597 lines
22 KiB
Rust
597 lines
22 KiB
Rust
//! Edge-aware training performance benchmarks
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//!
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//! Benchmarks validate performance targets across all edge platforms:
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//! - ARM NEON: Target 2.5x speedup over scalar
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//! - RISC-V RVV: Target 4.8x speedup with vector extensions
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//! - WASM SIMD: Target 1.8x speedup in browser
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//! - Mobile GPU: Target 4.0x speedup with compute shaders
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//! - IoT devices: Target 10x power efficiency improvement
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use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId};
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use rustytorch::revolutionary::*;
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use std::collections::HashMap;
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use std::time::{Duration, Instant, SystemTime};
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/// Benchmark ARM NEON optimization performance
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fn benchmark_arm_neon_optimization(c: &mut Criterion) {
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let mut group = c.benchmark_group("ARM_NEON_Optimization");
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let test_cases = vec![
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("Cortex-A", ArmArchitecture::CortexA, true),
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("Apple_Silicon", ArmArchitecture::AppleSilicon, true),
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("Cortex-A_No_NEON", ArmArchitecture::CortexA, false),
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];
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for (name, arch, enable_neon) in test_cases {
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group.bench_with_input(BenchmarkId::new("optimization", name), &(arch, enable_neon),
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|b, (arch, neon)| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let arm_opts = ArmOptimizations {
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enable_neon: *neon,
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memory_prefetch: true,
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cache_optimization: true,
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big_little_scheduling: true,
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target_arch: *arch,
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};
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optimizer.configure_arm(arm_opts);
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black_box(optimizer.optimize_for_target(EdgeTarget::ARM).unwrap())
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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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/// Benchmark RISC-V vector extension performance
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fn benchmark_riscv_vector_optimization(c: &mut Criterion) {
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let mut group = c.benchmark_group("RISCV_Vector_Optimization");
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let vector_lengths = vec![
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("VLEN128", RiscVVectorLength::VLEN128),
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("VLEN256", RiscVVectorLength::VLEN256),
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("VLEN512", RiscVVectorLength::VLEN512),
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("Variable", RiscVVectorLength::Variable),
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];
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for (name, vlen) in vector_lengths {
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group.bench_with_input(BenchmarkId::new("vector_length", name), &vlen,
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|b, vlen| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let riscv_opts = RiscVOptimizations {
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enable_rvv: true,
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vector_length: *vlen,
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custom_instructions: vec!["custom_matmul".to_string(), "custom_conv".to_string()],
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memory_model: RiscVMemoryModel::TSO,
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target_variant: RiscVVariant::Vector,
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};
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optimizer.configure_riscv(riscv_opts);
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black_box(optimizer.optimize_for_target(EdgeTarget::RISCV).unwrap())
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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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/// Benchmark WebAssembly SIMD performance
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fn benchmark_wasm_simd_optimization(c: &mut Criterion) {
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let mut group = c.benchmark_group("WASM_SIMD_Optimization");
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let runtime_configs = vec![
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("Browser_SIMD", WasmRuntime::Browser, true, true),
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("Browser_No_SIMD", WasmRuntime::Browser, false, false),
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("Wasmtime_SIMD", WasmRuntime::Wasmtime, true, true),
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("NodeJS_SIMD", WasmRuntime::NodeJS, true, true),
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];
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for (name, runtime, simd, threads) in runtime_configs {
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group.bench_with_input(BenchmarkId::new("runtime", name), &(runtime, simd, threads),
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|b, (runtime, simd, threads)| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let wasm_opts = WasmOptimizations {
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enable_simd: *simd,
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enable_threads: *threads,
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memory_growth: WasmMemoryGrowth::Dynamic { max_pages: 2048 },
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target_runtime: *runtime,
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bulk_memory: true,
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};
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optimizer.configure_wasm(wasm_opts);
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black_box(optimizer.optimize_for_target(EdgeTarget::WASM).unwrap())
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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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/// Benchmark Mobile GPU optimization across vendors
