
|
||||||
let configs = vec![
|
let configs = vec![
|
||||||
// 中等数据量测试 - 64KB
|
// Medium data tests - 64KB
|
||||||
BenchConfig::new(4, 2, 64 * 1024, 64 * 1024),
|
BenchConfig::new(4, 2, 64 * 1024, 64 * 1024),
|
||||||
BenchConfig::new(6, 3, 64 * 1024, 64 * 1024),
|
BenchConfig::new(6, 3, 64 * 1024, 64 * 1024),
|
||||||
// 大数据量测试 - 1MB
|
// Large data tests - 1MB
|
||||||
BenchConfig::new(4, 2, 1024 * 1024, 1024 * 1024),
|
BenchConfig::new(4, 2, 1024 * 1024, 1024 * 1024),
|
||||||
BenchConfig::new(6, 3, 1024 * 1024, 1024 * 1024),
|
BenchConfig::new(6, 3, 1024 * 1024, 1024 * 1024),
|
||||||
// 超大数据量测试 - 16MB
|
// Extra large data tests - 16MB
|
||||||
BenchConfig::new(4, 2, 16 * 1024 * 1024, 16 * 1024 * 1024),
|
BenchConfig::new(4, 2, 16 * 1024 * 1024, 16 * 1024 * 1024),
|
||||||
];
|
];
|
||||||
|
|
||||||
@@ -205,25 +159,25 @@ fn bench_decode_performance(c: &mut Criterion) {
|
|||||||
let data = generate_test_data(config.data_size);
|
let data = generate_test_data(config.data_size);
|
||||||
let erasure = Erasure::new(config.data_shards, config.parity_shards, config.block_size);
|
let erasure = Erasure::new(config.data_shards, config.parity_shards, config.block_size);
|
||||||
|
|
||||||
// 预先编码数据
|
// Pre-encode data
|
||||||
let encoded_shards = erasure.encode_data(&data).unwrap();
|
let encoded_shards = erasure.encode_data(&data).unwrap();
|
||||||
|
|
||||||
// 测试当前默认实现的解码性能
|
// Test SIMD decoding performance
|
||||||
let mut group = c.benchmark_group("decode_current");
|
let mut group = c.benchmark_group("decode_simd");
|
||||||
group.throughput(Throughput::Bytes(config.data_size as u64));
|
group.throughput(Throughput::Bytes(config.data_size as u64));
|
||||||
group.sample_size(10);
|
group.sample_size(10);
|
||||||
group.measurement_time(Duration::from_secs(5));
|
group.measurement_time(Duration::from_secs(5));
|
||||||
|
|
||||||
group.bench_with_input(
|
group.bench_with_input(
|
||||||
BenchmarkId::new("current_impl", &config.name),
|
BenchmarkId::new("simd_impl", &config.name),
|
||||||
&(&encoded_shards, &config),
|
&(&encoded_shards, &config),
|
||||||
|b, (shards, config)| {
|
|b, (shards, config)| {
|
||||||
let erasure = Erasure::new(config.data_shards, config.parity_shards, config.block_size);
|
let erasure = Erasure::new(config.data_shards, config.parity_shards, config.block_size);
|
||||||
b.iter(|| {
|
b.iter(|| {
|
||||||
// 模拟数据丢失 - 丢失一个数据分片和一个奇偶分片
|
// Simulate data loss - lose one data shard and one parity shard
|
||||||
let mut shards_opt: Vec<Option<Vec<u8>>> = shards.iter().map(|shard| Some(shard.to_vec())).collect();
|
let mut shards_opt: Vec<Option<Vec<u8>>> = shards.iter().map(|shard| Some(shard.to_vec())).collect();
|
||||||
|
|
||||||
// 丢失最后一个数据分片和第一个奇偶分片
|
// Lose last data shard and first parity shard
|
||||||
shards_opt[config.data_shards - 1] = None;
|
shards_opt[config.data_shards - 1] = None;
|
||||||
shards_opt[config.data_shards] = None;
|
shards_opt[config.data_shards] = None;
|
||||||
|
|
||||||
@@ -234,58 +188,52 @@ fn bench_decode_performance(c: &mut Criterion) {
|
|||||||
);
|
);
|
||||||
group.finish();
|
group.finish();
|
||||||
|
|
||||||
// 如果使用混合模式（默认），测试SIMD解码性能
|
// Test direct SIMD decoding for large shards
|
||||||
|
let shard_size = calc_shard_size(config.data_size, config.data_shards);
|
||||||
|
if shard_size >= 512 {
|
||||||
|
let mut simd_group = c.benchmark_group("decode_simd_direct");
|
||||||
|
simd_group.throughput(Throughput::Bytes(config.data_size as u64));
|
||||||
|
simd_group.sample_size(10);
|
||||||
|
simd_group.measurement_time(Duration::from_secs(5));
