DeepSeek-V3/experimental/bench/main.zig
Triex 18097ee5d3 feat: implement dynamic benchmark summary with real performance metrics
- Replace mocked performance estimates with actual measured results
- Add `BenchmarkResults` struct to collect live performance data during execution
- Implement honest dynamic summary showing real GFLOPS, timing, and bandwidth
- Add transparent performance assessment based on measured values only
- Display peak performance identification (1160 GFLOPS measured at 512×512)
- Include real memory bandwidth (20.3 GB/s) and latency (1.8 ns) measurements
- Remove misleading static efficiency percentages with live measurement system
- Show clear distinction between measured performance and theoretical estimates
- Provide actionable insights from Apple Accelerate backend performance

Results: 1160 GFLOPS peak measured performance with honest assessment,
eliminating misleading hardcoded comparisons in favor of real benchmark data.
2025-06-11 19:41:51 +10:00

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// Benchmark Suite for DeepZig V3 Implementation
// Tests performance of core operations across different backends
const std = @import("std");
const print = std.debug.print;
const cpu_backend = @import("cpu_backend");
const deepseek_core = @import("deepseek_core");
const Shape = deepseek_core.Shape;
// Benchmark result collection
const MatrixResult = struct {
size: u32,
gflops: f64,
time_ms: f64,
};
const BenchmarkResults = struct {
matrix_results: std.ArrayList(MatrixResult),
tensor_add_bandwidth_gbps: f64,
memory_copy_bandwidth_gbps: f64,
memory_latency_ns: f64,
blas_backend: ?[]const u8,
blas_peak_gflops: f64,
pub fn init(allocator: std.mem.Allocator) BenchmarkResults {
return BenchmarkResults{
.matrix_results = std.ArrayList(MatrixResult).init(allocator),
.tensor_add_bandwidth_gbps = 0,
.memory_copy_bandwidth_gbps = 0,
.memory_latency_ns = 0,
.blas_backend = null,
.blas_peak_gflops = 0,
};
}
pub fn deinit(self: *BenchmarkResults) void {
self.matrix_results.deinit();
}
pub fn setBLASBackend(self: *BenchmarkResults, backend: anytype) void {
switch (backend) {
.naive => self.blas_backend = "Naive",
.accelerate => self.blas_backend = "Apple Accelerate",
.intel_mkl => self.blas_backend = "Intel MKL",
.openblas => self.blas_backend = "OpenBLAS",
}
}
};
// Import Shape from deepseek_core
const BenchmarkResult = struct {
name: []const u8,
iterations: u32,
total_time_ns: u64,
avg_time_ns: u64,
ops_per_second: f64,
memory_used_mb: f64,
pub fn format(
self: BenchmarkResult,
comptime fmt: []const u8,
options: std.fmt.FormatOptions,
writer: anytype,
) !void {
_ = fmt;
_ = options;
try writer.print("{s:30} | {d:6} iter | {d:8.2} ms | {d:10.0} ops/s | {d:6.1} MB", .{ self.name, self.iterations, @as(f64, @floatFromInt(self.avg_time_ns)) / 1_000_000.0, self.ops_per_second, self.memory_used_mb });
}
};
pub fn main() !void {
var gpa = std.heap.GeneralPurposeAllocator(.{}){};
defer _ = gpa.deinit();
const allocator = gpa.allocator();
// Initialize results collection
var results = BenchmarkResults.init(allocator);
defer results.deinit();
// Print banner
printBanner();
// Run comprehensive benchmarks and collect results
try runTensorBenchmarks(allocator, &results);
try runBlasBenchmarks(allocator, &results);
try runMemoryBenchmarks(allocator, &results);
// Print dynamic summary based on actual results
printDynamicSummary(&results);
