When C++ Optimization Slows Down Your Go Code
When you have years of C++ experience, you definitely obtain some habits. These habits are good for C++, but could cause you some surprises in Go. In C++, you usually preallocate everything, avoiding unnecessary allocations, caching values aggressively, and always thinking of CPU cache misses. So when I rewrote a simple algorithm in Go—finding the number of days until the next warmer temperature—I reached for the same tricks. But this time, they backfired.
Here’s how applying familiar C++ optimizations ended up making my Go code slower and heavier.
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When C++ Optimization Slows Down Your Go Code
When you have years of C++ experience, you definitely obtain some habits. These habits are good for C++, but could cause you some surprises in Go. In C++, you usually preallocate everything, avoiding unnecessary allocations, caching values aggressively, and always thinking of CPU cache misses. So when I rewrote a simple algorithm in Go—finding the number of days until the next warmer temperature—I reached for the same tricks. But this time, they backfired.
Here’s how applying familiar C++ optimizations ended up making my Go code slower and heavier.
Lazy initialization in Go using atomics
Aside from the main performance guide, I’m considering using the blog to share quick, informal insights and quirks related to Go performance and optimizations. Let’s see if this casual experiment survives contact with reality.
Someone recently pointed out that my getResource() function using atomics has a race condition. Guilty as charged—rookie mistake, really. The issue? I naïvely set the initialized flag to true before the actual resource is ready. Brilliant move, right? This means that with concurrent calls, one goroutine might proudly claim victory while handing out a half-baked resource:
var initialized atomic.Bool
var resource *MyResource
func getResource() *MyResource {
if !initialized.Load() {
if initialized.CompareAndSwap(false, true) {
resource = expensiveInit()
}
}
return resource
}