|
||||||
|
|
||||||
{
|
simd_group.bench_with_input(
|
||||||
let shard_size = calc_shard_size(config.data_size, config.data_shards);
|
BenchmarkId::new("simd_direct", &config.name),
|
||||||
if shard_size >= 512 {
|
&(&encoded_shards, &config),
|
||||||
let mut simd_group = c.benchmark_group("decode_simd_direct");
|
|b, (shards, config)| {
|
||||||
simd_group.throughput(Throughput::Bytes(config.data_size as u64));
|
b.iter(|| {
|
||||||
simd_group.sample_size(10);
|
let per_shard_size = calc_shard_size(config.data_size, config.data_shards);
|
||||||
simd_group.measurement_time(Duration::from_secs(5));
|
match reed_solomon_simd::ReedSolomonDecoder::new(config.data_shards, config.parity_shards, per_shard_size)
|
||||||
|
{
|
||||||
simd_group.bench_with_input(
|
Ok(mut decoder) => {
|
||||||
BenchmarkId::new("simd_impl", &config.name),
|
// Add available shards (except lost ones)
|
||||||
&(&encoded_shards, &config),
|
for (i, shard) in shards.iter().enumerate() {
|
||||||
|b, (shards, config)| {
|
if i != config.data_shards - 1 && i != config.data_shards {
|
||||||
b.iter(|| {
|
if i < config.data_shards {
|
||||||
let per_shard_size = calc_shard_size(config.data_size, config.data_shards);
|
decoder.add_original_shard(i, black_box(shard)).unwrap();
|
||||||
match reed_solomon_simd::ReedSolomonDecoder::new(
|
} else {
|
||||||
config.data_shards,
|
let recovery_idx = i - config.data_shards;
|
||||||
config.parity_shards,
|
decoder.add_recovery_shard(recovery_idx, black_box(shard)).unwrap();
|
||||||
per_shard_size,
|
|
||||||
) {
|
|
||||||
Ok(mut decoder) => {
|
|
||||||
// 添加可用的分片（除了丢失的）
|
|
||||||
for (i, shard) in shards.iter().enumerate() {
|
|
||||||
if i != config.data_shards - 1 && i != config.data_shards {
|
|
||||||
if i < config.data_shards {
|
|
||||||
decoder.add_original_shard(i, black_box(shard)).unwrap();
|
|
||||||
} else {
|
|
||||||
let recovery_idx = i - config.data_shards;
|
|
||||||
decoder.add_recovery_shard(recovery_idx, black_box(shard)).unwrap();
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
}
|
||||||
|
|
||||||
let result = decoder.decode().unwrap();
|
let result = decoder.decode().unwrap();
|
||||||
black_box(result);
|
black_box(result);
|
||||||
}
|
|
||||||
Err(_) => {
|
|
||||||
// SIMD不支持此配置，跳过
|
|
||||||
black_box(());
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
});
|
Err(_) => {
|
||||||
},
|
// SIMD doesn't support this configuration, skip
|
||||||
);
|
black_box(());
|
||||||
simd_group.finish();
|
}
|
||||||
}
|
}
|
||||||
|
});
|
||||||
|
},
|
||||||
|
);
|
||||||
|
simd_group.finish();
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 基准测试: 不同分片大小对性能的影响
|
/// Benchmark: Impact of different shard sizes on performance
|
||||||
fn bench_shard_size_impact(c: &mut Criterion) {
|
fn bench_shard_size_impact(c: &mut Criterion) {
|
||||||
let shard_sizes = vec![64, 128, 256, 512, 1024, 2048, 4096, 8192];
|
let shard_sizes = vec![64, 128, 256, 512, 1024, 2048, 4096, 8192];
|
||||||
let data_shards = 4;
|
let data_shards = 4;
|
||||||
@@ -301,8 +249,8 @@ fn bench_shard_size_impact(c: &mut Criterion) {
|
|||||||
|
|
||||||
group.throughput(Throughput::Bytes(total_data_size as u64));
|
group.throughput(Throughput::Bytes(total_data_size as u64));
|
||||||
|
|
||||||
// 测试当前实现
|
// Test SIMD implementation
|
||||||
group.bench_with_input(BenchmarkId::new("current", format!("shard_{}B", shard_size)), &data, |b, data| {
|
group.bench_with_input(BenchmarkId::new("simd", format!("shard_{}B", shard_size)), &data, |b, data| {
|
||||||
let erasure = Erasure::new(data_shards, parity_shards, total_data_size);