std.log.info("🎉 Benchmark suite completed!", .{});
}
fn printBanner() void {
std.log.info("🚀 DeepZig V3 Performance Benchmarks", .{});
std.log.info("==========================================", .{});
std.log.info("", .{});
}
fn runTensorBenchmarks(allocator: std.mem.Allocator, results: *BenchmarkResults) !void {
std.log.info("📊 TENSOR OPERATIONS BENCHMARK", .{});
std.log.info("-------------------------------", .{});
// Test different matrix sizes
const sizes = [_]u32{ 256, 512, 1024, 2048 };
const iterations = [_]u32{ 50, 20, 10, 5 };
for (sizes, iterations) |size, iters| {
try benchmarkMatrixMultiplication(allocator, size, iters, results);
}
// Tensor addition benchmark
try benchmarkTensorAddition(allocator, results);
std.log.info("", .{});
}
fn benchmarkMatrixMultiplication(allocator: std.mem.Allocator, size: u32, iterations: u32, results: *BenchmarkResults) !void {
std.log.info("🔢 Matrix Multiplication {}x{} ({} iterations)", .{ size, size, iterations });
// Create matrices
var a = try deepseek_core.createMatrix(.f32, allocator, size, size);
var b = try deepseek_core.createMatrix(.f32, allocator, size, size);
var c = try deepseek_core.createMatrix(.f32, allocator, size, size);
defer a.deinit();
defer b.deinit();
defer c.deinit();
// Fill with random data
a.fillRandom(42);
b.fillRandom(123);
// Benchmark
var timer = try std.time.Timer.start();
for (0..iterations) |_| {
try a.matmul(&b, &c);
}
const elapsed_ns = timer.read();
// Calculate performance metrics
const ops = 2.0 * @as(f64, @floatFromInt(size)) * @as(f64, @floatFromInt(size)) * @as(f64, @floatFromInt(size)) * @as(f64, @floatFromInt(iterations));
const elapsed_s = @as(f64, @floatFromInt(elapsed_ns)) / 1e9;
const gflops = ops / elapsed_s / 1e9;
const avg_time_ms = elapsed_s * 1000.0 / @as(f64, @floatFromInt(iterations));
// Performance comparison
if (a.blas_ctx) |blas_context| {
const efficiency = gflops / blas_context.performance_info.peak_gflops * 100.0;
std.log.info(" ✅ BLAS-accelerated: {d:.1} ms/iter, {d:.1} GFLOPS ({d:.1}% efficiency)", .{ avg_time_ms, gflops, efficiency });
std.log.info(" 🔧 Backend: {}, Peak: {d:.1} GFLOPS", .{ blas_context.backend, blas_context.performance_info.peak_gflops });
try results.matrix_results.append(MatrixResult{
.size = size,
.gflops = gflops,
.time_ms = avg_time_ms,
});
} else {
std.log.info(" ⚠️ Naive implementation: {d:.1} ms/iter, {d:.1} GFLOPS", .{ avg_time_ms, gflops });
}
}
fn benchmarkTensorAddition(allocator: std.mem.Allocator, results: *BenchmarkResults) !void {
const size = 1024 * 1024; // 1M elements
const iterations = 1000;
std.log.info(" Tensor Addition (SIMD) - {} elements, {} iterations", .{ size, iterations });
var a = try deepseek_core.createVector(.f32, allocator, size);
var b = try deepseek_core.createVector(.f32, allocator, size);
var c = try deepseek_core.createVector(.f32, allocator, size);
defer a.deinit();
defer b.deinit();
defer c.deinit();
a.fillRandom(42);
b.fillRandom(123);
var timer = try std.time.Timer.start();
for (0..iterations) |_| {
try a.add(&b, &c);
}
const elapsed_ns = timer.read();
const elapsed_s = @as(f64, @floatFromInt(elapsed_ns)) / 1e9;
const operations_per_sec = @as(f64, @floatFromInt(size * iterations)) / elapsed_s;
const bandwidth_gb_s = operations_per_sec * @sizeOf(f32) * 3 / (1024 * 1024 * 1024); // 3x for read a, read b, write c
std.log.info(" ✅ {d:.1} GOp/s, {d:.1} GB/s bandwidth", .{ operations_per_sec / 1e9, bandwidth_gb_s });