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fn benchmark_mobile_gpu_optimization(c: &mut Criterion) {
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let mut group = c.benchmark_group("Mobile_GPU_Optimization");
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let gpu_vendors = vec![
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("Mali", MobileGpuVendor::Mali),
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("Adreno", MobileGpuVendor::Adreno),
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("PowerVR", MobileGpuVendor::PowerVR),
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("Apple_GPU", MobileGpuVendor::Apple),
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("Intel_GPU", MobileGpuVendor::Intel),
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];
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for (name, vendor) in gpu_vendors {
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group.bench_with_input(BenchmarkId::new("gpu_vendor", name), &vendor,
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|b, vendor| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let mobile_gpu_opts = MobileGpuOptimizations {
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gpu_vendor: *vendor,
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compute_shaders: true,
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tile_based_rendering: true,
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bandwidth_optimization: true,
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power_efficiency: true,
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};
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optimizer.configure_mobile_gpu(mobile_gpu_opts);
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black_box(optimizer.optimize_for_target(EdgeTarget::MobileGPU).unwrap())
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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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/// Benchmark IoT ultra-low power optimization
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fn benchmark_iot_optimization(c: &mut Criterion) {
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let mut group = c.benchmark_group("IoT_Ultra_Low_Power");
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let iot_platforms = vec![
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("ESP32", IoTPlatform::ESP32),
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("STM32", IoTPlatform::STM32),
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("Arduino", IoTPlatform::Arduino),
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("RaspberryPi_Pico", IoTPlatform::RaspberryPiPico),
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];
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for (name, platform) in iot_platforms {
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group.bench_with_input(BenchmarkId::new("platform", name), &platform,
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|b, platform| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let iot_opts = IoTOptimizations {
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ultra_low_power: true,
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minimal_memory: true,
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wake_on_inference: true,
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mesh_networking: true,
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target_platform: *platform,
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};
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optimizer.configure_iot(iot_opts);
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black_box(optimizer.optimize_for_target(EdgeTarget::IoT).unwrap())
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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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/// Benchmark federated device selection at scale
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fn benchmark_federated_device_selection(c: &mut Criterion) {
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let mut group = c.benchmark_group("Federated_Device_Selection");
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let device_scales = vec![100, 1_000, 10_000, 50_000];
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let selection_strategies = vec![
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("Random", SelectionStrategy::Random),
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("BatteryAware", SelectionStrategy::BatteryAware),
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("NetworkAware", SelectionStrategy::NetworkAware),
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("PerformanceBased", SelectionStrategy::PerformanceBased),
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("Hybrid", SelectionStrategy::Hybrid),
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("Intelligent", SelectionStrategy::Intelligent),
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];
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for device_count in device_scales {
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for (strategy_name, strategy) in &selection_strategies {
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let benchmark_name = format!("{}_{}", strategy_name, device_count);
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group.bench_with_input(
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BenchmarkId::new("device_selection", &benchmark_name),
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&(device_count, *strategy),
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|b, (count, strat)| {
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// Pre-create coordinator with devices
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let rt = tokio::runtime::Runtime::new().unwrap();
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let (coordinator, _sender) = FederatedCoordinator::new(
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"bench-coordinator".to_string(),
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*strat,
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GradientCompression {
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algorithm: CompressionAlgorithm::TopK,
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compression_ratio: 0.1,
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error_correction: true,
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adaptive_compression: true,
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},
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AggregationStrategy {
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algorithm: AggregationAlgorithm::FedAvg,
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weighting: WeightingScheme::Adaptive,
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byzantine_tolerance: ByzantineTolerance {
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enabled: false,
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max_byzantine_fraction: 0.0,