|
let erasure = Erasure::new(data_shards, parity_shards, total_data_size);
|
||||||
b.iter(|| {
|
b.iter(|| {
|
||||||
let shards = erasure.encode_data(black_box(data)).unwrap();
|
let shards = erasure.encode_data(black_box(data)).unwrap();
|
||||||
@@ -313,19 +261,19 @@ fn bench_shard_size_impact(c: &mut Criterion) {
|
|||||||
group.finish();
|
group.finish();
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 基准测试: 编码配置对性能的影响
|
/// Benchmark: Impact of coding configurations on performance
|
||||||
fn bench_coding_configurations(c: &mut Criterion) {
|
fn bench_coding_configurations(c: &mut Criterion) {
|
||||||
let configs = vec![
|
let configs = vec![
|
||||||
(2, 1), // 最小冗余
|
(2, 1), // Minimal redundancy
|
||||||
(3, 2), // 中等冗余
|
(3, 2), // Medium redundancy
|
||||||
(4, 2), // 常用配置
|
(4, 2), // Common configuration
|
||||||
(6, 3), // 50%冗余
|
(6, 3), // 50% redundancy
|
||||||
(8, 4), // 50%冗余，更多分片
|
(8, 4), // 50% redundancy, more shards
|
||||||
(10, 5), // 50%冗余，大量分片
|
(10, 5), // 50% redundancy, many shards
|
||||||
(12, 6), // 50%冗余，更大量分片
|
(12, 6), // 50% redundancy, very many shards
|
||||||
];
|
];
|
||||||
|
|
||||||
let data_size = 1024 * 1024; // 1MB测试数据
|
let data_size = 1024 * 1024; // 1MB test data
|
||||||
let data = generate_test_data(data_size);
|
let data = generate_test_data(data_size);
|
||||||
|
|
||||||
let mut group = c.benchmark_group("coding_configurations");
|
let mut group = c.benchmark_group("coding_configurations");
|
||||||
@@ -347,17 +295,17 @@ fn bench_coding_configurations(c: &mut Criterion) {
|
|||||||
group.finish();
|
group.finish();
|
||||||
}
|
}
|
||||||
|
|
||||||
/// 基准测试: 内存使用模式
|
/// Benchmark: Memory usage patterns
|
||||||
fn bench_memory_patterns(c: &mut Criterion) {
|
fn bench_memory_patterns(c: &mut Criterion) {
|
||||||
let data_shards = 4;
|
let data_shards = 4;
|
||||||
let parity_shards = 2;
|
let parity_shards = 2;
|
||||||
let block_size = 1024 * 1024; // 1MB块
|
let block_size = 1024 * 1024; // 1MB block
|
||||||
|
|
||||||
let mut group = c.benchmark_group("memory_patterns");
|
let mut group = c.benchmark_group("memory_patterns");
|
||||||
group.sample_size(10);
|
group.sample_size(10);
|
||||||
group.measurement_time(Duration::from_secs(5));
|
group.measurement_time(Duration::from_secs(5));
|
||||||
|
|
||||||
// 测试重复使用同一个Erasure实例
|
// Test reusing the same Erasure instance
|
||||||
group.bench_function("reuse_erasure_instance", |b| {
|
group.bench_function("reuse_erasure_instance", |b| {
|
||||||
let erasure = Erasure::new(data_shards, parity_shards, block_size);
|
let erasure = Erasure::new(data_shards, parity_shards, block_size);
|
||||||
let data = generate_test_data(block_size);
|
let data = generate_test_data(block_size);
|
||||||
@@ -368,7 +316,7 @@ fn bench_memory_patterns(c: &mut Criterion) {
|
|||||||
});
|
});
|
||||||
});
|
});
|
||||||
|
|
||||||
// 测试每次创建新的Erasure实例
|
// Test creating new Erasure instance each time
|
||||||
group.bench_function("new_erasure_instance", |b| {
|
group.bench_function("new_erasure_instance", |b| {
|
||||||
let data = generate_test_data(block_size);
|
let data = generate_test_data(block_size);
|
||||||
|
|
||||||
@@ -382,7 +330,7 @@ fn bench_memory_patterns(c: &mut Criterion) {
|
|||||||
group.finish();
|
group.finish();
|
||||||
}
|
}
|
||||||
|
|
||||||
// 基准测试组配置
|
// Benchmark group configuration
|
||||||
criterion_group!(
|
criterion_group!(
|
||||||
benches,
|
benches,
|
||||||
bench_encode_performance,
|
bench_encode_performance,
|
||||||
|
|||||||
Reference in New Issue
Block a user