Performance Engineering

A comprehensive exploration of high-performance computing across different compute paradigms and deployment platforms. From CPU optimization to GPU computing, demonstrating the real-world impact of hardware-aware programming.

Performance Decision Framework

CPU Optimization

✓ Sequential algorithms with dependencies
✓ Small to medium datasets where transfer overhead matters
✓ Complex branching and control flow
✓ Irregular memory access patterns

GPU Computing

✓ Embarrassingly parallel problems
✓ Large datasets that amortize transfer overhead
✓ Regular memory access patterns
✓ Simple operations with minimal branching

Language Performance Comparison

Comparing C++, Rust, and Zig implementations across different deployment platforms. Each platform has unique characteristics that influence performance and suitability.

Native Performance

Comparing C++, Rust, and Zig native implementations on the same hardware.

Performance Comparison

Implementation Details

Language	Description	Time (ns/call)	Memory (MB)	Throughput (ops/sec)	Relative Speed
C++WINNER	Advanced scalar (FMA + SIMD sqrt)	25.6	2.1	39.1k	1.0x (fastest)
Rust	Optimized scalar (FMA only)	118.7	1.8	8.4k	0.22x
Zig	Advanced scalar (standard arithmetic)	40.2	1.9	24.9k	0.64x

Key Insights

Performance Analysis

• C++ leads with 25.6 ns/call (FMA + SIMD sqrt)
• Zig follows at 40.2 ns/call (standard arithmetic)
• Rust shows 118.7 ns/call (FMA only)
• All use similar polynomial approximations

Note: This comparison uses similar algorithmic complexity but different optimization effort has been invested in each language. C++ and Zig implementations have received more optimization work than Rust.

Memory & Throughput

• Rust uses least memory (1.8 MB)
• C++ achieves highest throughput (39.1k ops/sec)
• Memory safety doesn't significantly impact performance
• All languages show excellent optimization potential

WebAssembly Performance

Comparing C++, Rust, and Zig WebAssembly implementations running in the browser.

WebAssembly Benchmarks

Run live WebAssembly benchmarks to compare C++, Rust, and Zig performance in the browser.

Server Performance

Comparing C++, Rust, and Zig server implementations with HTTP overhead.

Server Benchmarks

Run live server benchmarks to compare C++, Rust, and Zig performance with HTTP overhead.

Zig Optimization Journey

A detailed exploration of optimization techniques in Zig, from naive implementations to hand-optimized SIMD kernels and multithreading. Demonstrates the incremental approach to performance engineering.

Zig Optimization Journey

From naive implementation to hand-optimized SIMD kernels and multithreading. Demonstrates incremental performance engineering approach.

Performance Timeline

Speedup vs Naive Implementation

Implementation Details

Version	Name	Description	Category	Time (ns/call)	Speedup
V1	Naive	Basic haversine formula with standard math functions	Algorithmic	41.7	1.0x
V2	Advanced Scalar	Optimized polynomial approximations for sin/cos	Algorithmic	38.6	1.1x
V3	SIMD Optimized	Vectorized operations using AVX2 instructions	Algorithmic	47.8	0.9x
V4	Ultra Optimized	Loop unrolling and constant folding optimizations	Algorithmic	38.7	1.1x
V5	Ultra Fast	Reduced polynomial degree with minimal error	Algorithmic	36.4	1.1x
V6	Lookup Table	Precomputed sine/cosine values for common angles	Algorithmic	17.9	2.3x
V7	Approximation	Fast approximation using simplified math	Algorithmic	11.4	3.7x
V8	Lookup V11	Lookup table with precomputed values	Algorithmic	9.4	4.4x
V9	Ultra Aggressive	Combined lookup tables with aggressive inlining	Algorithmic	9.4	4.4x
V10	Multithreaded	Parallel processing across multiple CPU cores	Parallelization	5.3	7.9x

Key Insights from the Optimization Journey

Algorithmic Optimizations

• V1→V2: Scalar optimizations (1.1x speedup)
• V3→V5: SIMD and loop optimizations (1.2x speedup)
• V6→V9: Lookup tables and approximations (4.7x speedup)
• Total algorithmic: 4.7x speedup

Parallelization Impact

• V9→V10: Algorithmic + parallel (8.4x total speedup)
• Combined approach: 8.4x total speedup
• Best practice: Optimize algorithm first, then parallelize

Performance Methodology

1. Profile First

Measure before optimizing. Identify bottlenecks and understand the performance characteristics of your workload.

2. Optimize Incrementally

Start with algorithmic improvements, then move to hardware-specific optimizations. Measure at each step.

3. Choose the Right Tool

Match the compute paradigm to the problem characteristics. CPU for complex logic, GPU for parallel workloads.