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detection_algorithm: ByzantineDetection::None,
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},
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differential_privacy: None,
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},
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);
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rt.block_on(async {
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for i in 0..*count {
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let device = create_benchmark_device(&format!("device-{:06}", i));
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coordinator.register_device(device).await.unwrap();
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}
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});
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let selection_criteria = SelectionCriteria {
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min_battery_level: 0.3,
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min_bandwidth_mbps: 5.0,
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max_latency_ms: 200,
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required_availability_minutes: 30,
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min_data_quality: 0.7,
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};
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b.to_async(&rt).iter(|| async {
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let target_count = (*count / 10).max(10); // Select 10% of devices
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black_box(coordinator.select_devices(target_count as u32, selection_criteria.clone()).await.unwrap())
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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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/// Benchmark gradient aggregation performance
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fn benchmark_gradient_aggregation(c: &mut Criterion) {
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let mut group = c.benchmark_group("Gradient_Aggregation");
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let device_counts = vec![10, 100, 1_000, 10_000];
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let aggregation_algorithms = vec![
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("FedAvg", AggregationAlgorithm::FedAvg),
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("FedProx", AggregationAlgorithm::FedProx),
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("SCAFFOLD", AggregationAlgorithm::SCAFFOLD),
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("FedAdam", AggregationAlgorithm::FedAdam),
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];
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for device_count in device_counts {
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for (algo_name, algorithm) in &aggregation_algorithms {
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let benchmark_name = format!("{}_{}_devices", algo_name, device_count);
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group.bench_with_input(
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BenchmarkId::new("aggregation", &benchmark_name),
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&(device_count, *algorithm),
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|b, (count, algo)| {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let (coordinator, _sender) = FederatedCoordinator::new(
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"bench-coordinator".to_string(),
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SelectionStrategy::Random,
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GradientCompression {
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algorithm: CompressionAlgorithm::TopK,
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compression_ratio: 0.1,
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error_correction: false,
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adaptive_compression: false,
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},
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AggregationStrategy {
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algorithm: *algo,
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weighting: WeightingScheme::Equal,
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byzantine_tolerance: ByzantineTolerance {
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enabled: false,
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max_byzantine_fraction: 0.0,
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detection_algorithm: ByzantineDetection::None,
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},
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differential_privacy: None,
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},
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);
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// Create dummy gradient data
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let mut device_gradients = HashMap::new();
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for i in 0..*count {
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let device_id = format!("device-{:06}", i);
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device_gradients.insert(device_id, vec![0u8; 1024]); // 1KB per device
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}
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let round_id = 123456789u64;
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b.to_async(&rt).iter(|| async {
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black_box(coordinator.aggregate_gradients(round_id, device_gradients.clone()).await.unwrap())
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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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/// Benchmark end-to-end training round performance
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fn benchmark_training_round_e2e(c: &mut Criterion) {
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let mut group = c.benchmark_group("Training_Round_E2E");
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group.measurement_time(Duration::from_secs(60)); // Longer measurement for E2E
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let device_counts = vec![100, 1_000, 5_000];
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for device_count in device_counts {
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group.bench_with_input(
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BenchmarkId::new("e2e_training", device_count.to_string()),
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&device_count,
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|b, count| {
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let rt = tokio::runtime::Runtime::new().unwrap();
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b.to_async(&rt).iter_custom(|iters| async move {
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let mut total_duration = Duration::from_secs(0);