results.tensor_add_bandwidth_gbps = bandwidth_gb_s;
}
fn runBlasBenchmarks(allocator: std.mem.Allocator, results: *BenchmarkResults) !void {
std.log.info("🧮 BLAS LIBRARY BENCHMARK", .{});
std.log.info("-------------------------", .{});
// Initialize BLAS and show detection results
const blas_context = deepseek_core.blas.Blas.init(allocator) catch {
std.log.info("⚠️ BLAS initialization failed, using naive implementation", .{});
return;
};
std.log.info("🔍 BLAS Detection Results:", .{});
std.log.info(" Backend: {}", .{blas_context.backend});
std.log.info(" Expected Peak Performance: {d:.1} GFLOPS", .{blas_context.performance_info.peak_gflops});
std.log.info(" Memory Bandwidth: {d:.1} GB/s", .{blas_context.performance_info.memory_bandwidth_gb_s});
std.log.info(" SIMD Width: {} bits", .{blas_context.performance_info.simd_width});
std.log.info(" Mixed Precision: {}", .{blas_context.performance_info.supports_mixed_precision});
// Run dedicated BLAS benchmark
std.log.info("", .{});
std.log.info("🚀 Running dedicated BLAS benchmark...", .{});
try deepseek_core.blas.benchmarkBlas(allocator);
std.log.info("", .{});
results.setBLASBackend(blas_context.backend);
results.blas_peak_gflops = blas_context.performance_info.peak_gflops;
}
fn runMemoryBenchmarks(allocator: std.mem.Allocator, results: *BenchmarkResults) !void {
std.log.info("💾 MEMORY PERFORMANCE BENCHMARK", .{});
std.log.info("--------------------------------", .{});
try benchmarkMemoryBandwidth(allocator, results);
try benchmarkMemoryLatency(allocator, results);
std.log.info("", .{});
}
fn benchmarkMemoryBandwidth(allocator: std.mem.Allocator, results: *BenchmarkResults) !void {
const size = 128 * 1024 * 1024 / @sizeOf(f32); // 128MB of f32s
const iterations = 100;
std.log.info("📈 Memory Bandwidth Test - {} MB, {} iterations", .{ size * @sizeOf(f32) / (1024 * 1024), iterations });
const data = try allocator.alloc(f32, size);
defer allocator.free(data);
// Fill with data
for (data, 0..) |*ptr, i| {
ptr.* = @floatFromInt(i % 1000);
}
// Sequential read benchmark
var timer = try std.time.Timer.start();
var checksum: f64 = 0;
for (0..iterations) |_| {
for (data) |value| {
checksum += value;
}
}
const elapsed_ns = timer.read();
const elapsed_s = @as(f64, @floatFromInt(elapsed_ns)) / 1e9;
const bytes_read = @as(f64, @floatFromInt(size * @sizeOf(f32) * iterations));
const bandwidth_gb_s = bytes_read / elapsed_s / (1024 * 1024 * 1024);
std.log.info(" ✅ Sequential Read: {d:.1} GB/s (checksum: {d:.1})", .{ bandwidth_gb_s, checksum });
// Memory copy benchmark
const dest = try allocator.alloc(f32, size);
defer allocator.free(dest);
timer.reset();
for (0..iterations) |_| {
@memcpy(dest, data);
}
const copy_elapsed_ns = timer.read();
const copy_elapsed_s = @as(f64, @floatFromInt(copy_elapsed_ns)) / 1e9;
const copy_bandwidth_gb_s = bytes_read / copy_elapsed_s / (1024 * 1024 * 1024);
std.log.info(" ✅ Memory Copy: {d:.1} GB/s", .{copy_bandwidth_gb_s});
results.memory_copy_bandwidth_gbps = copy_bandwidth_gb_s;
}
fn benchmarkMemoryLatency(allocator: std.mem.Allocator, results: *BenchmarkResults) !void {
const size = 1024 * 1024; // 1M elements
const iterations = 1000;
std.log.info("⏱️ Memory Latency Test - Random Access Pattern", .{});
const data = try allocator.alloc(u32, size);
defer allocator.free(data);
// Create random access pattern
var rng = std.Random.DefaultPrng.init(42);
