How I Boosted Flutter Performance 3× by Rewriting a Dart Function in C++ Using FFI
If you're a Flutter developer struggling with slow loops, janky flows, or CPU-heavy parts, this is exactly the kind of real-world optimization that actually moves the needle.
I didn't plan to touch C++ in this project.
I was building a smooth fintech flow in Flutter — nice UI, fast screens, clean state management. Everything was perfect until one stupid function ruined the entire experience.
Every time the user hit Continue, the UI froze for around 250–300ms. Just long enough for users to feel the pause… and long enough for me to hate it.
I tried optimizations in Dart. I tried isolates. I even questioned my own sanity and rechecked the profiler results.
The culprit was always the same:
A CPU-heavy loop running thousands of iterations on the main thread.
I knew Dart wasn't slow — I was just forcing it into something it wasn't designed for.
So I did something I'd never done in Flutter before:
I rewrote the hot path in C++ and called it through FFI.
And the result?
~260ms → ~85ms. A 3× improvement, with roughly 67% time saved.
The funny part? I've been grinding DSA in C++ for FAANG prep — and that low-level thinking turned out to be my biggest real-world superpower here.
Here's exactly how I did it.
The Problem (Real-World Scenario)
Inside a payment confirmation flow, we had a step that:
- Iterated through 100k+ integers
- Performed multiple bitwise + arithmetic ops
- Updated an accumulator
- Repeated this several times per user action
Dart handled smaller datasets fine. But production loads revealed the truth:
This was pure CPU work — and the UI thread was choking.
I didn't want workarounds. I wanted a permanent fix.
The Dart Version (Baseline)
int processDart(List<int> data) {
var acc = 0;
for (var x in data) {
var y = ((x * 1664525) + 1013904223) & 0xffffffff;
y ^= (y >> 16);
y = (y * 1103515245) & 0xffffffff;
acc = (acc + y) & 0xffffffffffffffff;
}
return acc;
}Clean. Legible. Painfully slow at scale.
Before vs After (median across 10 runs):
Dart ██████████████████████████████ ~260ms
C++ + FFI ████████ ~85msHere's the part that convinced me I was bottlenecking the flow.
🔥 The C++ Rewrite (Where The Magic Happened)
Here's the exact native loop I wrote — nothing fancy, but extremely fast.
extern "C" {
uint64_t process_cpp(const int32_t* data, size_t len) {
uint64_t acc = 0;
const int32_t* end = data + len;
const int32_t* p = data;
// Light manual unrolling
while (p + 4 <= end) {
uint32_t x0 = *p++;
uint32_t y0 = ((uint64_t)x0 * 1664525u + 1013904223u) & 0xffffffffu;
y0 ^= (y0 >> 16);
y0 = (y0 * 1103515245u) & 0xffffffffu;
acc += y0;
uint32_t x1 = *p++;
uint32_t y1 = ((uint64_t)x1 * 1664525u + 1013904223u) & 0xffffffffu;
y1 ^= (y1 >> 16);
y1 = (y1 * 1103515245u) & 0xffffffffu;
acc += y1;
uint32_t x2 = *p++;
uint32_t y2 = ((uint64_t)x2 * 1664525u + 1013904223u) & 0xffffffffu;
y2 ^= (y2 >> 16);
y2 = (y2 * 1103515245u) & 0xffffffffu;
acc += y2;
uint32_t x3 = *p++;
uint32_t y3 = ((uint64_t)x3 * 1664525u + 1013904223u) & 0xffffffffu;
y3 ^= (y3 >> 16);
y3 = (y3 * 1103515245u) & 0xffffffffu;
acc += y3;
}
// Remaining elements
while (p < end) {
uint32_t x = *p++;
uint32_t y = ((uint64_t)x * 1664525u + 1013904223u) & 0xffffffffu;
y ^= (y >> 16);
y = (y * 1103515245u) & 0xffffffffu;
acc += y;
}
return acc;
}
}It's not pretty. It's not modern C++17. But it's fast.
The FFI Bridge
typedef _ProcessCppNative =
ffi.Uint64 Function(ffi.Pointer<ffi.Int32>, ffi.IntPtr);
typedef _ProcessCppDart =
int Function(ffi.Pointer<ffi.Int32>, int);
class HotPathFFI {
late final _ProcessCppDart _process;
HotPathFFI() {
final lib = Platform.isAndroid
? ffi.DynamicLibrary.open("libhotpath.so")
: ffi.DynamicLibrary.process();
_process = lib.lookupFunction<_ProcessCppNative, _ProcessCppDart>(
"process_cpp",
);
}
int process(List<int> data) {
final ptr = ffi.malloc<ffi.Int32>(data.length);
ptr.asTypedList(data.length).setAll(0, data);
final result = _process(ptr, data.length);
ffi.malloc.free(ptr);
return result;
}
}Simple. Reusable. Clean.
Why C++ Made It So Much Faster
- Raw pointer arithmetic
- Zero Dart object creation
- No GC pauses
- Fewer bounds checks
- Auto-vectorization at
-O3 - Predictable machine code
- A CPU-friendly hot loop
Flutter + Dart does many things brilliantly. But tight, CPU-bound loops belong in native land.
⚠️ Hidden Costs of Using FFI (Important)
FFI isn't something you sprinkle everywhere.
It adds:
- Native build complexity
- ABI management (arm64, iOS, simulator, etc.)
- Harder debugging
- CI/CD native setup
- Potential for memory unsafety
- Overhead on frequent small calls
- Extra maintenance
Use it only when you have:
✔ a measured bottleneck ✔ a CPU-heavy workload ✔ minimal cross-language calls ✔ a stable algorithm ✔ no UI dependencies
Otherwise, Dart is more than enough.
When This Pattern Shines
- Large numeric datasets
- Hashing / transforms
- Compression
- Bitwise pipelines
- ML-lite local ops
- Gaming loops
- Encryption-like operations
- Real-time fintech flows
This technique shines in fintech, crypto, gaming, ML inference, and real-time pipelines.
🔥 Final Thoughts (Where I Think This Skill Actually Shines)
This was the first time I realized my interview prep actually helped me ship a smoother product. I used to think my C++ DSA grind was "just for interviews".
Turns out it helped me fix a real production issue that directly improved user experience.
That's the peace I never expected: Knowing low-level fundamentals amplifies your high-level tools.
If you're a Flutter developer who knows some C++… you're sitting on an underrated superpower.
Try this once — and you'll see exactly what I mean.
Note: The benchmarks here are based on representative test data, not production logs.
More on how FFI fits into modern Flutter performance: 🔗R Flutter's Biggest Upgrade in 10 Years — FFI Became a Superpower
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If you enjoy deep Flutter + performance content, I share more experiments like this.