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for _ in 0..iters {
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let start = Instant::now();
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// Create coordinator
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let (coordinator, _sender) = FederatedCoordinator::new(
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"bench-coordinator".to_string(),
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SelectionStrategy::Intelligent,
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GradientCompression {
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algorithm: CompressionAlgorithm::TopK,
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compression_ratio: 0.01,
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error_correction: true,
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adaptive_compression: true,
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},
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AggregationStrategy {
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algorithm: AggregationAlgorithm::FedAvg,
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weighting: WeightingScheme::Adaptive,
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byzantine_tolerance: ByzantineTolerance {
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enabled: true,
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max_byzantine_fraction: 0.1,
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detection_algorithm: ByzantineDetection::Krum,
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},
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differential_privacy: Some(DifferentialPrivacy {
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epsilon: 1.0,
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delta: 1e-5,
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noise_mechanism: NoiseMechanism::Gaussian,
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clipping_threshold: 1.0,
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}),
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},
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);
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// Register devices
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for i in 0..*count {
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let device = create_benchmark_device(&format!("device-{:06}", i));
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coordinator.register_device(device).await.unwrap();
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}
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// Select devices
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let selection_criteria = SelectionCriteria {
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min_battery_level: 0.3,
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min_bandwidth_mbps: 5.0,
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max_latency_ms: 200,
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required_availability_minutes: 30,
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min_data_quality: 0.7,
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};
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let target_devices = (*count / 10).max(10) as u32;
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let selection_result = coordinator.select_devices(target_devices, selection_criteria).await.unwrap();
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// Start training round
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let training_config = TrainingConfig {
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local_epochs: 3,
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local_batch_size: 16,
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learning_rate: 0.001,
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gradient_clipping: Some(1.0),
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early_stopping_patience: Some(5),
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};
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let round_id = coordinator.start_training_round(
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selection_result.selected_devices.clone(),
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training_config,
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Duration::from_secs(300),
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).await.unwrap();
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// Simulate gradient aggregation
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let mut device_gradients = HashMap::new();
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for device_id in &selection_result.selected_devices {
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device_gradients.insert(device_id.clone(), vec![0u8; 100]);
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}
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coordinator.aggregate_gradients(round_id, device_gradients).await.unwrap();
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total_duration += start.elapsed();
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}
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total_duration
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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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/// Performance validation benchmark - ensures targets are met
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fn benchmark_performance_validation(c: &mut Criterion) {
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let mut group = c.benchmark_group("Performance_Validation");
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group.bench_function("ARM_NEON_Target_2.5x", |b| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let arm_opts = ArmOptimizations {
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enable_neon: true,
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target_arch: ArmArchitecture::CortexA,
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..Default::default()
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};
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optimizer.configure_arm(arm_opts);
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let metrics = optimizer.optimize_for_target(EdgeTarget::ARM).unwrap();
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assert!(
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metrics.performance_improvement >= 2.5,
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"ARM NEON target not met: {:.2}x < 2.5x",
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metrics.performance_improvement
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);
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black_box(metrics)
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})
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});
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group.bench_function("RISCV_RVV_Target_4.8x", |b| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let riscv_opts = RiscVOptimizations {
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enable_rvv: true,