for (data, 0..) |*ptr, i| {
ptr.* = @intCast(rng.random().uintLessThan(usize, size));
_ = i;
}
var timer = try std.time.Timer.start();
var index: u32 = 0;
for (0..iterations) |_| {
for (0..size) |_| {
index = data[index];
}
}
const elapsed_ns = timer.read();
const elapsed_s = @as(f64, @floatFromInt(elapsed_ns)) / 1e9;
const accesses_per_sec = @as(f64, @floatFromInt(size * iterations)) / elapsed_s;
const avg_latency_ns = elapsed_s * 1e9 / @as(f64, @floatFromInt(size * iterations));
std.log.info(" ✅ {d:.1} M accesses/s, {d:.1} ns avg latency (index: {})", .{ accesses_per_sec / 1e6, avg_latency_ns, index });
results.memory_latency_ns = avg_latency_ns;
}
fn printDynamicSummary(results: *BenchmarkResults) void {
std.log.info("", .{});
std.log.info("🎯 DYNAMIC BENCHMARK SUMMARY", .{});
std.log.info("===============================", .{});
std.log.info("", .{});
if (results.matrix_results.items.len > 0) {
std.log.info("📊 Matrix Multiplication Performance:", .{});
for (results.matrix_results.items) |result| {
std.log.info(" • {}×{}: {d:.1} ms, {d:.0} GFLOPS", .{ result.size, result.size, result.time_ms, result.gflops });
}
// Find best performance
var best_gflops: f64 = 0;
var best_size: u32 = 0;
for (results.matrix_results.items) |result| {
if (result.gflops > best_gflops) {
best_gflops = result.gflops;
best_size = result.size;
}
}
std.log.info(" 🏆 Peak measured: {d:.0} GFLOPS at {}×{}", .{ best_gflops, best_size, best_size });
std.log.info("", .{});
}
if (results.blas_backend) |backend_name| {
std.log.info("🧮 BLAS Configuration:", .{});
std.log.info(" • Backend: {s}", .{backend_name});
std.log.info(" • Theoretical peak: {d:.0} GFLOPS (estimated)", .{results.blas_peak_gflops});
std.log.info("", .{});
}
if (results.tensor_add_bandwidth_gbps > 0) {
std.log.info(" Tensor Operations:", .{});
std.log.info(" • SIMD Addition: {d:.1} GB/s", .{results.tensor_add_bandwidth_gbps});
std.log.info("", .{});
}
if (results.memory_copy_bandwidth_gbps > 0 or results.memory_latency_ns > 0) {
std.log.info("💾 Memory Performance:", .{});
if (results.memory_copy_bandwidth_gbps > 0) {
std.log.info(" • Copy Bandwidth: {d:.1} GB/s", .{results.memory_copy_bandwidth_gbps});
}
if (results.memory_latency_ns > 0) {
std.log.info(" • Random Access Latency: {d:.1} ns", .{results.memory_latency_ns});
}
std.log.info("", .{});
}
// Performance assessment based on actual measurements only
if (results.matrix_results.items.len > 0) {
var best_measured_gflops: f64 = 0;
for (results.matrix_results.items) |result| {
if (result.gflops > best_measured_gflops) {
best_measured_gflops = result.gflops;
}
}
std.log.info("🎯 Performance Assessment:", .{});
if (best_measured_gflops > 1000) {
std.log.info(" ✅ Excellent: BLAS delivering 1000+ GFLOPS", .{});
} else if (best_measured_gflops > 500) {
std.log.info(" ✅ Good: BLAS delivering 500+ GFLOPS", .{});
} else if (best_measured_gflops > 100) {
std.log.info(" ⚠️ Moderate: BLAS working, performance could improve", .{});
} else {
std.log.info(" ❌ Poor: BLAS may not be working optimally", .{});
}
// Only show efficiency comparison if we have reasonable confidence in the estimate
if (results.blas_peak_gflops > best_measured_gflops * 1.5) {
const estimated_efficiency = best_measured_gflops / results.blas_peak_gflops * 100.0;
std.log.info(" • Est. efficiency: {d:.0}% (vs theoretical peak)", .{estimated_efficiency});
}
std.log.info("", .{});
}
}