Optimize memory usage

Limit memory consumption in performance-critical paths by conditionally creating large data structures or deferring allocation until needed. Large tracking structures, debug information, and memory-intensive operations should be guarded by configuration flags to prevent them from impacting performance in production environments.

For example, when implementing debug features, wrap memory-intensive operations in conditional blocks:

// Before: Always creates large data structure
for (const auto& [v, lifetime] : lifetimes_) {
  for (const auto t : c10::irange(lifetime.start, lifetime.end + 1)) {
    alive_values_at_time_[t].emplace_back(v);
  }
}

// After: Only create when debug mode is enabled
#ifdef DEBUG_MODE
for (const auto& [v, lifetime] : lifetimes_) {
  for (const auto t : c10::irange(lifetime.start, lifetime.end + 1)) {
    alive_values_at_time_[t].emplace_back(v);
  }
}
#endif

Additionally, use appropriate resource management patterns (RAII, smart pointers) to ensure efficient memory utilization and prevent leaks in performance-sensitive code. When working with external APIs like the Python C API, maintain correct reference counting to avoid memory leaks that can gradually degrade performance over time.

In memory-constrained environments like GPU computing, these optimizations can prevent out-of-memory errors and significantly improve overall system performance.