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vector_length: RiscVVectorLength::VLEN512,
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target_variant: RiscVVariant::Vector,
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..Default::default()
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};
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optimizer.configure_riscv(riscv_opts);
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let metrics = optimizer.optimize_for_target(EdgeTarget::RISCV).unwrap();
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assert!(
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metrics.performance_improvement >= 4.8,
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"RISC-V RVV target not met: {:.2}x < 4.8x",
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metrics.performance_improvement
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);
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black_box(metrics)
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})
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});
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group.bench_function("WASM_SIMD_Target_1.8x", |b| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let wasm_opts = WasmOptimizations {
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enable_simd: true,
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enable_threads: true,
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..Default::default()
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};
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optimizer.configure_wasm(wasm_opts);
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let metrics = optimizer.optimize_for_target(EdgeTarget::WASM).unwrap();
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assert!(
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metrics.performance_improvement >= 1.8,
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"WASM SIMD target not met: {:.2}x < 1.8x",
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metrics.performance_improvement
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);
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black_box(metrics)
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})
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});
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group.bench_function("Mobile_GPU_Target_4.0x", |b| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let mobile_gpu_opts = MobileGpuOptimizations {
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gpu_vendor: MobileGpuVendor::Apple,
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compute_shaders: true,
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tile_based_rendering: true,
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bandwidth_optimization: true,
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power_efficiency: true,
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};
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optimizer.configure_mobile_gpu(mobile_gpu_opts);
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let metrics = optimizer.optimize_for_target(EdgeTarget::MobileGPU).unwrap();
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assert!(
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metrics.performance_improvement >= 4.0,
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"Mobile GPU target not met: {:.2}x < 4.0x",
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metrics.performance_improvement
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);
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black_box(metrics)
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})
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});
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group.bench_function("IoT_Power_Target_10x", |b| {
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b.iter(|| {
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let mut optimizer = EdgeTargetOptimizer::new();
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let iot_opts = IoTOptimizations {
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ultra_low_power: true,
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minimal_memory: true,
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wake_on_inference: true,
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target_platform: IoTPlatform::ESP32,
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..Default::default()
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};
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optimizer.configure_iot(iot_opts);
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let metrics = optimizer.optimize_for_target(EdgeTarget::IoT).unwrap();
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|
assert!(
|
|
metrics.power_efficiency_gain >= 10.0,
|
|
"IoT power efficiency target not met: {:.2}x < 10.0x",
|
|
metrics.power_efficiency_gain
|
|
);
|
|
|
|
black_box(metrics)
|
|
})
|
|
});
|
|
|
|
group.finish();
|
|
}
|
|
|
|
// Helper function to create benchmark test devices
|
|
fn create_benchmark_device(device_id: &str) -> FederatedDevice {
|
|
use std::collections::HashSet;
|
|
|
|
FederatedDevice {
|
|
device_id: device_id.to_string(),
|
|
device_type: EdgeDeviceType::StandardMobile,
|
|
status: DeviceStatus::Available,
|
|
network_info: NetworkInfo {
|
|
connection_type: ConnectionType::WiFi,
|
|
bandwidth_mbps: 50.0,
|
|
latency_ms: 20,
|
|
reliability: 0.95,
|
|
data_plan: DataPlan {
|
|
unlimited: true,
|
|
monthly_allowance_gb: None,
|
|
current_usage_gb: 0.0,
|
|
cost_per_gb: None,
|
|
},
|
|
},
|
|
power_status: PowerStatus {
|
|
battery_level: 0.8,
|
|
is_charging: false,
|
|
power_source: PowerSource::Battery,
|
|
estimated_battery_life_minutes: Some(240),
|
|
},
|
|
compute_capabilities: ComputeCapabilities {
|
|
cpu_cores: 4,
|
|
ram_mb: 4096,
|
|
has_gpu: false,
|
|
simd_support: true,
|
|
estimated_flops: 1e9,
|
|
memory_bandwidth_gbps: 10.0,
|
|
},
|
|
data_info: DataInfo {
|
|
sample_count: 1000,
|
|
quality_score: 0.9,
|
|
privacy_level: PrivacyLevel::Personal,
|
|
distribution: DataDistribution {
|
|
distribution_type: "normal".to_string(),
|
|
parameters: HashMap::new(),
|
|
},
|
|
},
|
|
availability: AvailabilitySchedule {
|
|
timezone_offset_hours: 0,
|
|
available_hours: (0..24).collect(),
|
|
preferred_duration_minutes: 30,
|
|
blackout_periods: vec![],
|
|
},
|
|
performance_metrics: PerformanceMetrics {
|
|
avg_training_time_seconds: 300.0,
|
|
avg_upload_time_seconds: 10.0,
|
|
accuracy_contribution: 0.85,
|
|
reliability_score: 0.9,
|
|
communication_efficiency: 0.8,
|
|
},
|
|
last_seen: SystemTime::now(),
|
|
}
|
|
}
|
|
|
|
criterion_group!(
|
|
edge_benchmarks,
|
|
benchmark_arm_neon_optimization,
|
|
benchmark_riscv_vector_optimization,
|
|
benchmark_wasm_simd_optimization,
|
|
benchmark_mobile_gpu_optimization,
|
|
benchmark_iot_optimization,
|
|
benchmark_federated_device_selection,
|
|
benchmark_gradient_aggregation,
|
|
benchmark_training_round_e2e,
|
|
benchmark_performance_validation
|
|
);
|
|
|
|
criterion_main!(edge_benchmarks);
|